Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Submitted to May 2018 CSIR-Central Road Research Institute (CRRI), New Delhi, INDIA In association with TNO, The Netherlands Delft University of Technology, The Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Submitted to The World Bank Group May 2018 CSIR-Central Road Research Institute (CRRI), New Delhi, INDIA In association with TNO, The Netherlands Delft University of Technology, The Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report FOREWORD India spends 15-20% of its GDP on transport and logistics and Indian freight transport market is expected to grow at a Compound Annual Growth Rate (CAGR) of about 13% by 2020. Road freight constitutes around 63% of the total freight movement and the average speed of trucks on Indian roads is about 20 km/hr covers only 250-300 km a day compared to 700-800 km in developed countries. Moreover, on an average, total trip expenses increases about 15% due to the delays at check-posts and on-road for filling in forms required by various government departments, checking of documents and physical checking of the vehicles, drivers and consignments by Regional Transport Offices and traffic police, and collecting highway toll and taxes. The working conditions for the truck drivers also deteriorating and they work for long hours, resulting in high stress and fatigue, which leads to accidents. The need is recognized for collaboration amongst stakeholders to identify optimal freight policies and pursue a rapid deployment of improvements. Creating better data and models is needed to enable planners to better predict freight movement and design better informed policies. Considering the above, the World Bank Group has funded research project on "Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG)" which is to be carried out by this institute in association with TNO, Netherlands and TU Delft, Netherlands. The present study mainly focused on development of city logistics metrics, capacity development for Sustainable City Logistics (SCL), development of freight transport demand model and logistics flow model for the city of New Delhi and knowledge sharing among stakeholders. By organising the meetings, short course and workshops as part of the present study, the need for city logistics, current limitations and problems for sustainable logistics have been discussed with the potential stakeholders and accordingly devised and the way forward to achieve sustainable city logistics. As the transport logistics and administrative setups of different cities more or less matches with present study area i.e. Delhi, the methodology adopted can be replicated to achieve sustainable city logistics for the other cities as well. It is hoped that the study findings would be helpful to all transport related actors in city logistics, including infrastructure management, transport sector and the government. Date: 30th May 2018 (Prof. Satish Chandra) Place: New Delhi Director, CSIR-CRRI The World Bank Group Page | i CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report The World Bank Group Page | ii CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report STUDY TEAM Director Prof. Satish Chandra Project Leader Dr. Errampalli Madhu, Principal Scientist and Head (TP Division) CSIR-CRRI, New Delhi Dr. Errampalli Madhu, Principal Scientist, HoD (TP Division) Dr. Kayitha Ravinder, Principal Scientist Dr. Ch. Ravi Sekhar, Principal Scientist Ms. Minal, Scientist TU-Delft, The Netherlands TNO, The Netherlands Prof. Lorant A. Tavasszy Mr. Jeroen Borst Dr. Jaap M. Vleugel Mr. Spencer W. Milburn Ms. Jolijn van Dijk Dr. Hans J. Quak External Advisers Prof. Russell G. Thompson, University of Melbourne, Australia Dr. Nilesh Anand, (on behalf of TU Delft, The Netherlands) Traffic Surveys and Assistance in Workshops Sh. S. Kannan, Senior Technical Officer Sh. Jagdish S. Jangpangi, Senior Technician Sh. Satish Kumar, Technical Officer Sh. Rajan Varma, Technical Assistant Sh. Sanjay Kumar, Senior Technician Sh. Amit Kumar Dubey, Project Assistant Sh. Vikas Kumar Thakur, Project Assistant Assistance in Project Execution Sh. D. Ravinder, Technical Officer Sh. P. V. Pradeep Kumar, Senior Principal Scientist Secretarial Assistance Ms. Krishna Verma The World Bank Group Page | iii CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report The World Bank Group Page | iv CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report EXECUTIVE SUMMARY Understanding and forecasting freight movements is critical to plan for future transportation in terms of capacity augmentation, operation, preservation, safety and security, energy and economy investment needs. Many demand forecasting models and data sources are more appropriate for passenger transportation than for freight transportation in terms of understanding freight travel behaviour. Creating better data and models is needed to enable planners to better predict freight movement and design better informed policies. In view of this, the present study have been conceptualised on Megacity Logistics: Metrics, Tools and Measures Creating better data and models is E XECUTIVE S UMMARY for Sustainability (MEGALOG) and submitted a proposal on the same by CSIR-CRRI, New Delhi, needed to enable planners to better TNO, The Netherlands and TU-Delft, The predict freight movement and Netherlands to the World Bank Group under the design better informed policies Multi Donor Trust Fund - Sustainable Logistics which is lacking in India at present (MDTF-SL) Scheme. Subsequently, The World Bank Group has sanctioned the proposed research study (Contract No. 7182067). An important goal of the project is to create an impact in practice. An extensive pilot study is carried out for the city of New Delhi, India with a transferable modelling approach. The city of New Delhi i.e. National Capital Territory of Delhi (NCTD) has been selected as study area for this study. By conducting extensive field surveys, metrics of city logistics, design of measurement system and data acquisition in the city of Delhi have been developed. The metrics are focused on logistics activity indicators (external and internal flows), logistics efficiency (vehicular and trip characteristics) and city livability (traffic loads in terms of vehicle kilometers travelled and emissions of pollutants). The activities carried out in the present study include:  Development of city logistics metrics  Capacity Development for Sustainable City Logistics (SCL) The World Bank Group Page | v CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report  Development of freight transport demand model and logistics flow model for the city of New Delhi  Knowledge Sharing In the present study, NCT of Delhi has been taken as study area and measured possible freight metrics from the various field studies and the summary is given below:  The journey speed of traffic stream is varying between 17 and 40 kmph and average journey speeds are around 27 kmph on the road network of Delhi. The journey About 1.24 million vehicles (about times are around 2.3 minutes per km 10% Freight and 4% of SMVs) enter which shows that the road network of E XECUTIVE S UMMARY and leave Delhi city daily which has Delhi is moderately congested all the time. grown with about 3% per annum  On a normal working day, a total of about 1.24 million vehicles enter and leave Delhi city which has grown with 3% per annum (about 1.02 million vehicles in 2009). The freight traffic forms about 10% of the total traffic with another 4% of traffic is composed of slow moving vehicles (SMV) like bicycle, cycle rickshaws, animal carts etc.  Maximum number of vehicles in the order of about 354 thousands entering and exiting through Rajokri Border followed by Ghazipur Border with an entry/ exit traffic volume of about 163 thousands and Kalindi Kunj Border with an entry/ exit traffic volume of about 126 thousands. About 100 thousand Freight vehicles  A total of about 100 Thousand freight vehicles enter and leave Delhi city on a normal enter and leave Delhi city daily and working day and about 21% of these freight about 21% of these just passing vehicles are found to be passing through the through the city due to absence of city which was almost same in 2009. Though adequate bypasses around Delhi the total traffic increased, freight traffic remain stagnated at outer cordons because of new bypass roads come around the city of Delhi such as Noida-Greater Noida Expressway, Yamuna Expressway, Kundli-Manesar-Palwal (KMP) Expressway etc. The World Bank Group Page | vi CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report  The freight vehicle types namely Goods Auto (GA), Goods Van (GV), Light Truck (LT), Heavy Truck (HT) and Multi- Axle Truck (MT) are observed at entry The mean age of freight vehicles and exit locations of outer cordons. In ranging between 4.5 and 5.0 years and case of passing through traffic, HT has almost 50% share followed by MT and the share of 10 year and more old LT has share of about 18% each. vehicles is ranging from 1 to 6% (within Smaller Goods Vehicles (GA and GV) the city) and 5 to 9% (outer cordons) has a share of about 14% of passing through traffic. This can be attributed to the fact that the heavy vehicles travel long distances compared to light and small vehicles. E XECUTIVE S UMMARY  From focal points studies within the city, it has been observed that maximum number of vehicles per day in the order of about 8 thousands entering and exiting through Ghanta Ghar Sabzi Mandi followed by Azadpur Sabzi Mandi with an entry/ exit volume of about 7 thousands and Chandini Chowk Area with an entry/ exit volume of about 5 thousands. It has also been found that about 40% are consisting of Goods Auto (GA) and Goods Van (GV) in that. The vehicle types of LT, HT and MT are in the range of 24%, 11% and 8% respectively. The other freight vehicles are about 18%.  The mid block traffic studies reveled that the total daily volume (24 hours) on Ring Road (Naraina) is almost 190 thousands with a peak volume of about 16 thousands (19:00 ~ 20:00 Hrs). The summary of traffic on all the mid block locations shows about 80% are consisting of private vehicles mainly cars and two wheelers. The freight transport is about 7% mainly consist of Goods Autos, LT, HT and MT.  The mean age of different freight vehicles is almost same at outer cordons and within the city varying between 4.5 and 5.0 years and the share of 10 year and more old vehicles within the city is ranging from 1 to 6% and 5 to 9% at outer cordons.  The fuel usage distribution of different freight vehicles at outer cordons and within the city results shows that Heavy Vehicles (HT and MT) mostly use Diesel where as Goods Auto and Goods Van almost use CNG as fuel. In case of LT, about 45% and 75% use Diesel as fuel at outer cordons and within city respectively. The World Bank Group Page | vii CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report  The ownership of different freight vehicles at outer cordons and within the city has been analysed and found that private company freight vehicles are high in On an average, the freight vehicle case of heavy vehicles (HT and MT) at travels about 200 km/day and this outer cordons and within the city. The private company and personal freight clearly indicates that these vehicles vehicle share is almost same for light face lot of congestion, delays and vehicles (LT, GA and GV) within the other problems leading to increased city whereas private company freight travel times and operating costs vehicle share is higher at outer cordons.  E XECUTIVE S UMMARY The mileage (fuel efficiency in terms of km/litre) of different freight vehicles has been observed that light vehicles (LT, GA and GV) have higher fuel efficiency which is mostly run on CNG. Heavy freight vehicles have fuel efficiency about 6.5 and 4.8 km/litre for HT and MT respectively. Light vehicles namely LT has about 11 km/litre, where as GA and GV has more than 14 km/kg of CNG.  The freight vehicle travels about 20-25 km within the city and the maximum average distance travelled in a day by these freight vehicle types is about 200 km. This clearly indicates that these freight vehicles face lot of congestion and other problems to travel more distances in a day experiencing lot of delays and increased operating costs.  The frequency of trips of different freight vehicles analysis shows that Light Vehicles are having more daily trips and Heavy Vehicles are more in Occasional trips.  From the results of weight carried by different freight vehicles, it has been observed that Estimated total weight carried by MT Vehicles are carrying average weight more freight vehicles on the road network than 13 tonne where as HT vehicle is carrying of Delhi is about 2.480 Million average load of 5-6 tonne. The LT is carrying Metric Tonne (MMT) per day average weight about 2 tonne and smaller vehicles like GA and GV are carrying less than a tonne.  The share of empty vehicles is about 10-20% across different freight vehicle types. Further the total weight carried by these freight vehicles on the entire road network of Delhi has been estimated to be about 2.480 Million Metric Tonne (MMT) per day. The World Bank Group Page | viii CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report  In the present study, freight transport demand model has been developed considering same traditional approach of four- stage modelling (Freight Trip Generation, Freight Modal Split, Total freight trips generated daily in the Freight Trip Distribution and Freight city of Delhi are estimated to be about Traffic Assignment). Accordingly, the 500 thousands (share of empty freight total trips generated daily in the city of vehicles is about 10-20%) which is 3% Delhi from all the zones are estimated of total trips generated in Delhi and it is to be about 500 thousands of freight trips. The final freight modal split for increasing with a growth rate of 4% per different freight vehicles namely GA, year E XECUTIVE S UMMARY LT, HT and MT shows almost equal share varying between 22-25% where as GV has about 5% share. The Freight O-D Matrix estimated from Freight Trip Distribution adopting Gravity Model.  The majority of freight trips are Internal - Internal (I-I) which is almost 80%. The Internal-External (I-E) and External-Internal (E-I) are almost same about 8% each and External-External (E-E) trips (passing through) are about 4%.  The analysis of modal split of these freight trips shows that heavy freight vehicle share is about 26% in case of I-I Trips, about 43% in case of I-E Trips, about 53% in case of E-I Trips and about 61% in case of E-E Trips.  The share of freight trips is only about 3% and passenger trips are about 97% in the city of Delhi. Though the share of freight trips is very insignificant, it is going to influence huge in traffic congestion, air pollution and road safety related issues of the city of Delhi.  The freight trips are estimated to increase to The VKT by freight vehicles about 572 thousands by the year 2021 with a growth rate of 4% per annum. are going to be 13 Millions in  The estimated traffic loads in terms of vehicle 2020 increasing with a growth kilometers travelled (VKT) on the road network of Delhi rate of about 8% per annum for the year 2017 and forecasted VKT for the year 2021 are about 240 Millions and 300 Millions respectively. The VKT by freight vehicles are going to be about 10 Million and 13 Millions in 2017 and 2020 respectively which The World Bank Group Page | ix CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report is having a share of about 4%. The growth of total VKT is increasing with 7% per annum growth whereas freight vehicles growth is about 8% per annum. Taking into account the findings from the inventory of the literature, a list of indicators to measure New Delhi’s performance in the area of SCL has been proposed which also included suggested units and sources for measurement. A questionnaire has been designed and proposed to use the same for assessment of the level of knowledge in the area of SCL among Present study has shown that a all the local authorities and policy makers, Decision Support System on the basis freight operators and experts. of Urban Strategy (Software developed E XECUTIVE S UMMARY With respect to decision support systems, the by TNO) is feasible for Delhi for Urban Strategy (Software developed by TNO) evaluation of different scenarios has been customised to visualise freight patterns and to model the impacts of different traffic measures in the city of Delhi. In the present study, it has been demonstrated that Urban Strategy is able to use the available data to construct a basic working model and distribute traffic on the basis of that model. The appropriate data collected in this study has been successfully uploaded, and in combination with open-source data from OpenStreetMaps, Urban Strategy has been used to carry out an initial traffic assignment, and the results displayed in the 2D and 3D interfaces and the Web interface. It can be concluded that the developed system can be utilised as decision support system to evaluate various transport policies by estimating traffic loads and emission loads from vehicular traffic. The following findings have emerged in the present study through the development of the decision support system for the city of Delhi and evaluation of different scenarios:  Based on the results of estimated traffic loads and emission loads from vehicular traffic, it can be said that the contribution of passenger car movements to road transportation emissions is dominant in comparison to road freight movements.  On the basis of these findings it appears that some measures, such as freight hubs, will only be effective if they are combined with measures to lower fleet emissions, such as the use of electric vehicles. The World Bank Group Page | x CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report  Removal of diesel vehicles older than 10 years shows 4 – 11% decrease in total COx, NOx and PM10 emissions, and negligible difference in Benzene or Hydrocarbon levels caused by freight traffic.  Freight hubs and heavy vehicle restrictions may lead to increased overall emissions if Removal of diesel vehicles there is no change to the emissions profile for older than 10 years shows 4 - the vehicles that replace them. 11% decrease in total COx,  Introduction of electric freight vehicles shows NOx and PM10 emissions promising results for reduction in emissions, dependent on the penetration rate achieved.  The impact of measures only targeting freight movements will be limited, due to its E XECUTIVE S UMMARY relatively small contribution to air pollution. Therefore, it would be valuable to apply this system on the integrated challenge of the city of Delhi with regards to air pollution and traffic noise. In the present study, an attempt has been made to Some measures, such as freight see the feasibility to apply Agent Based Modelling hubs and heavy vehicle (ABM) for City Logistics. A large variety of restrictions, will only be effective if activities (e.g. freight vehicle movements, parking, they are combined with measures loading/unloading goods) and stakeholders (consumer, retail, forwarding, trade, and to lower fleet emissions, such as manufacturing) is associated with urban freight the use of electric vehicles transportation, which differ with respect to location, types of goods and stakeholders’ characteristics. Altogether this creates a complex system that is difficult to manage. The emerging system is a direct result of colliding decisions and often conflicting objectives of different stakeholders. A well-designed agent based modelling approach that includes the business models and perception of multiple stakeholders of the domain would be useful to identify effective solutions (e.g. policy, regulation, facilitating schemes) for the above mentioned problems. The World Bank Group Page | xi CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report By collecting and synthesizing information about individual stakeholders and entities at micro level, an agent-based model (ABM) for Delhi can be created to analyse the interactions between urban freight entities to understand the background of movements of freight delivery vehicles and responses of these agents to policies for urban freight related problems. An agent-based model (ABM) for Delhi Furthermore, a role-playing game can be can be created to analyse the developed using an ABM as an interactive tool for urban freight stakeholders to interactions between urban freight understand the decision-making processes entities to understand the background of and complexity of urban freight activities. movements of freight delivery vehicles Such a role-playing game can act as E XECUTIVE S UMMARY and responses of these agents to policies platform to experience the complexity and for urban freight related problems. emergence effect of decision-making by different stakeholders. As data collection is challenging part of ABM development, we recommend that, initially, a proof-of-concept model is developed for a zone in Delhi. A conceptual framework provided in this report as starting point for model development. Data collection for a small area should be relatively easy and allow addressing the research challenges of ABM development. From the above, it can be said that agent-based modelling can be useful for many unrequited policy analysis problems in the urban freight domain. However, the time and precision required for developing such a system is a challenging task. Overcoming these challenges requires painstaking efforts but assures in-depth understanding about urban freight transportation process for successful urban freight policy analysis. In conclusion, in the present study, four important priorities for the future have been identified, which could be part of a joint mission statement of the collective of stakeholders to achieve sustainable urban freight systems:  Reduction of negative effects of urban freight transport while maintaining productivity.  Identification of workable urban freight solutions including roadmaps towards data, tools and appropriate research.  Increase of the knowledge base including data collection, models and scenarios. The World Bank Group Page | xii CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report  Collaboration with other stakeholders to realize solutions towards sustainability. In the final workshop of the project, these points were signed symbolically by all participants, as an expression of the start of a shared effort to create a follow-up to this project including further elaborated policy information, based on a process of joint fact finding and alignment of ideas by industry, governmental and knowledge partners. Mission statement of the collective of stakeholders to achieve sustainable urban freight systems E XECUTIVE S UMMARY  Reduction of negative effects of urban freight transport while maintaining productivity  Identification of workable urban freight solutions including roadmaps towards data, tools and appropriate research  Increase of the knowledge base including data collection, models and scenarios  Collaboration with other stakeholders to realize solutions towards sustainability The World Bank Group Page | xiii CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report The World Bank Group Page | xiv CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report TABLE OF CONTENTS Foreword .......................................................................................................................... i Study Team ....................................................................................................................... iii Executive Summary .................................................................................................................... v Table of Contents ..................................................................................................................... xv List of Figures ...................................................................................................................... xix List of Tables .................................................................................................................... xxiii 1. BACKGROUND ..................................................................................................................... 1 1.1. Freight Transport in India ........................................................................................... 1 1.2. Delhi City as Study Area ............................................................................................ 2 1.3. Need for the Present Study ......................................................................................... 4 2. OBJECTIVES AND SCOPE OF THE RESEARCH STUDY ............................................................. 7 2.1 Objectives ................................................................................................................... 7 2.2 Scope of the Study ...................................................................................................... 7 3. METHODOLOGY ................................................................................................................... 9 3.1. Consortium of Organisations ...................................................................................... 9 3.2. Work Packages (WP) .................................................................................................. 9 3.2.1. WP-1: Development of city logistics metrics .................................................. 9 3.2.2. WP 2: Capacity Development for Sustainable City Logistics ...................... 10 3.2.3. WP 3: Decision Support Systems .................................................................. 10 4. SUSTAINABILITY CITY LOGISTICS (SCL) ........................................................................... 13 4.1. Introduction ............................................................................................................... 13 4.2. Sustainable City Logistics: Definitions .................................................................... 13 4.2.1. City Logistics ................................................................................................ 13 4.2.2 Sustainability................................................................................................. 14 4.2.3. CL Impacts on the Triple Bottom Line.......................................................... 17 4.3. Additional Contextual Factors for Delhi................................................................... 18 4.3.1. Policy on Urban Development and City Logistics........................................ 18 4.3.2. Transport Vehicles, Air Quality and Air Pollution Abatement ..................... 19 4.3.3. Developing Country Challenges ................................................................... 20 4.4. SCL – A Review of the Literature ............................................................................ 21 The World Bank Group Page | xv CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report 4.4.1. Introduction................................................................................................... 21 4.4.2. Literature on Criteria, Indicators and Frameworks ..................................... 21 4.4.3. Discussion ..................................................................................................... 24 4.5. Proposed Metrics for SCL ........................................................................................ 24 5. LOGISTIC METRICS FOR CITY OF DELHI............................................................................. 29 5.1. General ...................................................................................................................... 29 5.2. List of Field Surveys ................................................................................................. 29 5.3. Speed Data ................................................................................................................ 30 5.4. Outer Cordon (OC) Traffic Volume Data ................................................................. 34 5.5. Outer Cordon Interview Data.................................................................................... 40 5.6. External Travel.......................................................................................................... 42 5.7. Focal Point Freight Traffic Survey ........................................................................... 44 5.8. Focal Point Interview Data ....................................................................................... 48 5.9. Mid Block Traffic Volume Survey ........................................................................... 49 5.10. Freight Vehicular and Travel Characteristics ........................................................... 52 5.9.1. Age Distribution ............................................................................................ 52 5.9.2. Fuel Used ...................................................................................................... 55 5.9.2. Ownership of Freight Vehicle ....................................................................... 55 5.9.3. Fuel Efficiency .............................................................................................. 56 5.9.4. Distance Travelled ........................................................................................ 57 5.9.5. Frequency of Trips ........................................................................................ 58 5.9.6. Weight Carried.............................................................................................. 58 5.11. Development of Freight Transport Demand Models ................................................ 59 5.11.1 Background ................................................................................................... 59 5.11.2. Traffic Zones, Road Network and Socio-economic Data.............................. 60 5.11.3 Freight Trip Generation Models ................................................................... 62 5.11.4. Freight Modal Split ....................................................................................... 65 5.11.5. Freight Trip Distribution Models ................................................................. 66 5.11.6. Freight Trip Assignment ............................................................................... 68 5.12. Pattern of Total Freight Trips ................................................................................... 69 5.13. Forecasting of Freight Trips from Freight Transport Demand Models .................... 71 5.14. Estimation of Traffic Loads on the Road Network ................................................... 72 The World Bank Group Page | xvi CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report 6. CAPACITY DEVELOPMENT FOR SUSTAINABLE CITY LOGISTICS ......................................... 75 6.1. Capacity Assessment ................................................................................................ 75 6.2. Knowledge Development Plan ................................................................................. 77 6.3. Stakeholders Meetings and 1st Workshop on MEGALOG ....................................... 80 6.4. Organizing Short Courses ......................................................................................... 83 6.5. National Level Dissemination Workshops ............................................................... 84 6.6. Design of Booklet on Sustainable City Logistics ..................................................... 90 6.7. Future Capacity Building Process on Sustainable City Logistics ............................. 91 7. DECISION SUPPORT SYSTEM: POLICY ANALYSIS AND VISUALIZATION TOOLSET .............. 93 7.1. Introduction ............................................................................................................... 93 7.1.1. Background ................................................................................................... 93 7.1.2. Policy Analysis and Visualization Toolset .................................................... 94 7.1.3. Urban Strategy .............................................................................................. 95 7.2. Technical Background of Urban Strategy................................................................. 95 7.2.1. Software architecture .................................................................................... 95 7.2.2. Interfaces....................................................................................................... 97 7.2.3. Data model and data store .......................................................................... 100 7.3. Setup of Urban Strategy for Delhi City .................................................................. 101 7.3.1. Methodology ............................................................................................... 101 7.3.2. Choice of coordinate system ....................................................................... 102 7.3.3. Assignment method ..................................................................................... 103 7.3.4. Emission Factors and Emission Loads ....................................................... 103 7.3.5. Uploading the data into a new database .................................................... 103 7.5.6. Input data .................................................................................................... 104 7.4. Policy Evaluation with Urban Strategy................................................................... 107 7.4.1. Freight Transport Policies/ Scenarios ........................................................ 107 7.4.2. Evaluation Results of Policies .................................................................... 111 7.5. Summary of Findings .............................................................................................. 119 7.5.1. Urban Strategy for Delhi ............................................................................ 119 7.5.2. Policy Findings ........................................................................................... 120 7.5.3. Suggestion for further research .................................................................. 120 7.5.4. Discussion ................................................................................................... 121 The World Bank Group Page | xvii CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report 8. FEASIBILITY OF AGENT BASED SIMULATION MODEL FOR URBAN FREIGHT ACTIVITIES IN DELHI .......................................................................................................... 123 8.1 Complexity in Urban Freight Transportation ......................................................... 123 8.2 Agent Based Modelling Approach.......................................................................... 123 8.3 ABM for Urban Freight Activities in Delhi NCR .................................................. 124 8.3.1. Conceptual Framework .............................................................................. 124 8.3.2. Application .................................................................................................. 127 8.3.3. Proof of concept ABM for Delhi ................................................................. 128 8.3.4. Data needed for urban freight ABM in Delhi ............................................. 128 8.3.5. Research capabilities for ABM development .............................................. 129 8.3.5. Challenges for ABM development for Delhi ............................................... 129 8.4 Summary ................................................................................................................. 130 9. CONCLUDING REMARKS .................................................................................................. 133 10. ANNEXURES .................................................................................................................... 141 The World Bank Group Page | xviii CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report LIST OF FIGURES Figure 1.1: Typical View of Trucks entering Delhi after the end of restriction time ................ 2 Figure 1.2: Typical View of Light Trucks moving on Delhi Roads .......................................... 3 Figure 1.3: Typical View of Different Types of Goods Vehicles (Heavy Trucks, Light Trucks, Goods Autos etc.) moving on Delhi Roads ............................................. 3 Figure 1.4: Typical View of Other Modes of Goods Transportation in Delhi .......................... 4 Figure 2.1: The Selected Study Area of National Capital Territory of Delhi (NCTD) ............. 8 Figure 5.1: Selected Arterial Roads for Journey Speed Data Collection ................................. 30 Figure 5.2: VBOX Equipment used for Journey Speed Data Collection on Selected Corridors ............................................................................................................................. 31 Figure 5.3: Average Journey Time on Selected Stretches of Delhi Road Network ................ 33 Figure 5.4: Selected Locations of Outer Cordons for Freight Traffic Data Collection ........... 35 Figure 5.5: Typical Views of Traffic Volume Count Survey at Different Locations of Outer Cordons for Freight Traffic Data Collection ...................................................... 36 Figure 5.6: Hourly Distribution of Classified Traffic Volume and Traffic Composition at Rajokri Border .................................................................................................... 38 Figure 5.7: Traffic Composition at Different Outer Cordons of Delhi .................................... 39 Figure 5.8: Typical Views of Interview Survey at Different Locations of Outer Cordons for Freight Traffic Data Collection ........................................................................... 41 Figure 5.9: Pattern of Total External Traffic at Outer Cordons of Delhi ................................ 42 Figure 5.10: Pattern of Freight External Traffic at Outer Cordons of Delhi ........................... 43 Figure 5.11: Selected Locations to Conduct Focal Point Survey in Delhi (24 hours) ............. 45 Figure 5.12: Hourly Distribution of Classified Freight Traffic Volume and Traffic Composition Azadpur Sabzi Mandi .................................................................... 46 Figure 5.13: Freight Traffic Composition at Different Focal Points of Delhi ......................... 48 Figure 5.14: Hourly Distribution of Classified Traffic Volume and Traffic Composition at Rajghat on Ring Road ......................................................................................... 51 Figure 5.15: Traffic Composition at Different Mid Block Locations of Delhi ....................... 52 Figure 5.16: Age Distribution of Different Freight Vehicles at Outer Cordons in Delhi ........ 53 The World Bank Group Page | xix CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Figure 5.17: Age Distribution of Different Freight Vehicles within the City of Delhi (Focal Point) ................................................................................................................... 54 Figure 5.18: Fuel Usage Age Distribution of Freight Vehicles at Outer Cordons and within the City of Delhi.................................................................................................. 55 Figure 5.19: Ownership Distribution of Freight Vehicles at Outer Cordons and within the City of Delhi ....................................................................................................... 56 Figure 5.20: Fuel Efficiency of Different Freight Vehicles ..................................................... 56 Figure 5.21: Distance Travelled by Different Freight Vehicles .............................................. 57 Figure 5.22: Frequency Distribution of Freight Vehicles at Outer Cordons and within the City of Delhi ............................................................................................................... 58 Figure 5.23: Frequency Distribution of Freight Vehicles at Outer Cordons and within the City of Delhi ............................................................................................................... 59 Figure 5.24: Share of Empty Vehicles in Different Typed of Freight Vehicles ...................... 59 Figure 5.25: Traffic Analysis Zones (TCZ) considered for the City of Delhi ......................... 61 Figure 5.26: Created Road Network (Links and Nodes) for the City of Delhi ........................ 62 Figure 5.27: Estimated Total Freight Trip Productions and Attractions in Delhi (2017) ........ 65 Figure 5.28: Modal Split of Total Freight Trips ...................................................................... 65 Figure 5.29: Typical View of Estimated Total Freight O-D Matrix for the City of Delhi ...... 67 Figure 5.30: Desire Line Diagram of O-D Matrices for Freight Trips .................................... 68 Figure 5.31: Pattern of Total Freight Trips i Delhi .................................................................. 69 Figure 5.32: Freight Modal Split for Different Types of Trips in Delhi.................................. 70 Figure 5.33: Share of Freight Trips in the Total Trips of Delhi .............................................. 70 Figure 5.34: Forecasted Total Freight Trips for the Year 2021 in Delhi ................................. 71 Figure 5.35: Estimated Vehicle Kilometers Travelled /Day for different Vehicle Types for Different Years.................................................................................................... 72 Figure 6.1: Description of Different Levels of Organisations with Different Objectives and Requirements ...................................................................................................... 76 Figure 6.2: Some of the News Clippings on Delhi Air Pollution and Entry/ Ban of Trucks in the City ................................................................................................................ 77 Figure 6.3: Road Map towards Zero Emission in the City of Rotterdam, The Netherlands ... 78 Figure 6.4: Existing (without Consolidation) and Proposed Situation of Trips into the city with Consolidation .............................................................................................. 79 The World Bank Group Page | xx CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Figure 6.5: Agenda of 1st Workshop on MEGALOG held on 9th May 2017 at CSIR-CRRI, New Delhi ........................................................................................................... 81 Figure 6.6: Some Views of 1st Workshop on MEGALOG held on 9th May 2017 at CSIR- CRRI, New Delhi ................................................................................................ 82 Figure 6.7: Agenda of Short Course on Sustainable City Logistic held on 12th December 2017 at CSIR-CRRI, New Delhi .................................................................................. 85 Figure 6.8: Some Views of Short Course on Sustainable City Logistics held on 12 th December 2017 at CSIR-CRRI, New Delhi ....................................................... 87 Figure 6.9: Agenda of National Dissemination Workshop on MEGALOG held on 13th December 2017 at CSIR-CRRI, New Delhi ....................................................... 88 Figure 6.10: Some Views of National Dissemination Workshop on MEGALOG held on on 13th December 2017 at CSIR-CRRI, New Delhi ................................................ 90 Figure 6.11: View of Designed Booklet on Sustainable City Logistics .................................. 91 Figure 7.1: Screenshot of Urban Strategy outputs (Source: TNO) .......................................... 94 Figure 7.2: Software architecture for Urban Strategy .............................................................. 96 Figure 7.3: Indicators implemented in Urban Strategy such as Noise Annoyance, Travel Times or the Total Emissions ............................................................................. 97 Figure 7.4: 2D interface with GIS (Geographical Information System) in Urban Strategy .... 98 Figure 7.5: 3D interface with GIS (Geographical Information System) in Urban Strategy .... 99 Figure 7.6: Screenshot of Web Interface of Urban Strategy .................................................. 100 Figure 7.7: Overview of the Decision Support System on the basis of Urban Strategy ........ 102 Figure 7.8: Screenshot from the 2D module analysed in Urban Strategy for New Delhi City ........................................................................................................................... 106 Figure 7.9: City of Delhi loaded in the 3D interface of Urban Strategy ................................ 107 Figure 7.10: Considered Locations of Freight Hubs in Scenario 2........................................ 109 Figure 7.11: Considered Closed-off Area in New Delhi City Center under Scenario 3 ........ 110 Figure 7.12: Proposed High Capacity Elevated Corridor in Scenario 4 ................................ 111 Figure 7.13: Web Interface of Urban Strategy of New Delhi ................................................ 112 Figure 7.14: Result of Traffic Flows In Relation To Capacity in Base Scenario. ................. 113 Figure 7.15: Spatial Distribution of NOx Emissions due to Road Traffic in the Base Scenario ........................................................................................................................... 113 Figure 7.16: Spatial Distribution of NOx Emission Reduction due to Scenario 1 ................ 114 The World Bank Group Page | xxi CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Figure 7.17: Spatial Distribution of Change in NOx Emission due to Scenario 2 ................ 115 Figure 7.18: Spatial Distribution of Change in NOx Emission due to Scenario 3 ................ 115 Figure 7.19: Spatial Distribution of Change in NOx Emission due to Scenario 4 ................ 116 Figure 7.20: Spatial Distribution of Change in NOx Emission due to Scenario 6 ................ 117 Figure 7.21: Breakdown of Emission Totals for Road Transport Vehicle Types in Delhi for Different Substances ......................................................................................... 118 Figure 7.22: Comparison of Emission Totals for Road Transport in Delhi for Different Scenarios ........................................................................................................... 118 Figure 7.23: Comparison of emission totals for road freight transport in Delhi for different scenarios. ........................................................................................................... 119 Figure 8.1: Conceptual framework of agent based model for urban freight domain in Delhi NCR .................................................................................................................. 125 Figure 8.2: Development stages of agent based model for urban freight in Delhi NCR ....... 126 The World Bank Group Page | xxii CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report LIST OF TABLES Table 4.1: List of Indicators to Measure New Delhi’s Performance in the area of SCL ......... 25 Table 5.1: Selected Stretches for Journey Speed Data Collection ........................................... 31 Table 5.2: Observed Journey Speed on Inner Ring Road (S-8) in Up Direction..................... 32 Table 5.3: Observed Journey Speed on Inner Ring Road (S-8) in Down Direction ................ 32 Table 5.4: Summary of Observed Journey Speed on Selected Stretches ................................ 33 Table 5.5: Selected Outer Cordon Locations for Freight Traffic Data Collection for 24-Hour Duration ................................................................................................................. 35 Table 5.6: Selected Vehicles Types Considered under Freight Transport in the Present Study ................................................................................................................................ 37 Table 5.7: Classified Traffic Volume at Rajokri Border ......................................................... 38 Table 5.8: Summary of Classified Traffic Volume (24 hours) at Different Outer Cordons of Delhi ....................................................................................................................... 39 Table 5.9: Sample Size of Freight Vehicles Collected at Different Outer Cordons of Delhi (24 hours) ..................................................................................................................... 40 Table 5.10: Selected Locations to Conduct Focal Point Survey in Delhi (24 hours) .............. 44 Table 5.11: Classified Freight Traffic Volume at Azadpur Sabzi Mandi ................................ 46 Table 5.12: Summary of Classified Freight Traffic Volume (24 hours) at Different Focal Points of Delhi ....................................................................................................... 47 Table 5.13: Selected Locations to Conduct Focal Point Survey in Delhi (24 hours) .............. 49 Table 5.14: Selected Mid-Block (MB) Locations to Conduct Traffic Volume Survey in Delhi (24 hours) ............................................................................................................... 50 Table 5.15: Classified Traffic Volume at Naraina on Ring Road............................................ 51 Table 5.16: Summary of Classified Traffic Volume (24 hours) at Different Mid Block Locations of Delhi.................................................................................................. 51 Table 5.17: Share of 10 Year and More Old Vehicles within the City and at Outer Cordons 54 Table 7.1: Mapping of OSM data to Urban Strategy categorization ..................................... 104 The World Bank Group Page | xxiii CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report The World Bank Group Page | xxiv CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report 1. BACKGROUND 1.1. Freight Transport in India Transport is a key element in the infrastructure of a nation as it provides services essential for promoting economic and social development and plays a significant role in influencing the pattern of distribution of economic activities and improving productivity. India spends 15 to 20% of its GDP on transport and logistics compared to an average 8 to 10% in other developing countries. Indian freight transport market is expected to grow at a Compound Annual Growth Rate (CAGR) of about 13% by 2020 driven by the growth in the manufacturing, retail, Fast-moving consumer goods (FMCG) or consumer packaged goods (CPG) and e-commerce sectors which have large freight transport requirements across the country which is generally done by road transportation. In India, road freight constitutes around 63% of the total freight movement consisting of 2.2 million heavy duty trucks and 0.6 million light duty trucks covering more than 18,00,000 km of road length carrying more than 3000 MMT (million metric ton) of load annually. Owing to poor road conditions and check-post delays, trucks in India travel for 20 days a month on an average compared to 25 days in developing countries (TCI and IIM, 2016). The delays could range from five per cent of time taken in a journey to a high of 25%. The average speed of trucks on Indian roads is about 20 km/ hour and a truck in India can cover only 250-300 km a day compared to 700-800 km in developed countries such as the US and Europe. Moreover, on an average, total trip expenses increases about 15 per cent due to the delays at check-posts and on-road for filling in forms required by various government departments, checking of documents and physical checking of the vehicles, drivers and consignments by Regional Transport Offices and traffic police, and collecting highway toll and taxes. The working conditions for the truck drivers also deteriorating and they work for long hours, resulting in high stress and fatigue, which leads to accidents. There is increasing recognition in India that transport infrastructure could become a serious bottleneck for future economic growth. The need is recognized for collaboration amongst stakeholders to identify optimal policies and pursue a rapid deployment of improvements. The World Bank Group Page | 1 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report 1.2. Delhi City as Study Area The present study considers Delhi urban road network as object of study. As per the Census of India (2011), Delhi has 16.75 million population which recorded a decennial population growth of about 20%. The increase in urbanization leads to growth of vehicular population in urban areas and this scenario accelerates various traffic problems such as congestion, air pollution, and reduction in safety. There is significant momentum in government to take the city logistics system as sustainable development priority. Recent verdict by National Green Tribunal (NGT) of India on banning 10 years old trucks to enter into the city of Delhi in view of high pollution emission by these vehicles. In order to study and understand these issues, new policies are needed and innovation needs to be promoted. The roads of Delhi have number of time restrictions for goods vehicles and there is 24 hours ban for some roads. The restriction is from 7:00 AM to 11:00 AM and 5:00 PM to 11:00 PM for most of the roads in Delhi. Some of the view of fright transportation in Delhi is shown in Figure 1.1 to 1.5. Figure 1.1: Typical View of Trucks entering Delhi after the end of restriction time The World Bank Group Page | 2 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Figure 1.2: Typical View of Light Trucks moving on Delhi Roads Figure 1.3: Typical View of Different Types of Goods Vehicles (Heavy Trucks, Light Trucks, Goods Autos etc.) moving on Delhi Roads The World Bank Group Page | 3 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Figure 1.4: Typical View of Other Modes of Goods Transportation in Delhi 1.3. Need for the Present Study Delhi is known as one of the most air polluted cities in the world as the air quality index (AQI) of most areas is above 150 (Delhi Air Pollution: Real-time AQI, 2017). AQI from 0 to 100 is in range of good to moderate. AQI more than 150 is considered unhealthy (Air Now, 2017). Emission from motor vehicles is one of the major reasons for poor quality in Delhi. The traffic congestion on Delhi road is as intimidating as the polluted air. It was also revealed from the past studies that about 100,000 freight vehicles crossed 10 count stations at the borders of Delhi in a day (CRRI, 2009). Clearly, freight transportation has its fair share in pollution and congestion of Delhi. The average share of freight transportation vehicles in Delhi is relatively low in overall situation. However, due to time window restrictions by local authorities, the share of freight vehicles varies during different time of the day/night. For instance, certain types of freight activities (e.g. furniture delivery, milk van, etc.) are allowed between 8 am and 4 pm. During that time the share of freight vehicle increases to 15-20%. In The World Bank Group Page | 4 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report the night after 12 am, all freight vehicles are allowed in the city resulting in majority of freight vehicles on Delhi road network. LCVs, trucks and auto rickshaws form backbone of urban goods movement in Delhi for longer distances. For short distances, non-motorised vehicles (e.g. animal cart, hand cart, head load, cycle Rickshaw) are extensively used, especially in highly congested parts of the Delhi (Gupta, 2017). Another interesting fact is that with online shopping spree companies are using Motorized Two Wheeler (MTW) trips, used as a way to navigate the high density and congestion of Delhi (Nilanjena, et. al., 2016). Understanding and forecasting freight movements is critical to plan for future transportation in terms of capacity augmentation, operation, preservation, safety and security, energy and economy investment needs. Many demand forecasting models and data sources are more appropriate for passenger transportation than for forecasting freight movements and understanding freight travel behaviour. Creating better data and models is needed to enable planners to better predict freight movement and design better informed policies. In view of this, the objectives of the study have been conceptualised on Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) and submitted a proposal on the same by CSIR-CRRI, TNO and TU-Delft to the World Bank Group under the Multi Donor Trust Fund - Sustainable Logistics (MDTF-SL) Scheme. Subsequently, The World Bank Group has sanctioned the proposed research study (Contract No. 7182067) and the defined objectives are presented in the next Chapter. References Census of India (2011), Government of India. TCI and IIM Calcutta (2016), “Operational Efficiency of Freight Transportation by Road in India” Joint Study Report, 3rd Edition, 2016. http://www.novonous.com/publications/freight-transport-market-india-2015-2020 http://www.thehindubusinessline.com/todays-paper/tp-logistics/average-speed-of-trucks-on- indian-roads-is-20-kmhr-study/article1069670.ece Delhi Air Pollution: Real-time Air Quality Index (2017). http://aqicn.org/city/delhi/ Air Now (2017). https://airnow.gov/index.cfm?action=aqibasics.aqi MoUD (2016), “Decongesting Traffic in Delhi”, Report of the High Powered Committee, Ministry of Urban Development, Govt. of India, June 2016. The World Bank Group Page | 5 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report CRRI. (2009). Urban Road Traffic and Air Pollution in Delhi, Central Road Research Institute (CRRI), Final Report Submitted to Society of Indian Automobile Manufactures (SIAM), 2009. Gupta, S. (2017), “Role of Non -Motorized Transport in Distribution of Goods in the Metropolitan City of Delhi”, Transportation Research Procedia, Elsevier Publishers, Vol. 25, pp. 978–984 Nilanjana De-Bakshi, Nomesh B. Bolia, Geetam Tiwari and Jose Holguin-Veras, (2017) “Urban Freight in Delhi: Characteristics and Mobility Restrictions”, VREF Research Brief 8 (RB08-2017), Volvo Research and Educational Foundations (VREF). The World Bank Group Page | 6 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report 2. OBJECTIVES AND SCOPE OF THE RESEARCH STUDY 2.1 Objectives The objective of the initiative is twofold: 1) Capacity development for R&D in the area of sustainable logistics for cities, in two areas:  Metrics for measurement of city logistics sustainability in livability and logistics efficiency and  Tools for policy design 2) Development, transfer and application of decision support system specifications for public policies and strategies related to sustainable megacity logistics. 2.2 Scope of the Study An important goal of the project is to create an impact in practice. An extensive pilot study is carried out for New Delhi with a transferable modelling approach. The city of Delhi i.e. National Capital Territory of Delhi (NCTD) has been selected as study area for this study. The geographical area coverage and the road network for the study area of NCTD have been shown in the Figure 2.1. This initiative will cover development of city logistics metrics, design of the measurement system and a pilot for data acquisition. The metrics will be focused on logistics activity indicators (flows, warehouses), logistics efficiency (lead times and costs) and city livability (emissions of pollutants). It will allow to benchmark results with other megacities globally. The study will be conducted in close collaboration with policy-makers, local authorities, firms and local organizations. The activities to be carried out include:  Development of city logistics metrics - measurements of key performance indicators of a city, in relation to freight transportation and logistics processes such as storage and handling;  Capacity Development for Sustainable City Logistics - assessment carried out among key stakeholders, including public and private parties. Evaluation including policy The World Bank Group Page | 7 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report objectives, work programmes and general knowledge position in the field of sustainable logistics for megacities;  Development of freight logistics flow model for the city of New Delhi - visualization of the city logistics metrics for monitoring purposes done in 3D city model;  Knowledge Sharing - short courses on sustainable city logistics (strategies, metrics, and tools). Figure 2.1: The Selected Study Area of National Capital Territory of Delhi (NCTD) (Source: Google Maps) The World Bank Group Page | 8 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report 3. METHODOLOGY 3.1. Consortium of Organisations In order to achieve the objectives of the study mentioned in the previous section and to implement the project for the city of New Delhi, the consortium has been proposed to form which include following research institutes:  CSIR-CRRI (Central Road Research Institute, Transportation Planning Division), New Delhi, India as Lead Organisation  TNO (Netherlands Organisation for Applied Scientific Research: section Sustainable Transport and Logistics) as Partnering Institute  Delft University of Technology (TU Delft, Section Transport and Logistics) as Partnering Institute Apart from that, the external advisors will be associated from within the VREF-CoE SUFS network. 3.2. Work Packages (WP) The initiative will be deployed in three work packages WP1 – Sustainable city logistics metrics WP2 – Knowledge transfer between R&D institutes for capacity development WP3 – Decision Support Systems 3.2.1. WP-1: Development of city logistics metrics In order to arrive at sound city logistics, measurements are important of the key performance indicators of a city, in relation to freight transportation and related logistics processes such as storage and handling. This work package will develop the metrics, design the measurement system and do a pilot for data acquisition. The metrics will be focused on logistics activity indicators (flows, warehouses), logistics efficiency (lead times and costs) and city liveability (mainly: emissions of pollutants). They will allow benchmarking with other cities globally, that are part of the CoE-SUFS network (Volvo Research and Education Foundation). This The World Bank Group Page | 9 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report approach will provide data on Delhi and serve as proof of concept in order to allow continuous monitoring and benchmarking in other cities. 3.2.2. WP 2: Capacity Development for Sustainable City Logistics This work package builds on the latest experiences of the Dutch partners TNO and TU Delft in large Global programmes (VREF Coe-SUFS), European R&D projects (CITYLAB, STRAITGHTSOL, FREVUE, CITYLOG, CIVITAS, AMITRAN) and national R&D projects (Rotterdam City Dashboard and Living Lab, Cross Chain Control Centers for City Distribution, Lean & Green) in city logistics (2014 running budget > 2 M Euro). Firstly a capacity assessment will be carried out among the key stakeholders of New Delhi City Logistics, including public and private parties. A scan and evaluation will be done of policy objectives, work programmes and the general knowledge position in the field of sustainable logistics for megacities. Subsequently a proposal is developed for knowledge development via educational programmes and knowledge organization. Two short courses on sustainable city logistics (sustainable logistics strategies, metrics, and tools) will be delivered locally. 3.2.3. WP 3: Decision Support Systems Decision support system specifications and pilot systems to be delivered are twofold. Firstly, the visualisation of the city logistics metrics for monitoring purposes is done via a Planning Support System (PSS) using the 3D city model 'Urban Strategy' of TNO. This PSS (www.tno.nl/urbanstrategy) has been applied in different cities around the world (Rotterdam, Dubai, and Shenzen) and allows tracing the generation and propagation of traffic and air pollutants. On the basis of data specified by TNO and provided by CSIR-CRRI, TNO will build a structured piloting database and demonstrator of a PSS based on Urban Strategy. The functioning of Urban Strategy application will be demonstrated for a number of policy scenarios that directly affect freight flows within the city (such as selective use of lanes, time windows, off hour deliveries etc.) and results will be visualised with Urban Strategy. The specifications, the process and follow-up recommendations will be laid down in a report. The World Bank Group Page | 10 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Subsequently, CSIR-CRRI and TU Delft will work together to develop specifications and a first prototype for a freight logistics flow model (freight travel demand model) for the city of New Delhi. This model will be designed in a way that it can function inside Urban Strategy. This will include trip generation models, OD synthesis / trip distribution models and simple tour building models. Finally, a feasibility study will be done for the transfer of a serious game, the Agent Based Model (ABM) for City Logistics developed by TU Delft. The feasibility study will address software, skills and data needs and will provide recommendations to the CSIR-CRRI for further development of a research program on this topic. The result of WP3 includes:  specification and demonstration of the model system i.e. the Urban Strategy pilot application and the prototype freight model, including recommendations for full implementation.  developed database for Urban Strategy and the prototype freight flow model  feasibility study for agent based modelling The above will be aligned with the performance management system of WP1. The World Bank Group Page | 11 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report The World Bank Group Page | 12 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report 4. SUSTAINABILITY CITY LOGISTICS (SCL) 4.1. Introduction One of the aims of the MEGALOG project is to contribute to the building of local management and scientific capacity concerning city logistics in New Delhi. Our focus lies on relevant contextual and city logistics specific data, together with the tools needed to transform this data into information and effective local policy-making. In this section, the measurement of performance of the city logistics processes which is an important input to management and scientific activities have been discussed. Along with that an overview of scientific papers which are relevant for the design of a Sustainable City Logistics (SCL) metrics framework has also been provided. Such a framework will allow quantification of urban logistics (UL) movements in (part of) New Delhi city and of its impact on congestion and emissions (pollutants, CO2 etc.). The definitions of city logistics processes and the relationship with sustainability frameworks have been provided in Section 4.2. Additional contextual factors for city logistics in New Delhi are given in Section 4.3. A description of the most common UCL metrics used in the literature is given in Section 4.4. This section has been finalized with a proposal for metrics for SCL which is given in Section 4.5. 4.2. Sustainable City Logistics: Definitions 4.2.1. City Logistics City logistics refers to the management and planning of delivering and collecting goods in urban areas (towns and city centres) by professional transport, storage and transhipment of goods. This excludes consumer shopping (C2B) but includes home deliveries (B2C). Urban freight transport (UFT) may be divided into a range of subcategories [1]:  Raw materials and semi-manufactured articles for industries;  Consumer goods for wholesalers;  Consumer goods for shops; The World Bank Group Page | 13 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report  In- and outbound consumer goods produced in the area;  Professional home deliveries;  Transit of goods trough the city.  Construction site related traffic The movement of goods has substantial economic, social and environmental impacts, both positive and negative. In the next section, the relations between city logistics and economics and the social and environmental impact will be briefly discussed. All these elements are of direct relevance in a discussion about sustainability, as can be seen in Section 5.3. 4.2.2 Sustainability In the famous Brundtland Report [2, page 54], sustainable development is defined as “a development that meets the needs of the present without compromising the ability of future generations to meet their own needs.” This implies that the economic, social and environmental impacts of a certain activity are jointly considered (‘Triple bottom line’) and public and private instruments are chosen and applied in order to enable (or stimulate) the positive impact of the respective activity and reduce the negative impact. There is a caveat in this way of thinking, because it ignores the role of power, i.e. vested interests, which has created a situation where economic and social objectives major over environmental objectives. Economic development is in this view regarded as a necessary condition for social development. Environmental policies should not restrict economic development. A country should be able to afford such policies. As [3] mentions: “development carries the dynamic tension between poverty and environmental concerns; pollution, non-renewable resource depletion and population growth.” Economic growth goes along with a larger impact on nature and natural resources, unless deliberate use of technology and efficiency improvements are able to provide clean(er) economic growth. This is the so-called decoupling of economic growth and environmental/ecological impact. The term sustainability is used to describe a situation where human activities take place within environmental ‘boundaries’, instead of a human-centered approach, where the environment may effectively be seen as a ‘prisoner’ of economic development. In the past few decades, environmental concerns receive growing attention in policy-making, which led to the introduction of policy instruments to reduce these impacts, in particular in developed countries. Developing The World Bank Group Page | 14 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report countries are lagging far behind the former countries, while the necessity is rising in developing countries. Nowadays, cleaner technologies, such as trucks with more fuel- efficient and cleaner engines, have become available and more is to be expected. Growing use of such technologies helps to lower their cost hence increases their affordability. It also reduces payback time and makes it easier to develop a positive business case for them. Legal instruments may support the introduction of cleaner technologies and business practices as well. The UNDP Development Agenda In 2007, UNDP published a Development Agenda [4]. It is especially relevant for developing countries, because it identifies their social, economic and environmental challenges. The following (policy) topics in this Development Agenda are relevant for this project: a) Social progress, social justice and inclusion. Despite the progress made in the last half century, a majority of the population is living in poor circumstances in India. The institutional factors responsible for this status (culture, religion, politics, education and other) are beyond this project. The relationship between improved city logistics and social progress is in the scope of this project, however. An efficient city logistics system gives more people access to basic resources like food. Time spent to acquire goods and carry these manually could also be used for activities of a higher social and economic value. Reducing transport time helps to preserve the quality of the goods transported and helps to prevent waste. b) Reducing inequality between countries. Social and economic development could help countries that lag behind to raise their level to that of more developed countries. Efficient city logistics is a means to achieve this aim. In a country where the population is relatively young and rapidly growing, the necessary pace of growth is very high. In an open economy, the direction and pace of development are also dependent on factors beyond the scope of India’s policy makers. c) Sustainable development. The tension between social/economic development and environmental/ecological concerns has already been mentioned in the previous section. The World Bank Group Page | 15 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Importance of sustainable transport A transport system is sustainable if “it contributes to economic growth and social equity without systematically increasing concentration of substances in the atmosphere and degrading nature.” [5, page 697]. Urban freight transport movements are a subset of all transport movements in a city, both passenger and freight. To give an example, estimates from the European Union from the beginning of 2000 are as follows. UFT had a share of 18% in terms of kilo meters driven in an average city. UFT was responsible for 31% of the energy use and 31% of the emissions in the year 2005 [7]. The share of UFT is thus disproportionate. Yet, the world is not the same as two decades ago. Logistics has changed considerably, both regionally (development of regional or city distribution centers) and well as at the street level (the infamous last mile). Internet ordering and home deliveries are on a steep growth path, partially replacing conventional shopping at retail stores. Technology has also changed in response to higher emission standards by the government (EU, national and local regulation) like environmental zoning and restricted access for delivery vehicles. Demands from business customers are also rising. Green transport has become a marketing tool as well. These demands and the requirement from transport operators to reduce operating costs (fuel and maintenance) have helped to increase energy efficiency of modern engines and to reduce emissions to the air to a very low level. For engines relying on conventional (fossil) fuels, Euro-6 is the current top tier. With fossil fuels, CO2-emissions remain a major problem. To deal with these, electrification of UFT is a likely route. In some cases combined transport might work as well. Noise abatement has also been successful. This allows delivery in mixed- use zones and during off-peak hours and nights. In developed countries, UFT frequently takes place with a very modern fleet of delivery vehicles. Modern logistic practices are commonplace. Both factors explain why several of these nuisances could be reduced substantially. EU countries are at the forefront of this development. Less developed countries face a much more complex situation. Probably many of the following statements hold: Operators may use outdated delivery vehicles. Emission standards may be lagging far behind the technology frontier, not available or hard to maintain. At the same time (freight) traffic is growing rapidly. Traffic regulation is frequently difficult. Logistic practices are very traditional and inefficient. Tailpipe emissions and noise emissions are probably high. Finally, the combined impact of traffic and other emission The World Bank Group Page | 16 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report sources, like (home) industries, agricultural practices etc. explains why cities like New Delhi, Beijing and Shanghai suffer from large-scale air pollution and smog. 4.2.3. CL Impacts on the Triple Bottom Line Economic impacts Goods delivery and pick-up can be regarded a micro-, meso- and macro-economic level. At the micro level, a city logistics operator intends to deliver ordered goods on time at the required location. Accessibility, average speed, reliability and delivery costs are thus important KPI’s in city logistics. By keeping logistic costs down, goods prices stay attractive for buyers. This stimulates demand for goods and services, which in turn stimulates investments and development of economic sectors (meso impact). This in turn contributes to economic development and to the tax income of the government (macro-economic impact). With a growing number of people living in cities, both passenger and freight transport will grow substantially. The city's government may spend part of the tax income on infrastructure in order to keep the economy on its growth path. However, if these investments cannot keep up with the growth in traffic, then traffic intensity rises. Then congestion starts or becomes worse. This makes it more difficult to deliver goods on time etc. A negative cycle may start. Economic development may slow-down. Companies and people may migrate to less congested areas. Congestion also leads to an increase of certain emissions (e.g. of CO). Social impacts City logistics has an important social impact as well, both positive and negative. A positive impact is that city logistics provides many jobs, either directly or indirectly. Having a job means a higher standard of living and more diverse spending opportunities (on education etc.). Another positive impact is that efficient city logistics allows people to consume a wider range of goods. The negative social impacts of city logistics refer to the externalities: air pollution, noise and safety hazards (citizens are hurt or killed on the road or as bystander). City logistics is also a source of visual intrusion, damage to buildings and infrastructure. All these impacts can be translated into monetary terms. Since not everyone is affected in a similar manner, there is also an aspect of social imbalance (equity) involved. The World Bank Group Page | 17 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Environmental impacts City logistics takes place with motorized and non-motorized vehicles and equipment. In most cases roads are used to transport the goods, but there are also examples where inland waterways and railways are used to ship goods in and out of cities. A growing use of motorized transport vehicles leads to a growing use of (fossil) fuels. This has many negative environmental impacts. One of these is the depletion of natural resources. The emission of CO2 contributes to climate change. Mining and production of conventional vehicle fuels damages and pollutes the environment as well. Air pollution with NOx, CO, PM10 and SO2 is also a well-known side effect of the use of (fossil) fuels in engines. Particles smaller then PM10 result from the wear of tires and brakes. There are also other environment effects, for instance a dissection of the (rural) landscape, loss of green areas, etc. Contamination of land with toxic or other hazardous materials may occur during production, maintenance and use of vehicles (e.g., loss of fluids, wear of brake pads). It may also occur during construction, maintenance and use (e.g. run-offs) of infrastructure. Finally, there is waste produced during the lifecycle of vehicles and infrastructure. 4.3. Additional Contextual Factors for Delhi 4.3.1. Policy on Urban Development and City Logistics India’s population is growing rapidly. New Delhi is India’s second largest metropolis, after Mumbai. Delhi’s population was 9.4 million in 1991. It is already over 19 million people at the beginning of 2017 and expected to rise towards 25 million (40%) by 2020 [9]. This population growth can be explained by a combination of factors. Two of them stand out in our opinion. First, a growing number of people want to live in urban areas, because they offer better living conditions, education facilities, (better) jobs, and better transport networks than non-urban areas. Migration from other states within India is the major reason for the strong population growth of New Delhi. Every year 200,000-300,000 people become a The World Bank Group Page | 18 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report permanent resident of New Delhi. Metropolitan areas like New Delhi also attract many temporary visitors, most notably tourists and business people. Second, the Government of National Capital Territory of Delhi (GNCTD) has improved the road and rail infrastructure, education facilities etc., which improve the economic structure and -climate in the city. More jobs go hand in hand with more passenger and freight transport and -traffic in and around the city. India’s Central Government considers city logistics as an important enabler of urban development. It has to achieve objectives in the following six areas [8; page. 57]:  Efficiency objectives: high quality of transport services at a low cost.  Economic objectives: provide income, mitigate prices, give India’s companies a certain market share and create business opportunities.  Road safety objectives: minimize or reduce the number and severity of traffic accidents.  Environmental objectives: reduce air pollution, noise, risk, physical hindrance and vibration, manage use of space and reduce the contribution to climate change.  Infrastructure objectives: mitigate cost of construction and maintenance of infrastructure.  Urban structure objectives: Protect urban heritage in and outside cities. 4.3.2. Transport Vehicles, Air Quality and Air Pollution Abatement The number of registered vehicles more than tripled from 2.2 million to 7.6 million between 1994 and 2016 [10] in New Delhi (details given in Appendix 1). A growing number of people, activities and more traffic is a recipe for an increase in externalities, unless mitigating policies are put in place. Air pollution is a very serious problem in New Delhi and Transport is attributed as a major cause (most recent pollution data given in Appendix 2). The GNCTD has implemented instruments to curb the problem of air pollution [11]:  Public awareness campaigns;  Prescription of catalytic converters;  Enlargement of mass rapid transport systems using electricity or CNG; The World Bank Group Page | 19 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report  Phasing out of old (> 15 years) commercial vehicles. This relates to autos and taxi’s driven on conventional fuels and buses driven on Diesel. Fiscal incentives (subsidy arrangement) are used to stimulate this phasing out;  Introduction of emission standards for new vehicles; First introduced in 1991, tightened in 1996 and 2000. Current standard is Bharat Stage IV/Euro IV for 4 or more wheelers in Delhi and other major metropolitan cities. In contrast, the European Union prescribes Euro 6 for new vehicles;  A complete phase-out of leaded petrol  Introduction of Low Sulphur Diesel and Low Benzene Petrol;  Broadening of the emissions test procedure (with CO, HC, oil temperature & RPM measurement for Diesel vehicles);  Instalment of improved emission measurement equipment at vehicle maintenance centers 4.3.3. Developing Country Challenges There are a few important challenges to consider that are typical to developing countries. First, there is the problem of non-compliance due to the high share of the informal sector in the local economy and cultural practices. Another factor is the high cost of replacement of vehicles, etc. Second, many measures have been introduced quite late or in moderate form compared to Europe or the USA, where air pollution has never been at the level of New Delhi. Third, the growing numbers of vehicles and kilometers driven have counteracted the achieved reduction in air pollution per average vehicle. Fourth, emissions are measured at a very small number of measurement points and not for an extensive period of time, hence the collected emission data are not representative for the whole city or a whole year. Still, the available data indicate that emissions of PM2.5, which is a critical parameter for the health impact of air pollution, are 100-300 mg per m3 at three monitoring stations. This translates to a ‘normal’ situation where the emission standard is exceeded by 4-10 (data Oct 2013- Jan 2014) [12]. Measuring performance is important and problematic at different levels. At the lowest level of interest, traffic data and in particular logistic data are scarce and not collected on a regular basis, mainly due to lack of regular funding [6]. This leads to a situation where available The World Bank Group Page | 20 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report public policies are based on scattered, incidentally available historic data, either locally collected or from other cities or even countries (in other parts of the world). This limits the practical value of such data. One may speak of ‘rules of thumb or expert guesses’. Generalization or extrapolation of such data is of limited use. 4.4. SCL – A Review of the Literature 4.4.1. Introduction In this section the results are presented on relevant methods and parameters used or suggested by different studies to determine the performance of city logistics in (comparable) other cities around the world. Three sub-questions were used to structure this section: 1. What can be learned from other researchers working in the area of city logistics about measurement, estimation and analysis of logistics practices in other cities all over the world? 2. What were the pros and cons of their approaches? 3. Can these approaches (with some amendment) also be applied to New Delhi? The literature review has been carried out keeping above questions in mind and is accordingly discussed in the next sections. 4.4.2. Literature on Criteria, Indicators and Frameworks Nicolas, et. al. [16] carried out a study for Renault in the city of Lyon, the second largest city in France, to compare different transport strategies within an urban area; between different urban contexts and through time. Goods and non-daily trips account for 45% (51% in rush hours) of total vehicle * kilometers in Lyon city. They distinguished three dimensions of sustainability and discussed feasibility and usefulness of indicators. With respect to data about emissions in a city, the authors mention that these come from multiple sources; hence no general (city) data can be used. In order to find the mobility-related emissions, it is necessary to concentrate on trip-related data. It is important to realize that O-D data don’t give detailed information about the itinerary. This means that a traffic assignment model is needed to provide that level of detail. A second study in this area was by Jeon, et. al. [17]. The World Bank Group Page | 21 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report This policy-oriented study compares sustainability policies in many states of the USA. It contains definitions, a framework and indicators. These are at a very high-level, not operational. Haghshenas, et. al. [18] provides a comparison of “world cities” by means of indicators. The intent is to deal with transport in general. The authors define composite indices and use a Z-score to normalize the indicators (weights). They distinguish four sustainability indicators: ITE = Environmental composite index, ITC = Economical composite index, ITS = Social composite index, IOST = Overall sustainable transport composite index. The data collected allowed a comparison of cities worldwide. Meers, et. al. [19] distinguish between 85 indicators for passenger and freight transport in Deinze (Belgium). Regarding freight transport, the authors mention modal split, vehicle type, efficiency, fleet, energy consumption, emission standards and emissions (PM10, PM2.5, NOx, and GHG), safety. Guerlain, et. al. [20] describe the use of a GIS to model complex urban freight situations including last mile deliveries. The tool has been used in several workshops in European cities. Data was collected about population, economy, freight transport networks, access restriction, transport facilities, urban logistic spaces. Public GIS data was limited, hence the authors decided to complement this data with their own background research. The benefit of GIS system is that it allows layering and integration of multiple data sources. The results are shown in GIS maps. A nice example is a map that shows where weak spots in services exist, such as a lack of electric charging points or pick- up points. Toilier, et. al. [25] carried out a study about passenger transport movements and city logistics in the Paris region. The survey involved all the logistic companies (at 1200 establishments) and their activities in city logistics. A self-administered driver-deliverer survey was employed to identify the main LSP’s. Ducret, et. al. [26] in a similar application which evaluates and develops guidelines to improve UFT management [24]. Policy goals found in all these cases were attractiveness, sustainability and economic competitiveness. Ducret, et. al. [26] found 8 criteria to be critical:  Formalisation of freight policy;  Quantitative diagnosis;  Public-private consultation or partnership;  Political support and commitment;  Traffic and parking regulations and the efficiency of the control system;  Urban plannning regulations; The World Bank Group Page | 22 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report  Delivery bays (number, location, design);  Human and financial resources are allocated in the policy. Zheng, et. al. [21] propose guidelines for the development of a composite index called TISP (Transport Index for Sustainable Places) for the USA. The paper discusses the way to select indicators, requirements and construction of a composite index, explore existing rating systems. A key issue is that sustainability is difficult to operationalize due to conflicting goals. A key issue with the index is how to weigh the components (equal, MCA-AHP?). This depends on the policy goals and objectives. The work of Ambrosini, et. al. [22] is an international comparison of UFT studies and surveys. Despite logical differences in approaches and methods, the authors see similar economic and environmental trends emerging worldwide. It is important to realize that although UFT problems are rather similar between countries, the definition of UGM differs per country. This is related with the way city logistics is treated in policy-making. There is also a difference in the local versus regional dimension of problems and solutions. Their exploration of various countries is about impact of policies, without quantification. Zenezini, et. al. [23] discuss UFT initiatives and the barriers that pilot projects frequently fail to pass before they can expand to a scale that allows optimization of UFT activities. The paper contains a classification of existing assessment methodologies based on scope and methods. The paper discusses pros and cons of these methodologies. It contains definitions, research gaps, methods and future trends. According to the authors, UFT is about measures (public and private), stakeholders (shippers, receives, carriers, citizens, authorities) and impact area (economical, social and operational). They mention that the 4-step modelling approach overlooks the tour-based nature of urban commercial traffic (multi-stop trips). Analysis is time and data intensive. They suggest aggregation and probabilistic methods using a limited percentage of the tours. The paper continues with a discussion of agent-based methods: agents interact; they have different with objectives and decision-making attributes. Now the analysis is about changing patterns. This requires significantly less data for simulation, but the interactions have to be modelled. Bader [24] contains an extensive overview of tools developed worldwide by and for sustainable urban transport experts. Although mainly devoted to non-freight movements and The World Bank Group Page | 23 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report impacts it makes a reference to a Green Trucks Toolkit by Clean Air Alaska (see also [7]). The environmental toolkits, or at least their criteria, could also be used for freight transport. 4.4.3. Discussion The development of a framework with consistent criteria and indicators is a basis to compare local UFT policies and to carry out an overall assessment of such policies. This is a departure from the past approaches, which were fragmented/ partial, while non-comparable criteria and indicators were used. With the new indicators cities can evaluate their own policies, compare their (planned) policies with those by other cities and improve the efficiency of their own local policies. The main users of the framework will likely be local engineers and transport specialists, policy auditors, freight transport operators. The research methods included case studies and stakeholder analysis. The use of a limited list of criteria improves time- and resource efficiency. The current practice of very different local policies and instruments, lack of harmonization and consistency affects UFT operators negatively. The urban logistic function should be clearly identified in the technical departments of the government; technical experts should act as contact point for stakeholders. In this way the government becomes aware of their needs. The recent initiative by the Indian government to initiate a Sustainable Urban Transport Project (GEF-SUTP) headed by the Ministry of Urban Development [27] is also a step in that direction. This document deals with passenger transport, but part of the data collected is also relevant for UFT, in particular the city characteristics (demography, traffic and travel characteristics, travel demand forecast, road network characteristics, stakeholder analysis). 4.5. Proposed Metrics for SCL Taking into account our findings from the inventory of the literature and with the aim to arrive at a reasonably complete but manageable list of indicators to measure New Delhi’s performance in the area of SCL, we arrived at the list of indicators has been arrived as presented in Table 4.1 which also included suggested units and sources for measurement. The survey has been proposed to conduct among all the local authorities and policy makers, freight operators and experts through a predesigned questionnaire are given in Appendix 3. The World Bank Group Page | 24 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Table 4.1: List of Indicators to Measure New Delhi’s Performance in the area of SCL Category Indicator Parameter [unit] Notes on Data and Sources Economic - Level of road Road infrastructure GIS/Statistics: Road and lane length General infrastructure density [lane-kms/km2] [km] transport GIS/Statistics: City area [km2] system Source: urban planning agency, transport agency Availability of Number of intermodal GIS/Statistics: Counts of intermodal intermodal hubs according to type hubs within urban area from transport access (rail, inland waterway, transport agency sea, air) [#] Cost of congestion Average congestion Calculate using traffic model: delay [veh-hours/year] compare travel time of 10 kms during peak-hour and free flow Road quality Percentage of road Statistics: Road length undergoing undergoing maintenance maintenance [km] per year [%] Statistics: total road length [km] Financial health Transport budget [RP] Finance department Economic – Level of service for Mean speed of freight Survey: mean travel time for 10 km logistics service roads vehicles [km/h] trip quality Reliability of travel % trips deviating from Survey: Mean travel time deviation time mean travel time according to the deviation categories Costs of transport Mean costs (fixed and Survey: Total costs for delivery services/ Total running costs) per (using activity-based accounting costs of delivery deliveries approach and total cost of ownership) Survey: Total number of deliveries Accessibility for Percentage of accessible GIS/statistics: Vehicle access road freight vehicles roads per vehicle length category [%] GIS/statistics: Total road length Environmental Greenhouse gasses Well to wheels GHG Statistics/traffic model: Fuel used per emissions for UFT [ton- vehicle category CO2eq/year] Energy efficiency Total energy used Statistics/traffic model: Freight according to freight traffic volume [ton-kms] traffic volume [MJ/ton- Statistics/traffic model: Fuel used per km] vehicle category Air pollution Total emissions of air Statistics/traffic model: Fuel used per pollutants (PM10, NOx) vehicle category [kg/year] Urban area used for Percentage of urban area GIS/statistics: Area for direct (road, UFT used for UFT out of total rail, inland ports/water ways) city area indirect: parking, warehouse, loading bay, logistic center. GIS/statistics: Total city area Social Traffic safety Injuries and fatalities Traffic safety statistics involving freight vehicles [#] Jobs Employment in city Employment statistics logistics (#) The World Bank Group Page | 25 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report In the Table 4.1, the “total costs of delivery” under Economic - logistics service quality has been given which includes handling and storage costs as part of the activity based accounting mentioned in the rightmost cell. With the perspective of city logistics, storage costs are a part of total logistics costs. However, our present model and measurements are not directed at logistic optimization for shopkeepers. The focus of the present study is descriptive, i.e. to capture all the freight traffic in the city (not just retail) and assess its impacts on the environment. However, a next step could be to provide more logistical detail in order to extend the focus towards an explanatory model. This would need to include additional surveying of shipment sizes and frequencies, shop locations with land prices, etc. References [1] Behrends, S., Lindholm, M., Woxenius, J., 2008. The impact of urban freight transport: A definition of sustainability form an actor’s perspective, Transportation Planning and Technology, 31 (6), pp. 693-713. [2] World Commission on Environment and Development (WCED), Oxford UP, 1987. [3] Robinson, J., 2004. Squaring the circle? Some thoughts on the idea of sustainable development. Ecological Economics, 48, pp. 369-384. [4] UNDP, 2007. The United Nations Development Agenda, http://www.un.org/esa/ devagenda/UNDA1.pdf. [5] Merchán, D.E., Blanco, E.E. and Bateman, A.H., 2015. Urban metrics for urban logistics: Building an atlas for urban freight transport policy makers, paper presented at 14th International Conference on Computers in Urban Planning and Urban Management CUPUM 2015, July 7-10, 2015, Cambridge, MA USA. [6] Browne, M., Allen, J., Woodburn, A., Patier, D., Routhier, J. L., & Ambrosini, C. (2007, June). Comparison of urban freight data collection in European countries. In Proceedings of the 11th World Conference on Transport Research, Berkeley CA, USA. [7] Dablanc, L., 2009, Freight transport for development toolkit: Urban freight, World Bank TRS and DFID, report no. 57971, Washington DC. [8] Government of India, Ministry of Urban Development & UNDP, Urban Freight Transport Planning and Management, Development of Toolkits under the “Sustainable Urban Transport Project”, New Delhi, 2015. [9] Population of Delhi 2017, http://www.indiaonlinepages.com/population/delhi-population. html The World Bank Group Page | 26 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report [10] Govt. of NCT of Delhi, Vehicular pollution in Delhi, http://www.delhi.gov.in/wps/wcm/ connect/doit_transport/Transport/Home/Pollution+Control/Vehicular+Pollution+in+Del hi. [11] Govt. of NCT of Delhi, http://www.delhi.gov.in/wps/wcm/connect/doit_transport/ Transport/Home/Pollution+Control/Steps+Taken+by+Delhi+Govt.+to+reduce+the+Pol lution+in+Delhi. [12] Delhi needs policy for air pollution, Times of India, Feb 6, 2014. http://www.hindustan times.com/delhi-news/air-pollution-new-epca-plan-to-cut-pm2-5-levels-in-delhi-by- 70/story-m43bCoJ12N7RYnhmeGPn2I.html [13] Behrends, S., 2011, Urban freight transport sustainability – The interaction of urban freight transport and intermodal transport, PhD Chalmers University of Technology, Gothenburg, Sweden. [14] Lammgard, C., Hagberg, J., 2013, Designing for sustainable logistics in urban areas – What do we know?, Proc. 13th World Conference of Transport Research (WCTR), July 15-18, 2013, Rio de Janeiro, Brazil. [15] Kin, B., Verlinde, S., Macharis, C., 2017, Sustainable urban freight transport in megacities in emerging markets, Sustainable Cities and Society, 32, pp. 31-41. [16] Nicolas, J.-P., Pochet, O., Poimboeuf, H., 2003, towards sustainable mobility indicators: application to the Lyons conurbation, Transport Policy, 10, pp. 197-208. [17] Jeon, C.M., Amekudzi, A., 2005, Addressing sustainability in transportation systems: Definitions, indicators, and metrics, J. Infrastructure Systems, 11(1), pp. 31-50. [18] Haghshenas, H., Vaziri, M., 2012, Urban sustainable transportation indicators for global comparison, Ecological Indicators 15 (2012), 115-121. [19] Meers, D., Hermans, E., Buldeo Rai., H., Lier, T. van, Shen, Y., Macharis, C., 2016, Using indicators to disentangle the transport-related sustainability of a city, paper presented at Vervoerslogistieke Werkdagen (congress), Mechelen, 2016. [20] Guerlain, C., Cortina, S., Renault, S., 2015, Towards a collaborative geographical information system to support collective decision making for urban logistics initiative, Transportation Research Procedia, 12 (2016), pp. 634-643. [21] Zheng, J., Garrick, N.W., Atkinson-Palombo, C., McCahill, C., Marshall, W., 2013, Guidelines on developing performance metrics for evaluating transportation sustainability, Research in Transportation Business & Management, 7 (2013), pp. 4-13. [22] Ambrosini, C., Routhier, J.L., 2004, Objectives, methods and results of surveys carried out in the field of urban freight transport: An international comparison, Transport Reviews, January 2004, 24 (1), pp. 57-77. The World Bank Group Page | 27 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report [23] Zenezini, G., De Marco, A., 2016, A review of methodologies to assess urban freight initiatives, IFAC-Papers On Line 49-12 (2016), 1359-1364. [24] Bader, N., 2014, Tools for sustainable urban transport experts, GIZ Deutsche Gesellschaft für Internationale Zusammenarbeid (GIZ) GmbH, Eschborn, Germany. [25] Toilier, F., Serouge, M., Routhier, J.-L., Patier, D., Gardrat, M., 2016, How can urban goods movements be surveyed in a megacity? The case of the Paris region, Transportation Research Procedia, 12 (2016), 570-583. [26] Ducret, R., Diziain, D., Plantier, Th., 2016, Proposal for an evaluation grid for analysing local public urban freight policies: strengths, weaknesses and opportunities for French cities, Transportation Research Procedia, 12 (2016) 105-118. [27] Delhi IMTS Ltd., TRL UK, DINTS, Kimley-Horn Consulting & Engineering India Pvt. Ltd., 2016, Delhi National Urban Transport Helpline (NUTH), Operations Document for the Ministry of Urban Development. [28] Rogers, P., Karolin Kokaz, 2000, Transportation and Environment: Problems in Delhi and Beijing. www.seas.harvard.edu/TransportAsia/Presentations/UCEsept28.pdf. [29] Indian Institute of Ecology and Environment (IIEE), Environmental policy and institutional provisions in India, www.ecology.edu.pdf. [30] Anand, N. (2015). An Agent Based Modelling Approach for Multi-Stakeholder Analysis of City Logistics Solutions. PhD Thesis Delft University of Technology, Delft: TRAIL Research School. The World Bank Group Page | 28 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report 5. LOGISTIC METRICS FOR CITY OF DELHI 5.1. General As mentioned in the previous sections, one of the objectives is to develop logistics metrics for city of Delhi and also develop a freight travel demand model. In that direction, the first and foremost task is to collect the necessary data and a database needed to be created by collecting freight travel behaviour data, road network, economic data etc. For this purpose, a number of traffic surveys have been proposed to be carried out. The details of the field studies carried out in the present study are explained in detail in the following section. 5.2. List of Field Surveys Keeping the objectives of the study in view, the following traffic surveys have been undertaken in the present study: 1. Outer Cordon Traffic Survey This survey would be to estimate the quantum of traffic entering or exiting city of Delhi and the share of freight traffic 2. Origin-Destination Survey at Outer Cordons This survey would be to collect the travel behaviour of freight traffic entering or exiting city of Delhi so as to develop OD matrix 3. Focal Point Survey at Commercial Areas/ Market Places This survey would be to collect the travel behaviour of freight traffic plying within Delhi so as to develop OD matrix 4. Journey Speed Survey on Arterial Road Network This survey would be to collect the journey speed which would be utilised in making road network and skim matrix for developing travel demand model for freight traffic 5. Mid Block Traffic Survey This survey would be to estimate the quantum of traffic and share of freight traffic on the road network city of Delhi The details of the above surveys and data collected have been described in the following sections. The World Bank Group Page | 29 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report 5.3. Speed Data In order to collect the prevailing speed on the road network on city of Delhi, the first step is to identify the road network where freight vehicles usually ply on it. Accordingly this road network where freight traffic primarily plies has been identified and shown in Figure 5.1. This network has been divided into different stretches and the details have been given in the Table 5.1. From the Figure 5.1 and Table 5.1, it can be observed that the major arterial road network has been selected in terms of circular and radial roads and total selected road network is about 418 km. The speed survey has been carried out using VBOX equipment as shown in Figure 5.2. S-1 S-2 S-14 S-13 S-3 S-12 S-17 S-11 S-4 S-10 S-8 S-5 S-9 S-15 S-7 S-6 S-16 Source: Google Earth, 2017 Figure 5.1: Selected Arterial Roads for Journey Speed Data Collection The World Bank Group Page | 30 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Table 5.1: Selected Stretches for Journey Speed Data Collection S. Stretch Length Name of the Stretch No Code (Km) 1 S-1 Tees Hazari to Kundli 28.90 2 S-2 Prashant Vihar (Ring Road Crossing) to Ferozpur Bangar 20.57 3 S-3 GT Road Shahadara to Bahadurgarh (Tikri Border) 36.94 4 S-4 Dhansa (Najafgarh Road) to Rajeev Chowk (Connaught Place) 43.05 5 S-5 Rajokri Border (Delhi-Gurgoan Expressway) to Rajeev Chowk 18.10 6 S-6 Arjan Garh (Aya Nagar Border) to Aurobindo marg 19.46 7 S-7 Badarpur Border to Rajeev Chowk (Connaught Place) 17.78 8 S-8 Inner Ring Road (Ashram Chowk to Ashram Chowk) 47.81 9 S-9 Outer Ring Road (Kalindi Kunj Border to Dwarka Mor Metro Station) 33.76 10 S-10 GT Road Shahadara to INA (via Gazipur, DND, Barapulla Elevated Road) 20.65 11 S-11 Ghazipur to Nizamudding Bridge 7.90 12 S-12 Wazirabad Road to Mandoli Border 9.80 13 S-13 Loni Border to Old Delhi Railway Station (via Vikas Marg, Sadar Bazar) 21.10 14 S-14 Bhagpat Road to Chilla Border 18.80 15 S-15 Khanpur Junction to Kashmere gate 18.41 16 S-16 Badarpur Border to Lado Sarai 15.26 17 S-17 Outer Ring Road (Station Road to ISBT) 39.65 Total 417.94 Figure 5.2: VBOX Equipment used for Journey Speed Data Collection on Selected Corridors The VBOX equipment has been fixed in the subject vehicle (car) and travelled with traffic stream especially in the peak hours to know the prevailing journey speeds in those periods. The journey speed data of the traffic stream has been collected for the stretches that are shown in Table 5.1. The VBOX equipment records the position of subject vehicles in terms of latitude in each time interval and longitude and journey speed has been calculated from distance and time. The collected journey speed data has been analysed in the direction wise for different stretches is given in Appendix 4. The typical journey speed data for Inner Ring The World Bank Group Page | 31 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Road (Stretch S-8) for up and Down directions is given in Table 5.2 and 5.3 respectively. The Summary of average journey speeds and travel times of the entire selected road network is given in Table 5.4 and Figure 5.3 respectively. Table 5.2: Observed Journey Speed on Inner Ring Road (S-8) in Up Direction S. Avg. Speed Name of the Section Distance (Km) No (Km/Hr) 1 Maharani Bagh to Vikas Marg 3.83 29.26 2 Vikas Marg to Rose Garden 3.48 24.73 3 Ross Garden to Vijayghat 0.75 53.56 4 Vijayghat to Indira Gandhi IT 2.76 29.11 5 Indira Gandhi IT to Matkaf Metro Station 1.98 22.29 6 Matkaf Metro Station to Naya Azadpur 0.99 15.69 7 Naya Azadpur to Pitampura 6.61 20.95 8 Pitampura to Britania Chowk 2.11 53.48 9 Britania Chowk to Punjabi Bagh Chowk 3.60 14.01 10 Punjabi Bagh Chowk to Punjabi Bagh Bus Stop 0.64 12.03 11 Punjabi Bagh Bus Stop to Sardana Eye Institute 2.42 24.90 12 Sardana Eye Institute to Army Medical College 4.59 32.53 13 Army Medical College to Sardar Patel Marg 3.48 32.51 14 Sardar Patel Marg to Safdarjung 3.68 20.77 15 Safdarjung to AIIMS Metro 1.90 52.32 16 AIIMS Metro to Moolchand Metro Station 2.54 26.21 17 Moolchand Metro Station to Ashram 2.59 30.12 Total 47.95 29.09 Table 5.3: Observed Journey Speed on Inner Ring Road (S-8) in Down Direction S. Avg. Speed Name of the Section Distance (Km) No (Km/Hr) 1 Mathura Marg to Moolchand Metro Station 2.59 34.71 2 Moolchand to AIIMS Metro Station 2.54 15.56 3 AIIMS to Safdarjung Metro Station 1.90 40.96 4 Safdarjung to Sardar Patel Marg 3.68 35.52 5 Sardar Patel Marg to Army Medical School 3.48 43.72 6 Army Medical College to Sardana Eye Institute 4.59 50.07 7 Sardana Eye Institute to Punjabi Bagh Bus Stop 2.42 39.42 8 Punjabi Bagh Bus Stop to Punjabi Bagh Chowk 0.64 51.23 9 Punjabi Bagh Chowk to Britania Chowk 3.60 40.50 10 Britania Chowk to Pitampura 2.11 44.80 11 Pitampura to Naya Azad Pur 6.61 15.72 12 Naya Azadpur to Matkaf Metro Station 0.99 14.96 13 Matkaf Metro to Indra Gandhi I.T 1.98 24.56 14 Indra Gandhi I.T to Vijayghat 2.76 41.33 15 Vijayghat to Rose Garden 0.75 49.68 16 Rose Garden to Vikas Marg 3.40 36.38 17 Vikas Marg to Maharani Bhag 3.83 40.31 Total 47.87 36.43 The World Bank Group Page | 32 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Table 5.4: Summary of Observed Journey Speed on Selected Stretches S. Stretch Length Journey Speed (Kmph) Name of the Stretch UP DOWN No Code (Km) 1 S-1 Tees Hazari to Kundli 28.90 36.70 34.66 2 S-2 Prashant Vihar (Ring Road Crossing) to 20.57 25.39 28.12 Ferozpur Bangar 3 S-3 GT Road Shahadara to Bahadurgarh (Tikri 36.94 17.46 17.09 Border) 4 S-4 Dhansa (Najafgarh Road) to Rajeev Chowk 43.05 22.95 21.94 (Connaught Place) 5 S-5 Rajokri Border (Delhi-Gurgoan Expressway) to 18.10 39.87 36.99 Rajeev Chowk (Connaught Place) 6 S-6 Arjan Garh (Aya Nagar Border) to Aurobindo 19.46 24.14 23.15 Marg 7 S-7 Badarpur Border to Rajeev Chowk (Connaught 17.78 24.52 23.26 Place) 8 S-8 Inner Ring Road (Ashram Chowk to Ashram 47.81 29.09 36.43 Chowk) 9 S-9 Outer Ring Road (Kalindi Kunj Border to 33.76 30.16 32.7 Dwarka Mor Metro Station) 10 S-10 GT Road Shahadara to INA (via Gazipur, DND, 20.65 31.90 32.58 Barapulla Elevated Road) 11 S-11 Ghazipur to Nizamudding Bridge 7.90 34.58 38.62 12 S-12 Wazirabad Road to Mandoli Border 9.80 22.72 25.92 13 S-13 Loni Border to Old Delhi Railway Station (via 21.10 17.20 20.73 Vikas Marg, Sadar Bazar) 14 S-14 Bhagpat Road to Chilla Border 18.80 23.52 22.27 15 S-15 Khanpur Junction to Kashmere Gate 18.41 23.98 25.53 16 S-16 Badarpur Border to Lado Sarai 15.26 22.01 21.06 17 S-17 Outer Ring Road (Station Road to ISBT) 39.65 31.65 29.42 Total 417.94 26.93 27.67 Journey Time 4 UP 3.5 Journey Travel Time DOWN 3 (min/km) 2.5 2 1.5 1 0.5 0 S-1 S-2 S-3 S-4 S-5 S-6 S-7 S-8 S-9 S-10 S-11 S-12 S-13 S-14 S-15 S-16 S-17 Stretch Figure 5.3: Average Journey Time on Selected Stretches of Delhi Road Network The World Bank Group Page | 33 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report From the Table 5.4, it can be observed that the journey speed is varying between 17 and 40 kmph and average journey speeds are around 27 kmph. From the Figure 5.3, the journey times are around 2.3 minutes per km which shows that the road network of Delhi city is moderately congested all the time. 5.4. Outer Cordon (OC) Traffic Volume Data In order to achieve the envisaged objective of developing freight travel demand model, the travel behaviour data along with their vehicular data, trip data and commodity type data has to be collected. For this purpose, a questionnaire survey has been proposed to carry out to collect all the above mentioned parameters related to freight traffic. The significant amount of freight traffic enters and leaves daily city from different parts of the country, this traffic can be captured at the outer cordon locations. In the city of Delhi, there are more than 100 entry and exit locations from which the freight traffic can enter and exit from the adjoining states namely Utter Pradesh and Haryana. However, about 95% of the freight traffic enters/ exits through 10 outer cordon/ entry-exit locations and accordingly these locations have been selected for data collection purpose. The geographic locations of these points are shown in Figure 5.4. The details of these locations have been given in Table 5.5. To collect the freight traffic volume data that are entering into or exiting from city, manual method of enumeration has been adopted in this study. The enumerators have been given sufficient training and deployed in the field to perform manual count of all the vehicles types which are entering and exiting the outer cordon location. The survey has been carried out for 24 hour duration starting from 8:00 AM to 8:00 AM and typical view of the traffic volume count survey at outer cordon location is shown in Figure 5.5. The enumeration of the traffic has been done in every 15-minute and accordingly recorded in predesigned proforma as given in Appendix 5. The traffic volume enumeration of vehicles has been done for all the vehicle types in order to understand the share of freight traffic in that. The typical view of traffic volume count survey at various outer cordon locations that are shown in Figure 5.4. The vehicle types mainly considered include all private vehicles, public transport, intermediate public transport, freight vehicles and non-motorised transport vehicles. The vehicles types considered for Freight Transport are given in the Table 5.6. The World Bank Group Page | 34 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report OC-5 OC-6 OC-4 OC-7 OC-8 OC-9 OC-10 OC-3 OC-1 OC-2 Figure 5.4: Selected Locations of Outer Cordons for Freight Traffic Data Collection Table 5.5: Selected Outer Cordon Locations for Freight Traffic Data Collection for 24- Hour Duration S. OC Name of the Outer Cordon Location Date of Survey No Code 1 OC-1 Badarpur Border (NH -2) 10.7.2017 2 OC -2 Aya Nagar Border (Arjan Garh on Mehrauli - Gurgaon Road) 18.7.2017 3 OC -3 Rajokri Border (Delhi-Gurgoan Expressway NH-8) 26.7.2017 4 OC -4 Tikri Border (NH-10) 19.7.2017 5 OC -5 Singhu Border (NH-1) 04.7.2017 6 OC -6 Loni Border 24.7.2017 7 OC -7 Apsara Border (G.T. Road at Shahadara) 25.7.2017 8 OC -8 NH-24 Bypass (Ghazipur) 14.7.2017 9 OC -9 Chilla Border (Mayur Vihar - Noida Link Road) 12.7.2017 10 OC -10 Kalindi Kunj Border (Sarita Vihar - Noida Road) 11.7.2017 The World Bank Group Page | 35 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Figure 5.5: Typical Views of Traffic Volume Count Survey at Different Locations of Outer Cordons for Freight Traffic Data Collection The World Bank Group Page | 36 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Table 5.6: Selected Vehicles Types Considered under Freight Transport in the Present Study S. Freight/ Goods Typical Image No Vehicle Type Goods Auto 1 Rickshaw (GA) 2 Goods Van (GV) Light Commercial 3 Truck (LT) Tanker Heavy 4 Commercial Truck (HT) Multi-Axle 5 Commercial Truck (MT) The collected traffic data has been analysed and the location wise hourly vehicular traffic distribution and traffic composition is given in Appendix 6. A typical traffic volume for 24 hour duration at Rajokri Border (NH-8) on Delhi-Gurgoan Expressway is shown in Table 5.7. The World Bank Group Page | 37 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report The hourly distribution of traffic volume and traffic composition at Rajokri Border (NH-8) on Delhi-Gurgoan Expressway is shown in Figure 5.6. From the Table 5.7, it can be observed that the total daily volume (24 hours) entering and exiting Delhi through Rajokri Border is in the order of 354 thousands and the peak hour is occurring in the evening between 18:00 and 19:00 Hrs with a peak volume of about 24 thousands. From the Figure 5.6, it can be inferred that about 95% are consisting of private vehicles mainly cars and two wheelers. The freight transport is about 4% mainly consist of Goods Autos, LT, HT and MT. Table 5.7: Classified Traffic Volume at Rajokri Border Road Name: Delhi - Gurgaon Expressway Location: Rajokari Border Date: 26.07.2017 - 27.07.2017 Outer Cordon OC-03 G o o ds Light Two Axle Multi Axle C yc le Small Two A ut o / Cycle R ic k s ha ws Grand Time Big Cars Auto Buses Mini Bus G o o ds V a n Commercial Trucks Trucks a nd O t he r Total FMV Total SMV Percentage PCU Cars Wheeler (CYC) Total (GA V) Vihicles (LT) (HT) (MT) (C Y SM V) 08:00-09:00 10872 1462 40 152 64 3047 51 73 32 18 102 0 15811 102 15913 4.5% 15665 09:00-10:00 12043 2016 41 183 77 3840 71 86 12 6 81 0 18375 81 18456 5.2% 18030 10:00-11:00 10715 1916 48 154 54 3888 60 106 65 11 64 0 17017 64 17081 4.8% 16700 11:00-12:00 12291 1743 44 143 53 2877 166 215 157 32 26 0 17721 26 17747 5.0% 17979 12:00-13:00 11417 2062 29 94 23 3379 225 247 130 24 6 0 17630 6 17636 5.0% 17585 13:00-14:00 14797 2053 48 99 9 2692 284 313 118 67 1 0 20480 1 20481 5.8% 20793 14:00-15:00 11540 2289 66 103 12 3197 329 321 178 74 30 0 18109 30 18139 5.1% 18496 15:00-16:00 11611 2446 38 137 18 4417 318 241 122 63 7 0 19411 7 19418 5.5% 19354 16:00-17:00 14868 2543 39 126 49 3166 262 215 149 27 9 0 21444 9 21453 6.1% 21597 17:00-18:00 14140 2428 28 293 98 4646 117 93 32 8 13 0 21883 13 21896 6.2% 21615 18:00-19:00 15354 2236 20 234 86 5763 42 39 15 0 23 0 23789 23 23812 6.7% 22988 19:00-20:00 11456 2328 22 145 43 8085 52 41 10 6 29 0 22188 29 22217 6.3% 20606 20:00-21:00 12696 2489 23 102 17 4016 74 59 9 15 73 0 19500 73 19573 5.5% 18895 21:00-22:00 10473 1945 26 73 4 2979 75 117 25 24 93 0 15741 93 15834 4.5% 15428 22:00-23:00 9291 1652 18 37 2 1705 93 202 106 185 128 0 13291 128 13419 3.8% 14015 23:00-24:00 6520 1470 2 31 7 693 125 240 347 428 76 0 9863 76 9939 2.8% 12172 00:00-01:00 4742 1540 9 39 2 611 93 259 381 478 53 0 8154 53 8207 2.3% 10721 01:00-02:00 3096 1243 13 24 2 504 62 296 333 385 29 0 5958 29 5987 1.7% 8092 02:00-03:00 2985 1315 28 13 7 353 87 282 327 385 4 0 5782 4 5786 1.6% 7920 03:00-04:00 1601 834 6 25 18 362 67 270 254 299 0 0 3736 0 3736 1.1% 5438 04:00-05:00 2435 1387 6 76 12 597 52 222 326 233 8 0 5346 8 5354 1.5% 6970 05:00-06:00 5009 1892 14 73 12 615 34 225 303 143 52 0 8320 52 8372 2.4% 9589 06:00-07:00 7205 2191 18 96 33 862 55 123 151 45 159 0 10779 159 10938 3.1% 11420 07:00-08:00 7957 2977 16 147 52 1059 44 84 34 10 252 0 12380 252 12632 3.6% 12757 Total 225114 46457 642 2599 754 63353 2838 4369 3616 2966 1318 0 352708 1318 354026 100.0% 364826 Percentage 63.6% 13.1% 0.2% 0.7% 0.2% 17.9% 0.8% 1.2% 1.0% 0.8% 0.4% 0.0% 99.6% 0.4% 100.0% Peak Volume= 23812 Peak Time= 18:00-19:00 Figure 5.6: Hourly Distribution of Classified Traffic Volume and Traffic Composition at Rajokri Border The summary of all the outer cordon locations is presented in Table 5.8 and traffic composition is presented in Figure 5.7. From the Table 5.8, it can be observed that maximum number of vehicles in the order of about 354 thousands entering and exiting through Rajokri Border followed by Ghazipur Border with an entry/ exit volume of about 163 thousands and The World Bank Group Page | 38 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Kalindi Kunj Border with an entry/ exit volume of about 126 thousands. From the Figure 5.7, it can be inferred that about 85% are consisting of private vehicles mainly cars and two wheelers. The freight transport is about 8% mainly consist of Goods Autos, LT, HT and MT. Table 5.8: Summary of Classified Traffic Volume (24 hours) at Different Outer Cordons of Delhi G o o ds Light Two Axle Multi Axle C yc le Small Two A ut o / Cycle R ic k s ha ws Grand S. No Outer Cordon Big Cars Auto Buses Mini Bus G o o ds V a n Commercial Trucks Trucks a nd O t he r Cars Wheeler (CYC) Total (GA V) Trucks (LT) (HT) (MT) (C Y SM V) 31304 16216 15825 1922 763 26380 3753 3754 5369 6221 4123 2910 118540 1 Badarpur Border 36.4% 14.8% 4.2% 1.5% 0.6% 28.5% 2.8% 2.8% 3.2% 3.3% 1.5% 0.4% 100.0% Arjun Garh 39688 15668 78 359 219 24403 357 163 128 240 718 216 82237 2 (Ayanagar Border) 48.3% 19.1% 0.1% 0.4% 0.3% 29.7% 0.4% 0.2% 0.2% 0.3% 0.9% 0.3% 100.0% 225114 46457 642 2599 754 63353 2838 4369 3616 2966 1318 0 354026 3 Rajokari Border 63.6% 13.1% 0.2% 0.7% 0.2% 17.9% 0.8% 1.2% 1.0% 0.8% 0.4% 0.0% 100.0% 20474 5387 2281 974 127 17657 1542 2176 1190 1058 1811 451 55128 4 Tikri Border 37.1% 9.8% 4.1% 1.8% 0.2% 32.0% 2.8% 3.9% 2.2% 1.9% 3.3% 0.8% 100.0% 25756 19237 1487 1007 525 13829 3304 3085 2812 3319 773 927 76061 5 Singhu Border 33.9% 25.3% 2.0% 1.3% 0.7% 18.2% 4.3% 4.1% 3.7% 4.4% 1.0% 1.2% 100.0% 7421 1397 21370 375 183 32100 254 664 555 123 4666 2456 71564 6 Loni Border 10.4% 2.0% 29.9% 0.5% 0.3% 44.9% 0.4% 0.9% 0.8% 0.2% 6.5% 3.4% 100.0% Apsara Border 34151 4553 4880 2961 247 30116 1603 1551 1185 625 1155 1817 84844 7 (Dilshad Garden) 40.3% 5.4% 5.8% 3.5% 0.3% 35.5% 1.9% 1.8% 1.4% 0.7% 1.4% 2.1% 100.0% 51534 34695 9567 3390 806 35486 6688 2474 5932 5388 4255 3004 163219 8 Ghazipur Border 31.6% 21.3% 5.9% 2.1% 0.5% 21.7% 4.1% 1.5% 3.6% 3.3% 2.6% 1.8% 100.0% 50187 15923 3425 1699 858 24088 1803 1679 1723 708 96 144 102333 9 Chilla Boarder 49.0% 15.6% 3.3% 1.7% 0.8% 23.5% 1.8% 1.6% 1.7% 0.7% 0.1% 0.1% 100.0% 45808 18596 5237 1891 743 35957 3589 3539 4088 4153 1828 530 125959 10 Kalindi Kunj Border 36.4% 14.8% 4.2% 1.5% 0.6% 28.5% 2.8% 2.8% 3.2% 3.3% 1.5% 0.4% 100.0% Light Commercial Two Axle Multi Axle Trucks (LT) Trucks (HT) Trucks (MT) Cycle (CYC) 2% 2% 2% 2% Cycle Goods Auto/ Rickshaws Goods Van and Other (GAV) (CY SMV) 2% 1% Two Wheeler 25% Mini Bus 0.4% Small Cars Buses Auto Big Cars 43% 1% 5% 15% Figure 5.7: Traffic Composition at Different Outer Cordons of Delhi The World Bank Group Page | 39 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report 5.5. Outer Cordon Interview Data Apart from the classified traffic volume count that has been conducted at these 10 outer cordon locations, interview survey also carried out to collect the travel behaviour of the freight vehicles that are entering and exiting Delhi through these locations. The survey has been carried out using predesigned questionnaire comprising the questions related to vehicle data, trip data and commodity data. The questionnaire utilised for this survey is given in Appendix 7. The questionnaire survey has been carried out for 24-hour duration and collected data from the freight vehicles on sample basis. Typical views of interview survey at various outer cordon locations are shown in Figure 5.8. The sample size collected at different outer cordon locations are given in Table 5.9. Table 5.9: Sample Size of Freight Vehicles Collected at Different Outer Cordons of Delhi (24 hours) S. OC Name of the Outer Cordon Sample Size of Freight Vehicles No Code Location Entering Delhi Exiting Delhi Total 1 OC-1 Badarpur Border 441 459 900 2 OC -2 Aya Nagar Border 285 130 415 3 OC -3 Rajokri Border 983 208 1191 4 OC -4 Tikri Border 946 54 1000 5 OC -5 Singhu Border 580 176 756 6 OC -6 Loni Border 342 422 764 7 OC -7 Apsara Border 830 170 1000 8 OC -8 Ghazipur 428 335 763 9 OC -9 Chilla Border 521 246 767 10 OC -10 Kalindi Kunj Border 482 353 835 Total 8391 From the Table 5.9, it can be seen that a total of 8,391 samples of freight vehicles were interviewed and collected the travel behaviour data. The data collected at the above outer cordon locations would be further analysed to understand the freight vehicular characteristics and travel behaviour of freight vehicles. The Origin and Destination (OD) data also analysed with respect to traffic analysis zones (TCZ) to assess the external travel and also to create data base primarily to estimate total freight trips and OD matrix to develop travel demand models namely trip generation and trip distribution models. The World Bank Group Page | 40 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Figure 5.8: Typical Views of Interview Survey at Different Locations of Outer Cordons for Freight Traffic Data Collection The World Bank Group Page | 41 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report 5.6. External Travel From analysis of the roadside interview data at the selected 10 outer cordon locations, overall pattern of external traffic in the city on a normal working day along with their composition was estimated and shown in Figure 5.9. The results reveal that a total of about 1.24 million vehicles enter and leave Delhi city on normal working day which was about 1.02 million vehicles in 2009 (CRRI, 2009). From this result, it can be observed that the external traffic has grown with 3% per annum. It can also be noticed that the goods traffic forms about 10% of the total traffic with another 4% of traffic is composed of slow moving vehicles like bicycle, animal carts etc. The pattern of external freight traffic in the city on a normal working day along with their composition was estimated and shown in Figure 5.10. The results reveal that a total of about 100 Thousands freight vehicles enter and leave Delhi city on normal working day and about 21% of these freight vehicles are found to be passing through the city which was almost same in 2009 (CRRI, 2009). Exit Entry 472,852 (in 2009) 550,841 (in 2009) Goods NMT Vehicles Goods NMT Bus 4% 11% Bus Vehicles 4% 2% 2% 10% Auto Auto 7% 7% Private Vehicles Private 76% Vehicles 77% Average Growth 3% per annum Figure 5.9: Pattern of Total External Traffic at Outer Cordons of Delhi The World Bank Group Page | 42 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Exit Entry GAV LCV GAV LCV 28% 23% 23% 24% HAV MAV MAV HAV 26% 23% 26% 27% Passing Through GAV LCV 14% 19% MAV 18% HAV 49% Figure 5.10: Pattern of Freight External Traffic at Outer Cordons of Delhi From these results, it can be observed that though the total traffic increased, freight traffic remain stagnated at outer cordons because of new bypass roads come around the city of Delhi such as Noida-Greater Noida Expressway, Yamuna Expressway, Kundli-Manesar-Palwal (KMP) Expressway etc. It can also be observed that the freight vehicle types namely Goods Auto (GA), Goods Van (GV), Light Trucks (LT), Heavy Truck (HT) and Multi-Axle Truck (MT) are found at entry and exit locations of outer cordons. In case of passing through traffic, HT has almost 50% share followed by MT and LT has share of about 18% each. Smaller Goods Vehicles (GA and GV) has a share of about 14% of passing through traffic. This can be attributed to the fact that the heavy vehicles travel long distances compared to light and small vehicles. The World Bank Group Page | 43 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report 5.7. Focal Point Freight Traffic Survey The focal point survey has been proposed with an aim to collect travel behaviour from the freight traffic that is primarily plying within the city. The intra-city movements by various commercial vehicles can be captured in this survey. For this purpose, interview survey has been proposed to carry out at selected locations to collect the travel behaviour of the freight vehicles that are plying within Delhi. The survey would be carried out using predesigned questionnaire comprising the questions related to vehicle data, trip data and commodity data. The questionnaire utilised for this survey is given in Appendix 7. The questionnaire survey has been carried out for 24-hour duration and collected data from the freight vehicles on sample basis. The selected locations for this survey are given in Table 5.10. These locations have been selected considering market areas and shopping areas. The geographic locations of these points are shown in Figure 5.11. Table 5.10: Selected Locations to Conduct Focal Point Survey in Delhi (24 hours) S. FP Date of Name of the Focal Point Nature of Land Use/Business Activity No Code Survey 1 FP-01 Azadpur Sabzi Mandi Fruit & Vegetable 14.9.2017 2 FP-02 Okhla Sabzi Mandi Fruit & Vegetable 15.9.2017 3 FP-03 Arya Pura Sabzi Mandi Fruit & Vegetable 14.9.2017 4 FP-04 Ghanta Ghar Sabzi Mandi Fruit & Vegetable 12.9.2017 5 FP-05 Old Delhi Sabzi Mandi Fruit & Vegetable 16.9.2017 6 FP-06 Shahdara Sabzi Mandi Fruit & Vegetable 18.9.2017 7 FP-07 Mandawali Sabzi Mandi: Fruit & Vegetable 18.9.2017 8 FP-08 Shahdara Fruit & Vegetable, Food Grains, Fodder 13.9.2017 9 FP-09 Gazipur Fish & Poultry 19.9.2017 10 FP-10 Connaught Place Retail Shopping areas 18.9.2017 11 FP-11 Chandni Chowk, Retail/Whole Sale Shopping areas 14.9.2017 12 FP-12 Sarojini Nagar Retail/Whole Sale Shopping areas 19.9.2017 13 FP-13 Lajpat Nagar Retail/Whole Sale Shopping areas 15.9.2017 14 FP-14 Pitampura Retail/Whole Sale Shopping areas 14.9.2017 15 FP-15 Nehru Place Retail/Whole Sale Shopping areas 14.9.2017 16 FP-16 Gandhi Nagar Whole Sale Shopping areas 14.9.2017 17 FP-17 Rajouri Garden Retail/Whole Sale Shopping areas 19.9.2017 18 FP-18 Narela Food Grain 19.9.2017 19 FP-19 Najafgarh Food Grains 18.9.2017 20 FP-20 Keshopur Fruit & Vegetables 14.9.2017 Note: Subzi Mandi means Fruit and Vegatable Market The World Bank Group Page | 44 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report FP-18 FP-14 FP-01 FP-04 FP-07 FP-06 FP-20 FP-03 FP-16 FP-05 FP-08 FP-17 FP-10 FP-11 FP-19 FP-09 FP-12 FP-13 FP-02 FP-15 Figure 5.11: Selected Locations to Conduct Focal Point Survey in Delhi (24 hours) To collect the freight traffic volume data that are entering into or exiting selected focal point/ market area, manual method of enumeration has been adopted in this study. The enumerators have been given sufficient training and deployed in the field to perform manual count of all freight vehicles types which are entering and exiting the selected focal point location. The survey has been carried out for 24 hour duration starting from 8:00 AM to 8:00 AM. The enumeration of the traffic has been done in every 15-minute and accordingly recorded in predesigned proforma as given in Appendix 5. The traffic volume enumeration of vehicles has been done for all the freight vehicle types in order to understand the quantity of freight traffic in that area. The vehicles types considered for Freight Transport are given in the Table 5.6. The World Bank Group Page | 45 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report The collected freight traffic data has been analysed and the location wise hourly vehicular traffic distribution and traffic composition is given in Appendix 8. A typical traffic volume for 24 hour duration at Azadpur Sabzi Mandi is shown in Table 5.11. The hourly distribution of traffic volume and traffic composition at Azadpur Sabzi Mandi is shown in Figure 5.12. From the Table 5.11, it can be observed that the total daily volume (24 hours) entering and exiting Azadpur Sabzi Mandi is in the order of about 7 thousands and the peak hour is occurring in the midnight between 23:00 and 24:00 Hrs with a peak volume of about 575 freight vehicles. From the Figure 5.12, it can be inferred that about 26% are consisting of Goods Autos and Goods Vans, LT is about 23% and HT and MT are 21% each. It can also be observed that Slow Moving Vehicles (SMVs) are about 9%. Table 5.11: Classified Freight Traffic Volume at Azadpur Sabzi Mandi Location Name: Azadpur Sabzi Mandi Date: 14.09.2017 - 15.09.2017 Focal Point Code: FP-01 Light Two Axle Multi Axle Cycle Rickshaws Total Goods Goods E-Rickshaw Hand Cart Animal Cart Total FMV- Total SMV- Time Trucks Trucks Trucks and Other (CY Vehicles- Percentage PCU Van (GV) Auto (GA) (EA) Goods (HC) (AC) Goods Goods (LT) (HT) (MT) SMV) Goods Goods 08:00-09:00 9 20 4 6 12 3 41 0 15 54 56 110 1.6% 222 09:00-10:00 4 25 12 12 14 4 24 0 23 71 47 118 1.7% 236 10:00-11:00 13 32 9 22 16 5 39 0 38 97 77 174 2.6% 340 11:00-12:00 7 31 2 12 5 1 18 0 31 58 49 107 1.6% 199 12:00-13:00 14 17 0 12 4 0 14 0 17 47 31 78 1.2% 140 13:00-14:00 14 7 1 3 0 0 1 0 7 25 8 33 0.5% 51 14:00-15:00 2 15 2 3 2 0 0 0 7 24 7 31 0.5% 57 15:00-16:00 2 25 1 5 2 1 2 0 12 36 14 50 0.7% 96 16:00-17:00 4 13 3 2 0 0 2 0 8 22 10 32 0.5% 54 17:00-18:00 1 16 0 2 0 0 1 0 16 19 17 36 0.5% 63 18:00-19:00 3 7 1 2 0 0 2 0 8 13 10 23 0.3% 39 19:00-20:00 0 29 0 16 18 13 3 0 4 76 7 83 1.2% 215 20:00-21:00 0 137 14 131 108 140 8 0 0 530 8 538 7.9% 1523 21:00-22:00 0 135 2 128 107 125 8 0 0 497 8 505 7.4% 1428 22:00-23:00 0 148 0 145 127 88 25 0 0 508 25 533 7.9% 1413 23:00-24:00 0 108 0 138 138 139 52 0 0 523 52 575 8.5% 1636 00:00-01:00 0 112 0 112 116 115 37 0 0 455 37 492 7.3% 1388 01:00-02:00 0 90 0 132 95 118 16 0 0 435 16 451 6.7% 1292 02:00-03:00 0 99 0 95 105 95 24 0 0 394 24 418 6.2% 1179 03:00-04:00 0 121 0 137 110 107 18 0 0 475 18 493 7.3% 1364 04:00-05:00 0 140 0 132 103 119 17 0 0 494 17 511 7.5% 1423 05:00-06:00 0 133 0 115 122 113 37 0 0 483 37 520 7.7% 1445 06:00-07:00 6 86 0 82 89 93 14 0 0 356 14 370 5.5% 1057 07:00-08:00 3 99 0 127 115 137 17 0 0 481 17 498 7.3% 1451 Total 82 1645 51 1571 1408 1416 420 0 186 6173 606 6779 100% 18307 Percentage 1.2% 24.3% 0.8% 23.2% 20.8% 20.9% 6.2% 0.0% 2.7% 91.1% 8.9% 100.0% Peak Volume= 575 Peak Time= 23:00-24:00 Figure 5.12: Hourly Distribution of Classified Freight Traffic Volume and Traffic Composition Azadpur Sabzi Mandi The World Bank Group Page | 46 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report The summary of all the focal points is presented in Table 5.12 and traffic composition is presented in Figure 5.13. From the Table 5.12, it can be observed that maximum number of vehicles per day in the order of about 8 thousands entering and exiting through Ghanta Ghar Sabzi Mandi followed by Azadpur Sabzi Mandi with an entry/ exit volume of about 7 thousands and Chandini Chowk Area with an entry/ exit volume of about 5 thousands. From the Figure 5.13, it can be inferred that about 40% are consisting of Goods Auto and Goods Van. The vehicle types of LT, HT and MT are in the range of 24%, 11% and 8% respectively. The other freight vehicles are about 18%. Table 5.12: Summary of Classified Freight Traffic Volume (24 hours) at Different Focal Points of Delhi Two Multi Goods Goods Light Cycle E S. FP Axle Axle Hand Animal Name of the Focal Point Van Auto Truck Rickshaw Rickshaw Total No Code TruckTruck Cart Cart (GV) (GA) (LT) Goods Goods (HT) (MT) 1 FP-01 Azadpur Sabzi Mandi 82 1645 1571 1408 1416 420 186 51 6779 2 FP-02 Okhla Sabzi Mandi 92 177 270 193 92 1 67 4 896 3 FP-03 Arya Pura Sabzi Mandi 483 1838 926 42 73 105 55 402 3924 4 FP-04 Ghanta Ghar Sabzi Mandi 452 3587 2478 912 553 0 7982 5 FP-05 Old Delhi Sabzi Mandi 155 706 603 212 242 595 0 0 2513 6 FP-06 Shahdara Sabzi Mandi 19 899 536 575 148 646 2823 7 FP-07 Mandawali Sabzi Mandi 32 200 106 196 25 8 567 8 FP-08 Shahdara 64 309 223 50 16 439 1101 9 FP-09 Gazipur 82 190 436 112 116 0 17 129 1082 10 FP-10 Connaught Place 126 85 269 71 38 15 604 11 FP-11 Chandni Chowk 106 1062 248 119 28 1278 275 771 637 4524 12 FP-12 Sarojini Nagar 44 172 119 19 0 354 13 FP-13 Lajpat Nagar 246 204 195 68 45 190 948 14 FP-14 Pitampura 77 267 170 34 17 370 935 15 FP-15 Nehru Place* 197 304 39 13 0 29 582 16 FP-16 Gandhi Nagar* 215 599 462 119 68 22 1485 17 FP-17 Rajouri Garden 116 301 361 25 22 91 0 916 18 FP-18 Narela 49 248 301 165 243 0 0 37 1043 19 FP-19 Najafgarh 145 201 343 190 119 998 20 FP-20 Keshopur 61 780 564 190 126 1114 0 0 2835 * 12-Hour The World Bank Group Page | 47 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Cycle Rickshaw E Rickshaw Animal Cart Goods Goods Goods Van 1% 2% 3% 7% Hand Cart 12% Goods Auto Multi Axle 32% Trucks (MT) 8% Two Axle Truck (HT) Light 11% Commercial Vehicle (LT) 24% Figure 5.13: Freight Traffic Composition at Different Focal Points of Delhi 5.8. Focal Point Interview Data Apart from the classified traffic volume count that has been conducted at these 20 focal point locations, interview survey also carried out to collect the travel behaviour of the freight vehicles that are entering and exiting these market locations. The survey has been carried out using predesigned questionnaire comprising the questions related to vehicle data, trip data and commodity data. The questionnaire utilised for this survey is given in Appendix 7. The questionnaire survey has been carried out for 24-hour duration and collected data from the freight vehicles on sample basis. The sample size collected at different Focal Point locations are given in Table 5.13. From the Table 5.13, it can be seen that a total of 10,091 samples of freight vehicles were interviewed and collected the travel behaviour data. The data collected at these locations has been further analysed to understand the freight vehicular characteristics and travel behaviour of freight vehicles. The Origin and Destination (OD) data also analysed with respect to traffic analysis zones (TCZ) to assess the external travel and also to create data base primarily to estimate total freight trips and OD matrix to develop travel demand models namely trip generation and trip distribution models. The World Bank Group Page | 48 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Table 5.13: Selected Locations to Conduct Focal Point Survey in Delhi (24 hours) S. FP Sample Name of the Focal Point No Code Size 1 FP-01 Azadpur Sabzi Mandi 500 2 FP-02 Okhla Sabzi Mandi 650 3 FP-03 Arya Pura Sabzi Mandi 550 4 FP-04 Ghanta Ghar Sabzi Mandi 496 5 FP-05 Old Delhi Sabzi Mandi 858 6 FP-06 Shahdara Sabzi Mandi 468 7 FP-07 Mandawali Sabzi Mandi: 250 8 FP-08 Shahdara 398 9 FP-09 Gazipur 634 10 FP-10 Connaught Place 240 11 FP-11 Chandni Chowk, 506 12 FP-12 Sarojini Nagar 202 13 FP-13 Lajpat Nagar 402 14 FP-14 Pitampura 252 15 FP-15 Nehru Place 194 16 FP-16 Gandhi Nagar 1200 17 FP-17 Rajouri Garden 461 18 FP-18 Narela 650 19 FP-19 Najafgarh 650 20 FP-20 Keshopur 458 Total 10,091 5.9. Mid Block Traffic Volume Survey In order to assess the current traffic volume situation on the road network of Delhi, classified traffic volume count surveys at five locations has been proposed. In 2013, traffic volume studies were conducted by CSIR-CRRI at various intersections and mid-block section in Delhi. Accordingly the current traffic volume at other locations can be estimated from the determined traffic growth factors. To collect the freight traffic volume data that are plying on the selected locations in the city, manual method of enumeration has been adopted in this study. The enumerators have been given sufficient training and deployed in the field to perform manual count of all the vehicles types which are crossing that mid block location. The survey has been carried out for 24 hour duration starting from 8:00 AM to 8:00 AM. The enumeration of the traffic has been done in every 15-minute and accordingly recorded in The World Bank Group Page | 49 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report predesigned proforma as given in Appendix 5. The enumeration of vehicles has been done for all the vehicle types in order to understand the share of freight traffic in that. The vehicle types mainly considered include all private vehicles, public transport, intermediate public transport, freight vehicles and non-motorised transport vehicles. The selected locations for this survey are given in Table 5.14. Table 5.14: Selected Mid-Block (MB) Locations to Conduct Traffic Volume Survey in Delhi (24 hours) S. MB Name of the Mid-Block Location Date of Survey No Code 1 MB-1 Ring Road (Rajghat) 7.9.2017 2 MB -2 Connaught Place Outer Circle 11.9.2017 3 MB -3 Ring Road (Naraina) 7.9.2017 4 MB -4 I.T.O. Barrage Bridge 8.9.2017 5 MB -5 Nizamuddin Bridge 8.9.2017 The collected traffic data has been analysed and the location wise hourly vehicular traffic distribution and traffic composition is given in Appendix 9. A typical traffic volume for 24 hour duration at Ring Road (Rajghat) is shown in Table 5.14. The hourly distribution of traffic volume and traffic composition at Ring Road (Rajghat) is shown in Figure 5.14. From the Table 5.14, it can be observed that the total daily volume (24 hours) on Ring Road (Rajghat) is almost 190 thousands and the peak hour is occurring in the evening between 19:00 and 20:00 Hrs with a peak volume of about 16 thousands. From the Figure 5.14, it can be inferred that about 80% are consisting of private vehicles mainly cars and two wheelers. The freight transport is about 12% mainly consist of Goods Autos, LT, HT and MT. The summary of traffic on all the mid block locations is presented in Table 5.15 and traffic composition is presented in Figure 5.15. From the Figure 5.15, it can be inferred that about 80% are consisting of private vehicles mainly cars and two wheelers. The freight transport is about 7% mainly consist of Goods Autos, LT, HT and MT. The World Bank Group Page | 50 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Table 5.15: Classified Traffic Volume at Naraina on Ring Road Road Name: Ring Road Location: Naraina Date: 9/7/2017 Mid Block MB-03 Goods Light Two Axle Multi Axle Cycle Small Two Cycle Grand Time Big Cars Auto Buses Mini Bus Auto/Good Commercial Trucks Trucks Rickshaws Total FMV Total SMV Percentage PCU Cars Wheeler Vihicles (CYC) and Other Total s Va (GAV) (HT) (MT) (LT) (CY SMV) 08:00-09:00 3438 766 290 195 43 1434 201 59 91 17 244 13 6534 257 6791 3.6% 7180 09:00-10:00 4643 1038 336 168 22 3646 161 90 110 13 131 19 10227 150 10377 5.5% 10226 10:00-11:00 4898 1057 460 245 21 5523 221 120 129 11 71 16 12685 87 12772 6.8% 12434 11:00-12:00 4009 1094 544 216 4 4217 426 145 266 18 89 23 10939 112 11051 5.9% 11389 12:00-13:00 3956 928 582 204 7 3148 531 375 352 41 91 12 10124 103 10227 5.5% 11232 13:00-14:00 2447 1077 602 225 7 3139 498 366 268 33 59 8 8662 67 8729 4.7% 9580 14:00-15:00 2599 1378 619 224 8 3375 427 294 197 19 26 15 9140 41 9181 4.9% 9733 15:00-16:00 3161 1456 727 203 11 3242 527 347 259 18 20 14 9951 34 9985 5.3% 10752 16:00-17:00 2915 1296 673 209 11 3357 473 332 192 16 23 9 9474 32 9506 5.1% 10066 17:00-18:00 4596 1650 698 219 17 3950 302 164 54 8 78 17 11658 95 11753 6.3% 11699 18:00-19:00 7636 2229 761 195 45 3875 196 166 54 7 128 4 15164 132 15296 8.2% 15166 19:00-20:00 7531 2323 718 321 33 3845 330 264 65 20 74 5 15450 79 15529 8.3% 15849 20:00-21:00 6036 1153 773 299 51 2579 417 179 120 79 30 6 11686 36 11722 6.3% 12683 21:00-22:00 3101 872 378 167 17 2087 381 248 169 164 53 0 7584 53 7637 4.1% 8742 22:00-23:00 3227 619 202 96 22 1105 290 227 248 164 10 2 6200 12 6212 3.3% 7515 23:00-24:00 1130 655 96 37 12 604 276 418 406 478 3 1 4112 4 4116 2.2% 6901 00:00-01:00 1388 518 80 5 2 235 143 321 398 533 2 1 3623 3 3626 1.9% 6488 01:00-02:00 786 301 63 5 3 62 231 293 355 427 0 0 2526 0 2526 1.3% 5003 02:00-03:00 750 149 58 0 0 47 230 245 315 414 2 0 2208 2 2210 1.2% 4525 03:00-04:00 680 175 71 3 2 185 343 300 340 424 3 0 2523 3 2526 1.3% 4986 04:00-05:00 720 345 77 17 7 128 253 278 326 396 1 2 2547 3 2550 1.4% 4878 05:00-06:00 930 569 64 65 12 245 237 239 273 263 6 5 2897 11 2908 1.6% 4706 06:00-07:00 1335 674 102 155 41 654 329 345 257 121 17 10 4013 27 4040 2.2% 5519 07:00-08:00 2436 1103 261 171 50 1035 384 271 184 41 21 5 5936 26 5962 3.2% 6978 Total 74348 23425 9235 3644 448 51717 7807 6086 5428 3725 1182 187 185863 1369 187232 100.0% 214228 Percentage 39.7% 12.5% 4.9% 1.9% 0.2% 27.6% 4.2% 3.3% 2.9% 2.0% 0.6% 0.1% 99.3% 0.7% 100.0% Peak Volume= 15529 Peak Time= 19:00-20:00 Figure 5.14: Hourly Distribution of Classified Traffic Volume and Traffic Composition at Rajghat on Ring Road Table 5.16: Summary of Classified Traffic Volume (24 hours) at Different Mid Block Locations of Delhi G o o ds Light Two Axle Multi Axle C yc le Small Two A ut o / Cycle R ic k s ha ws Grand S. No Outer Cordon Big Cars Auto Buses Mini Bus G o o ds V a n Commercial Trucks Trucks a nd O t he r Cars Wheeler (CYC) Total (GA V) Trucks (LT) (HT) (MT) (C Y SM V) 39202 12656 25270 1268 268 49073 4586 1394 1178 1211 1313 889 138308 1 Ring Road (Rajghat) 28.3% 9.2% 18.3% 0.9% 0.2% 35.5% 3.3% 1.0% 0.9% 0.9% 0.9% 0.6% 100.0% Connaught Place 22388 6306 13350 4060 65 16424 227 130 69 14 519 84 63636 2 (Regal Cinema) 35.2% 9.9% 21.0% 6.4% 0.1% 25.8% 0.4% 0.2% 0.1% 0.0% 0.8% 0.1% 100.0% 74348 23425 9235 3644 448 51717 7807 6086 5428 3725 1182 187 187232 3 Ring Road (Naraina) 39.7% 12.5% 4.9% 1.9% 0.2% 27.6% 4.2% 3.3% 2.9% 2.0% 0.6% 0.1% 100.0% 80811 14944 15571 3725 150 53246 1457 1647 1536 417 1843 281 175628 4 ITO Bridge 46.0% 8.5% 8.9% 2.1% 0.1% 30.3% 0.8% 0.9% 0.9% 0.2% 1.0% 0.2% 100.0% 66586 15300 11265 3440 2295 29399 2792 4504 4324 3629 859 114 144507 5 NH-24 Bypass 46.1% 10.6% 7.8% 2.4% 1.6% 20.3% 1.9% 3.1% 3.0% 2.5% 0.6% 0.1% 100.0% The World Bank Group Page | 51 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Light Two Axle Commercial Multi Axle Trucks (HT) Trucks (MT) Trucks (LT) 2% Cycle (CYC) 2% 1% 1% Cycle Goods Auto/ Rickshaws Goods Van and Other (GAV) (CY SMV) 2% 0% Two Wheeler 28% Mini Bus Small Cars 0.5% 40% Buses Big Cars Auto 10% 2% 11% Figure 5.15: Traffic Composition at Different Mid Block Locations of Delhi 5.10. Freight Vehicular and Travel Characteristics 5.9.1. Age Distribution A total of 8391 freight vehicles at 10 outer cordon locations and 10,091 freight vehicles at 20 focal points (within city) were intercepted and interviewed. Through the roadside interviews, age of the vehicles were recorded along with other important travel characteristics and analysed for all the sampled vehicles. From the data of model (manufacturing) year of vehicle, the age of vehicle has been determined and age distribution is developed for different freight vehicle types at outer cordons and within city. Figure 5.16 and 5.17 present the distribution of vehicles as per the year of manufacture, as obtained at the outer cordon points and focal points. From the Figure 5.16 and 5.17, it can be found that the mean age of freight vehicles is almost same at outer cordons and within the city varying between 4.5 and 5.0 years and the share of 10 year and more old vehicles within the city is ranging from 1 to 6% and 5 to 9% at outer cordons as shown in Table 5.17. The World Bank Group Page | 52 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Goods Auto (GA) - CNG Goods Van (GV) - CNG 20 25 Percenage (%) Percenage (%) 15 20 Mean Age = 4.53 Years Mean Age = 4.72 Years 15 10 10 5 5 0 0 2017 2015 2013 2011 2009 2007 2005 2003 2001 1999 2017 2015 2013 2011 2009 2007 2005 2003 2001 1999 Year Year LT - Diesel HT - Diesel 20 20 Mean Age = 5.07 Years Percenage (%) Percenage (%) 15 Mean Age = 4.41 Years 15 10 10 5 5 0 0 2017 2015 2013 2011 2009 2007 2005 2003 2001 1999 2017 2015 2013 2011 2009 2007 2005 2003 2001 1999 Year Year MT - Diesel 20 Percenage (%) 15 Mean Age = 4.61 Years 10 5 0 2017 2015 2013 2011 2009 2007 2005 2003 2001 1999 Year Figure 5.16: Age Distribution of Different Freight Vehicles at Outer Cordons in Delhi The World Bank Group Page | 53 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Goods Auto (GA) - CNG Goods Van (GV) - CNG 20 25 Percenage (%) Percenage (%) 15 Mean Age = 4.99 Years 20 Mean Age = 4.41 Years 15 10 10 5 5 0 0 2007 2017 2015 2013 2011 2009 2005 2003 2001 1999 1997 2017 2015 2013 2011 2009 2007 2005 2003 2001 1999 1997 Year Year LT - Diesel HT - Diesel 20 20 Percenage (%) Percenage (%) 15 Mean Age = 4.69 Years 15 Mean Age = 4.52 Years 10 10 5 5 0 0 2007 2017 2015 2013 2011 2009 2005 2003 2001 1999 1997 2017 2015 2013 2011 2009 2007 2005 2003 2001 1999 1997 Year Year MT - Diesel 20 Mean Age = 4.87 Years Percenage (%) 15 10 5 0 2001 2017 2015 2013 2011 2009 2007 2005 2003 1999 1997 Year Figure 5.17: Age Distribution of Different Freight Vehicles within the City of Delhi (Focal Point) Table 5.17: Share of 10 Year and More Old Vehicles within the City and at Outer Cordons S. No Location Age LT HT MT GA GV 1 Outer 1 - 10 Years 94.3% 92.1% 94.3% 93.1% 92.1% 2 Cordons More Than 10 Years 5.7% 7.9% 5.7% 6.9% 7.9% 3 Within 1 - 10 Years 97.4% 97.6% 95.9% 93.8% 98.7% 4 the City More Than 10 Years 2.6% 2.4% 4.1% 6.2% 1.3% The World Bank Group Page | 54 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report 5.9.2. Fuel Used In case of freight vehicles, two types of fuels are mainly used. They are Diesel and Compressed Natural Gas (CNG). The fuel usage distribution of different freight vehicles at outer cordons and within the city is shown in Figure 5.18. From the Figure 5.18, it can be seen that Heavy Vehicles mostly use Diesel where as Goods Auto and Goods Van almost use CNG as fuel. In case of LT, about 45% and 75% use Diesel as fuel at outer cordons and within city respectively. Outer Cordons Within City 100% 100% 90% 90% 80% 80% 70% 70% 60% 60% 50% 50% 40% 40% 30% 30% 20% 20% 10% 10% 0% 0% LT HT Tanker MT GA GV LT HT MT GA GV Freight Vehicle Type Freight Vehicle Type CNG Diesel CNG Diesel Figure 5.18: Fuel Usage Age Distribution of Freight Vehicles at Outer Cordons and within the City of Delhi 5.9.2. Ownership of Freight Vehicle The ownership of different freight vehicles at outer cordons and within the city has been analysed and shown in Figure 5.19. From the Figure5.19, it can be seen that private company vehicles are high in case of heavy vehicles (HT and MT) at outer cordons and within the city. The private vehicle share is almost same for light vehicles (LT, GA and GV) within the city whereas it is higher at outer cordons. The World Bank Group Page | 55 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Outer Cordons Within City 100% 100% 90% 90% 80% 80% 70% 70% 60% 60% 50% 50% 40% 40% 30% 30% 20% 20% 10% 10% 0% 0% LT HT Tanker MT GA GV LT HT MT GA GV Freight Vehicle Type Freight Vehicle Type Personal/ Hired Pvt. Company Govt Personal/ Hired Pvt. Company Govt Figure 5.19: Ownership Distribution of Freight Vehicles at Outer Cordons and within the City of Delhi 5.9.3. Fuel Efficiency The mileage (fuel efficiency in terms of km/litre) data of different freight vehicles has been analysed and shown in Figure 5.20. From the Figure 5.20, it can be observed that light vehicles (LT, GA and GV) have higher fuel efficiency which are mostly run on CNG. Heavy freight vehicles have fuel efficiency about 6.5 and 4.8 km/litre for HT and MT respectively. Light vehicles namely LT has about 11 km/litre, where as GA and GV has more than 14 km/litre. 16 Fuel Efficiency 14.5 14.2 14 Fuel Efficiency (km/lt) 12 10.7 10 8 6.5 6.6 6 4.8 4 2 0 LT HT Tanker MT GA GV Freight Vehicle Type Figure 5.20: Fuel Efficiency of Different Freight Vehicles The World Bank Group Page | 56 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report 5.9.4. Distance Travelled The distance travelled data in terms of km/trip, km inside city and km/day of different freight vehicles has been analysed and shown in Figure 5.21. From the Figure 5.21, it can be observed that average trip distance of MT is about 228 km and for HT, it is about 112 km, whereas vehicle type LT has about 70 km and smaller vehicles are having a trip distance of about 50 km. All these vehicle types travels about 20-25 km within the city. And it can also be observed that the maximum average distance travelled in a day by these vehicle types is about 200 km. This clearly indicate that these freight vehicles face lot of congestion and other problems to travel more distances in a day experiencing lot of delays and increased operating costs. Average Trip Distance Average Distance (inside 250 228 30 City) 26 Average Distance Travelled (km) Average Trip Distance (km) 26 24 200 25 21 19 20 20 150 112 103 15 100 71 55 10 45 50 5 0 0 LT HT Tanker MT GA GV LT HT Tanker MT GA GV Freight Vehicle Type Freight Vehicle Type Average Distance Travelled 250 in a Day Average Distance Travelled 203 184 191 200 (km/day) 150 134 121 116 100 50 0 LT HT Tanker MT GA GV Freight Vehicle Type Figure 5.21: Distance Travelled by Different Freight Vehicles The World Bank Group Page | 57 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report 5.9.5. Frequency of Trips The frequency of trips data of different freight vehicles has been analysed and shown in Figure 5.22. From the Figure 5.22, it can be observed that Light Vehicles are having more daily trips and Heavy Vehicles are more in Occasional trips. Outer Cordons Within City 100% 100% 90% 90% 80% 80% 70% 70% 60% 60% 50% 50% 40% 40% 30% 30% 20% 20% 10% 10% 0% 0% LT HT MT GA GV LT HT MT GA GV Freight Vehicle Type Freight Vehicle Type Ocassionally Weekly Tri-Weekly Ocassionally Weekly Tri-Weekly Bi-Weekly Daily Bi-Weekly Daily Figure 5.22: Frequency Distribution of Freight Vehicles at Outer Cordons and within the City of Delhi 5.9.6. Weight Carried The weight carried by different freight vehicles has been analysed and shown in Figure 5.23. From the Figure 5.23, it can be observed that MT Vehicles are carrying average weight more than 13 tonne where as HT vehicle is carrying average loads of 5-6 tonne. The LT is carrying average weight about 2 tonne and smaller vehicles like GA and GV are carrying less than a tonne. Further, an analysis has been carried out to assess the share of empty vehicles and the result is presented in Figure 5.24. From Figure 5.24, it can be seen that the 10-20% vehicles are running empty on the road network of Delhi. Further the total weight carried by these freight vehicles on the entire road network of Delhi has been estimated from average distance travelled and weight carried in a day which comes to be about 2.480 Million Metric Tonne (MMT) per day. The World Bank Group Page | 58 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Average Weight Carried (Tonne) 14 13.21 Avg. Weight (Tonne) 12 10 8 5.62 6 4 1.94 2 0.81 0.76 0 LT HT MT GA GV Freight Vehicle Type Figure 5.23: Frequency Distribution of Freight Vehicles at Outer Cordons and within the City of Delhi Share of Empty Vehicles 100% Percentage of Vehicle 80% 60% 40% Empty 20% Loaded 0% LT HT MT GA GV Freight Vehicle Type Figure 5.24: Share of Empty Vehicles in Different Typed of Freight Vehicles 5.11. Development of Freight Transport Demand Models 5.11.1 Background Generally, passenger transport models are developed based on the observed travel pattern and the socio-economic characteristics of commuters of the city. The traditional approach of four- stage modelling has following transport sub-models are: (i) Trip Generation (ii) Modal-Split (iii) Trip Distribution (iv) Traffic Assignment. In the present study, freight transport demand model has been proposed to develop considering same traditional approach of four-stage modelling as passenger travel demand modeling. However, in this chapter, freight trip generation, freight trip distribution and freight modal split models have been discussed. The The World Bank Group Page | 59 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report freight traffic assignment along with passenger traffic assignment has been discussed in the Chapter 7. Prior to this, development of existing transport network is the foremost data input for the transport models besides the observed travel characteristic data and planning parameters at traffic zone level. 5.11.2. Traffic Zones, Road Network and Socio-economic Data The study area i.e. NCT of Delhi is divided into 360 administrative wards and the same has been adopted in the present study. These zones are also called as Traffic Analysis Zones (TCZ) and these 360 zones of the study area have been shown in Figure 5.25 and the details of these zones are given in Appendix 10. The zone wise socio economic data such as Population, Land Use Types, Number of Households, Employment, Total Land in Hectares, Commercial Area, Industrial Area, Residential Area, Recreational Area, Public & Semi Public Area etc. which are going to be used for development of travel demand modelling is also collected from the secondary source namely Census data (Census, 2011) and Master Plan for Delhi - 2021 (DDA, 2010). The road network of study area i.e. NCT of Delhi has been created from the exiting maps and field visits. The network has been developed by creating links and nodes as shown in Figure 5.26. The Traffic Zone Centroids have been serially numbered starting from 1 onwards for each of the Traffic Zone of NCT of Delhi (1-360) and external zones (361-368). The transport network has been prepared for the whole of NCT of Delhi area including external zones. The existing transport network of Delhi city consists of only roads as the road based mode is only considered at present. The road network has total 2263 Links and 1500 nodes and included road link characteristics: link-type, length, observed carriageway width, no. of lanes, divided/undivided, and speed, capacity, etc. The transport network of existing roads is shown in Figure 5.26. As speed and delay studies were conducted, the speeds for different links are taken from the surveys separately for different type of roads namely major roads, intermediate roads, connectors etc. The World Bank Group Page | 60 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Figure 5.25: Traffic Analysis Zones (TCZ) considered for the City of Delhi The World Bank Group Page | 61 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Figure 5.26: Created Road Network (Links and Nodes) for the City of Delhi 5.11.3 Freight Trip Generation Models Freight Trip Production Models The freight trip data has been analysed based on zone wise and estimated the trips generated from that zone. Multiple Linear Regression (MLR) Analyses technique has been used to model the Freight Trip Productions. The form of the freight trip production equation is given below: The World Bank Group Page | 62 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report T=Xβ+ ---------- Eq. (5.1) Where, T is number of freight trips produced or attracted X is vector of independent variables (socioeconomic and land use intensity) β is parameter vector to be estimated  is unexplained error term and a constant can be considered for this Out of the socio-economic and land use parameters discussed in the Section 5.11.2, the following variables are taken as influential parameters in estimating freight trip productions in zonal level:  Population (P)  Employment (E)  Commercial Area (C)  Industrial Area (I) Using the above variables, the zonal trip productions are modelled and developed zonal level trip production regression models. For this purpose SPSS 18 has been utilised to estimate the parameters and statistical validation. The developed model for freight transport trip production is given below: PFi = 0.021*Pi + 0.003*Ei + 14.499*Ci - 17.858*Ii ---------- Eq. (5.2) Where, PF is Freight Trip Productions i is zone number The above regression equation can be considered as relatively good statistical significance as it is having R2 Value of 0.3. Freight Trip Attraction Models Similar to freight trip productions, Multiple Linear Regression (MLR) Analyses technique has been used to model the Freight Trip Attractions. The form of the freight trip production equation is given in Eq. 5.1. The World Bank Group Page | 63 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Out of the socio-economic and land use parameters discussed in the Section 5.11.2, the following variables are taken as influential parameters in estimating freight trip productions in zonal level:  Population (P)  Employment (E)  Commercial Area (C)  Industrial Area (I) Using the above variables, the zonal trip attractions are modelled and developed zonal level trip production regression models. For this purpose SPSS 18 has been utilised to estimate the parameters and statistical validation. The developed model for freight transport trip production is given below: AFi = 0.026*Pi + 0.002*Ei - 17.564*Ci ---------- Eq. (5.3) Where, AF is Freight Trip Attractions i is zone number The above regression equation can be considered as relatively good statistical significance as it is having R2 Value of 0.38. From this analysis, it can be concluded that the developed equations for trip productions and attractions can be used to estimate trips with relatively good accuracy. Estimation of Total Freight Trips From the developed Freight Trip Production and Attraction Models given in Eq. 5.2 and Eq. 5.3, the total trips have been estimated from all the zones and presented in Figure 5.27. From the Figure 5.27, it can be seen that about 500 thousands of freight trips are generated daily in the city of Delhi. The World Bank Group Page | 64 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Total Trips (Freight) 500000 490822 495698 400000 Productions Total Trips 300000 Attractions 200000 100000 0 2017 Figure 5.27: Estimated Total Freight Trip Productions and Attractions in Delhi (2017) 5.11.4. Freight Modal Split After estimation of the total freight trips, the modal split has been estimated considering the traffic composition observed at outer cordon and within the city at focal points. The final freight modal split is shown in Figure 5.28. From the Figure 5.28, it can be observed that all different freight vehicles namely GA, LT, HT and MT form almost equal share varying between 22-25% where as GV is about 5% share. Freight Modal Split GV LT GA 5% 24% 25% HT MT Tanker 23% 22% 1% Figure 5.28: Modal Split of Total Freight Trips The World Bank Group Page | 65 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report 5.11.5. Freight Trip Distribution Models Gravity Model Formulation Gravity Model formulation shown in Eq. 5.4 has been used for Trip Distribution Model calibration. The deterrence function used is Tanner’s function. Tij = Ai * Bj * Pi * Dj * F(cij) -----------------(5.4) The F(cij) is used from Tanner's function as shown in Eq. 5.5. F(cij) = (cij ** X1) * (e ** X2*cij) ----------------- (5.5) where: Ai * Bj: Balancing factors Pi: Production from ith Zone Dj : Attraction to jth Zone F(cij) : Deterrence Function cij : Generalized Cost of Travel from 'i' to 'j' Zone X1 and X2: Calibration Parameters Gravity Model Calibration and Estimation of Freight O-D Matrices Freight trip distribution models have been calibrated using the Distance and Time Skim matrices generated from the coded network of existing roads. The observed Origin Destination (O-D) - Trip matrices have been separately calibrated for each mode. For this purpose, VISUM 11 Software has been utilised and estimated freight O-D matrices for different freight vehicle types. In the present study, the total number of zones taken as 368, out of which 360 are internal zones and 8 are external zones. The size of O-D Matrix would be 368 X 368. A typical view of estimated total freight O-D matrix has been shown in Figure 5.29. The desire line drawings have been developed in order to see the trend and intensity of trips between origin and destination using VISUM Software and shown in Figure 5.30. The World Bank Group Page | 66 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Figure 5.29: Typical View of Estimated Total Freight O-D Matrix for the City of Delhi The World Bank Group Page | 67 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Figure 5.30: Desire Line Diagram of O-D Matrices for Freight Trips 5.11.6. Freight Trip Assignment The trip assignment has been performed using Urban Strategy Software tool developed TNO. For this, purpose, the developed O-D Matrices for freight traffic has been submitted to TNO. When the assignment of freight trips made on the network, there would be passenger trips already on the network. Hence it is essential to consider all the trips including freight and passenger trips together to perform trip assignment. The passenger trips which are developed in the project of SUSTRANS (CRRI, 2017) for the city of Delhi has been considered in the present study and carried out the trip assignment in Urban Strategy by TNO which is discussed in Chapter 6. The World Bank Group Page | 68 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report 5.12. Pattern of Total Freight Trips The pattern of total freight trips are classified under four categories. They are:  External - External (E-E)  External - Internal (E-I)  Internal to External (I-E)  Internal - Internal (I-I) Accordingly the freight trips are analysed and results are shown in Figure 5.31. From the Figure 5.31, it can be seen that the majority of freight trips are Internal - Internal which is almost 80%. The Internal-External and External-Internal are almost same about 8% each and External-External trips (passing through) are about 4%. Travel Pattern (Freight) E-E E-I 4% 9% I-E 8% I-I 79% Figure 5.31: Pattern of Total Freight Trips i Delhi The modal split of these freight trips has been analysed and presented in Figure 5.32. From the Figure 5.32, it can be observed that heavy freight vehicle share is about 26% in case of I-I Trips, about 43% in case of I-E Trips, about 53% in case of E-I Trips and about 61% in case of E-E Trips. The comparison of total freight trips are made with passenger trips in order to understand the share of freight trips in the city of Delhi and shown in the Figure 5.33. From the Figure 5.33, it can be observed that the share of freight trips is only about 3% which is very insignificant, however it is going to influence huge in traffic congestion, air pollution and road safety related issues of the city of Delhi. The World Bank Group Page | 69 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report I-I Travel (Freight) I-E Travel (Freight) GV GV LT LT 6% 7% GA 24% 28% 27% GA 39% HT HT 15% MT 25% MT Tanker 17% Tanker 10% 1% 1% E-I Travel (Freight) E-E Travel (Freight) GA GA GV LT GV LT 15% 20% 3% 24% 2% 22% MT MT 34% HT HT 26% 25% 25% Tanker Tanker 2% 2% Figure 5.32: Freight Modal Split for Different Types of Trips in Delhi Total Trips (2017) Freight Trips 3% Passenge r Trips 97% Figure 5.33: Share of Freight Trips in the Total Trips of Delhi The World Bank Group Page | 70 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report 5.13. Forecasting of Freight Trips from Freight Transport Demand Models The developed freight transport models are utilised to forecast the trips that going to be generated in the City of Delhi. As it was already discussed in Section 5.11.3 that the trip productions and attractions are depend on certain variables which are intern considered in equation development. The variables are:  Population (P)  Employment (E)  Commercial Area (C)  Industrial Area (I) Out of all these variables, the population data over the years is available and limited data for the other variables namely employment available for the year 2021 which is related to Census Updation year in India. Using the growth factors for these variables, growth factors for other variables have been appropriately considered. From this exercise, the total trips productions and attractions for the year 2021 are estimated. The estimated trips for the year 2017 and 2021 are shown in the Figure 5.34. Total Trips (Freight) 572,071 600000 490,822 500000 400000 Total Trips 300000 200000 100000 0 2017 2021 Figure 5.34: Forecasted Total Freight Trips for the Year 2021 in Delhi The World Bank Group Page | 71 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report From the Figure 5.34, it can be seen that the forecasted trips have increased to about 572 thousands with a growth rate of 4% per annum. 5.14. Estimation of Traffic Loads on the Road Network In 2002 and 2009, CSIR-CRRI has conducted a study to estimate traffic loads in terms of vehicle kilometers travelled (VKT) on the road network of Delhi and accordingly projected for the years 2010 and 2015 (CRRI, 2009). Utilising this data, the projections have been made from the growth factors for all the vehicle types. The estimated VKT for 2017 and forecasted VKT for the year 2021 are presented in Figure 5.35. From the Figure 5.35, it can be observed that the estimated total traffic loads in terms of VKT are about 240 Millions and 300 Millions in 2017 and 2020 respectively. The VKT by freight vehicles are going to be about 10 Million and 13 Millions in 2017 and 2020 respectively which is having a share of about 4%. The growth of total VKT is increasing with 7% per annum growth whereas freight vehicles growth is about 8% per annum. 350 308.7 Cars 300 Two Wheelers Autos 240.0 250 Goods Vehicles VKT (in Millions) 211.8 Buses 200 Total 160.8 150.6 150 100 79.2 13.03 50 9.79 8.49 6.19 5.73 2.51 0 2002 2009 2010 2015 2017* 2021* Year * Estimated in Present Study Figure 5.35: Estimated Vehicle Kilometers Travelled /Day for different Vehicle Types for Different Years The World Bank Group Page | 72 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report References CRRI. (2017). Development and Application of Technologies for Sustainable Transportation (SUSTRANS), Central Road Research Institute (CRRI), 12th Five Year Plan Project, , Transportation Module, Final Report, Planning Commission 2017. CRRI. (2009). Urban Road Traffic and Air Pollution in Delhi, Central Road Research Institute (CRRI), Final Report Submitted to Society of Indian Automobile Manufactures (SIAM), 2009. Census (2011), Census of India, Government of India. DDA (2010) Master Plan for Delhi - 2021, Delhi Development Authority (DDA), Government of NCTD. The World Bank Group Page | 73 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report The World Bank Group Page | 74 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report 6. CAPACITY DEVELOPMENT FOR SUSTAINABLE CITY LOGISTICS 6.1. Capacity Assessment From the literature on the subject of sustainable urban freight systems (SUFS), the following are critical capabilities for the development and deployment of solutions:  Process capabilities: this includes the ability to create an institutional environment and a series of activities that allow stakeholders to go through consecutive design cycles and implement change.  Substantive capabilities: this concerns data, information, models and knowledge about initiatives, necessary to be able to identify problems, design innovative solutions and implement them. Fundamental to alleviating problems is the recognition that the SUFS problem is a multi- stakeholder problem with many interdependencies and possible conflicts. Generally, a shared understanding amongst stakeholders about the main problems or the preferred solutions is absent. As in most countries, the logistics sector is fragmented. Besides the different actors or stakeholders (think of private vs. public, supply chain actors, etc.), there are different markets in urban freight that require a differentiated approach (e.g. construction traffic having different needs than retail and waste retrieval). There seem to be no national or local NGOs working on the problem of city logistics that work towards a unifying approach. Also, as in most countries, there are fundamental difficulties with the functioning of the freight market that make governance issues non-trivial – both market and government failures in relation to sustainability are easy to identify. First and foremost, sustainability problems are largely external to these markets (i.e. non-priced, at least not on the basis of social marginal costs). Internalizing external costs through pricing has important political constraints, relating to both the economic and the social pillar of sustainability. Secondly, there are few other incentives in place for the transport industry to follow a sustainable course, such as recognition programs or fleet renewal benefits. Thirdly, innovation and experimentation costs are relatively high for the low margin business of transportation services. The World Bank Group Page | 75 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Partly because of these reasons, as some countries have already demonstrated, it is good practice to develop a pro-active policy towards cohesiveness between stakeholder groups so that problems can be addressed simultaneously. There are different ways to organize this co- operation. The highest form of organization is the so-called living lab (Quak, et. al., 2016), with freight partnerships and demonstration or pilot-oriented cooperation as intermediate forms as shown in Figure 6.1. Figure 6.1: Description of Different Levels of Organisations with Different Objectives and Requirements Currently, the city of Delhi has a very low level of organized stakeholder involvement in urban freight and, as a result, a low level of shared situational awareness. This has not been a problem for some important measures that have been successful, such as the establishment of freight markets for local distribution supported by local Master Planning, nation-wide abolition of fuel subsidies, and the massive deployment of CNG engines for auto-rickshaws and government vehicles in New Delhi. For other measures, such as the local pollution tax, the ban for trucks aged older than 10 years, or even the short term ban for all trucks during the severe smog days after 9 November 2017, still a lack of understanding and agreement can be felt from the transport sector as shown in Figure 6.2. Trucks are perceived and treated as The World Bank Group Page | 76 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report one of the major causes of air pollution in the city, while this perception is based on few facts and measurements. Figure 6.2: Some of the News Clippings on Delhi Air Pollution and Entry/ Ban of Trucks in the City A dialogue between stakeholders which is based on some form of joint fact finding could be the start of a process towards shared situational awareness and broadly supported innovation measures. This confirms the need for a dual approach, resting on process and substantive aspects of the SUF problem. 6.2. Knowledge Development Plan Given the results of this first capacity assessment, the following needs can be identified, building on the general needs statement provided in the above. 1. Process capabilities: this includes the ability to create an institutional environment and a series of activities that allow stakeholders to go through consecutive design cycles and implement change. a. Organizational capabilities; understanding stakeholder positions, organizational forms, stakeholder management, forms of stakeholder cooperation. b. Innovation management, including R&D management, roadmap development, strategic alliances, innovation economics and management of technology, project and process management. The World Bank Group Page | 77 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report A useful example in this respect is the processes followed in various European cities as described in Quak, et. al. (2016). Road mapping has been an important activity for the city of Rotterdam. Here, based on a shared vision to move to zero-emission transport in 2025, the community drafted a roadmap towards this goal with four chapters: technology, behaviour, logistics organization and public policy. The steps necessary to achieve the goal were found by back casting from the objective towards the present and by seeking mutual alignment between chapters. Figure 6.3 illustrates this roadmap and the process followed with stakeholders was instrumental to arrive at this result. Figure 6.3: Road Map towards Zero Emission in the City of Rotterdam, The Netherlands 2. Substantive capabilities: this concerns data, information, models and knowledge about initiatives, necessary to be able to identify problems, design innovative solutions and implement them. The World Bank Group Page | 78 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report a. Data acquisition, statistical analysis, visualisation, reporting: training in these skills belongs to the standard curriculum of local universities and institutes such as CRRI. Their understanding at government (outside academically trained personnel) is limited, however. b. Freight modelling including 4 step models; currently locally taught and practiced at Delhi’s School of Planning, IIT Delhi and CRRI. Also more advanced approaches including logistics responses or methods such as agent based modelling could be adopted. These require investments in research, however. c. Understanding of SUF solutions, including understanding of problems, agreement about causes, knowledge of possible measures and experiences in other cities, knowledge of evaluation approaches (business modelling, cost- benefit analysis) and implementation complexities. In order to understand how the above three points are intervened, let us look at the concrete demands of a design for future logistics in Delhi as was discussed during the national workshop for this project. Figure 6.4 shows the current situation on the left hand panel, with many criss-cross trips going into the city, without consolidation. The right hand panel, inspired by the well-documented Dutch initiative of Binnenstadsservice (Van Rooijen et. al., 2011) shows how a combination of innovations can reduce the burden on a city. Consolidation takes place outside the city borders, and the last mile is done with clean vehicles. To incentivize the use of this new technology, city tax schemes directed at polluting vehicles could be employed. By recycling this tax towards subsidies for clean vehicles, the cost disadvantage of the new solution would be further reduced. Existing Proposed Figure 6.4: Existing (without Consolidation) and Proposed Situation of Trips into the city with Consolidation The World Bank Group Page | 79 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report In order to evaluate the impact of such an initiative, we need to understand how retailers, manufacturers, carriers and logistic service providers would respond to the new facilities and fiscal measures. This complex interaction of behaviours requires many data and advanced freight models to be in place. Carriers will plan their trips differently, with changes in tours and vehicle types; the operator of the urban consolidation centre will modify the price of their service to maximize their revenues; if this does not lead to a social optimum, government may decide to subsidize additionally or change the pollution taxes; retailers may adapt their ordering policies to the changes in transport costs; finally, consumers may decide to change their shopping behaviour if streets become more attractive. Anand (2015) has modelled this solution. Depending on the outcome of such an evaluation, changes in the proposed measures would need to be discussed and evaluated in a systematic way, to arrive at the best possible outcome – on paper. Implementation with all the stakeholder will not rely on analysis capabilities, but mostly on the process capabilities sketched above. 6.3. Stakeholders Meetings and 1st Workshop on MEGALOG As part of the project, the stakeholders need to be involved in conducting meetings so as to understand and reach the actual ground level problems and difficulties in implementing transport policies related urban fright traffic. Accordingly the first meeting for this purpose has been conducted on May 9th, 2017 at the C. V. Raman Hall of CSIR - CRRI, Mathura Road, New Delhi. This meeting was attended by the policy makers, development authorities, practising engineers representing national / state level and local bodies, academia, research institutes and decision makers etc. A total of 34 delegates (list is given in Annexure 11) were attended this 1st meeting and emphasised issues related to freight transport in India and especially in Delhi. The agenda of the program is given in Figure 6.5 and some of the views of the meeting are presented in Figure 6.6. The World Bank Group Page | 80 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Figure 6.5: Agenda of 1st Workshop on MEGALOG held on 9th May 2017 at CSIR- CRRI, New Delhi The World Bank Group Page | 81 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Director (CSIR-CRRI) chairing the Dr. Errampalli Madhu (CSIR-CRRI) making MEGALOG Meeting presentation Prof. Lorant A. Tavasszy (TU-Delft, Mr. Jeroen Borst (TNO, Netherlands) making Netherlands) making presentation presentation View of the Stakeholders participated in the MEGALOG Meeting Figure 6.6: Some Views of 1st Workshop on MEGALOG held on 9th May 2017 at CSIR- CRRI, New Delhi During the above stakeholders meeting, the following points are discussed:  Tuglakabad Container Depot should be considered in the freight travel demand modelling  Impact of Goods and Service Tax (GST) may be examined The World Bank Group Page | 82 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report  Impact of mass housing and commercial complexes on freight generation parameters may be studied  Stakeholders group who are related to freight operations and policies should be finalised  Travel Planning Model and Travel Optimisation Model should be focussed  Modelling process should focus both small haul like Pizza Delivery and large trucks  Passenger traffic has to be taken into consideration in freight travel demand modelling especially at traffic assignment stage  Both internal distribution and outer cordon points should be considered  Integration with Current Government policies  Increase in emission loads and fuel consumption from the freight traffic need to be studied  E-commerce and Home Delivery trends can be examined with respect to travel behaviour  City logistics mainly focus on restriction of timings and areas  Education campaign and training workshop should be focusing on freight operators, policy makers etc.  Drivers awareness program also should be included  Parking requirements for trucks may be studied  Significance of bypass for truck traffic which are passing through the city has to be studied 6.4. Organizing Short Courses An important goal of the project is to create an impact in practice. An extensive pilot study is carried out for New Delhi with a transferable modelling approach. The city of Delhi i.e. National Capital Territory of Delhi (NCTD) has been selected as study area for this study and quantified the metrics related to freight traffic in the city. As part of fulfilment of the objective of the project, skill development training in terms of short courses to various stakeholders and operators on sustainable city logistics (SCL) has been organised on December 12th, 2017. Prior to that, capacity assessment of existing situation has been done The World Bank Group Page | 83 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report through a proforma of Capacity Assessment and Policy Inventory. The proformas for Policy Makers, Operators and Experts have been designed separately for these groups as the objective of these stakeholders are different however, they are all towards sustainable city logistics. The designed proformas are given in Appendix 3. Accordingly the short course on Sustainable City Logistics has been conducted on December 12th, 2017 at the Lecture Hall of CSIR - CRRI, Mathura Road, New Delhi. This course was attended by the policy makers, development authorities, practising engineers representing national / state level and local bodies, academia, research institutes and decision makers etc. A total of 37 delegates (list is given in Annexure 11) were attended this short course where issues related to sustainable city logistics, freight transport in India especially in Delhi, Activity Based Modelling approach, evaluation tools namely Urban Strategy etc are emphasised. The agenda of the program is given in Figure 6.7 and some of the views of the meeting are presented in Figure 6.8. 6.5. National Level Dissemination Workshops Subsequently, National Dissemination Workshop on Megacity Logistics has been organised on December 13th, 2017 at CSIR-CRRI, Mathura Road, New Delhi to disseminate the findings from the project, approaches to be adopted by different policy makers/ stakeholders to achieve sustainability in the area of City Logistics in the city of Delhi. This workshop was attended by the policy makers, development authorities, practising engineers representing national / state level and local bodies, academia, research institutes and decision makers etc. A total of about 53 delegates (list is given in Annexure 11) were attended this workshop where issues related to good practices to achieve sustainable city logistics, freight transport in India especially in Delhi, Activity Based Modelling approach, evaluation tools namely Urban Strategy etc are emphasised. The agenda of the program is given in Figure 6.9 and some of the views of the meeting are presented in Figure 6.10. In the workshop, the commitment towards sustainable city logistics session has been carried out in that, the participants expressed their support by signing on that banner where the goals of sustainable city logistics are mentioned as shown in Figure 3.2. The World Bank Group Page | 84 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Figure 6.7: Agenda of Short Course on Sustainable City Logistic held on 12th December 2017 at CSIR-CRRI, New Delhi The World Bank Group Page | 85 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report As the workshop was attended by some freight operators as well, the common issues faced by operators are also discussed like, time and age restrictions for freight vehicles to enter/ operate in the city, entry tax/ tolls at various points etc. The policy makers need to consider the operator perspective also to achieve the sustainable city logistics as a whole. Inauguration of Short Course on "Sustainable City Logistics for Policy Making and Freight Operations" by Prof. Satish Chandra, Director (CSIR-CRRI) and Prof. Lorant A. Tavasszy (TU Delft) Presentations by Prof. Russell G Thompson (Univ. of Melbourne, Australia) and Prof. Lorant A. Tavasszy (TU Delft, Netherlands) Presentations by Dr. Errampalli Madhu (CSIR-CRRI) and Dr. Hans Quak (TNO, Netherlands) The World Bank Group Page | 86 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Presentations by Mr. Jeroen Borst (TNO, Netherlands) and Dr. Nilesh Anand (TU Delft, Netherlands) Figure 6.8: Some Views of Short Course on Sustainable City Logistics held on 12th December 2017 at CSIR-CRRI, New Delhi The four important points emerged in the workshop to achieve sustainable city logistics are given below:  Reduction of negative effects of urban freight transport while maintaining productivity  Encourage to carry out appropriate research  Systematic planning of facilities  deployment of Innovative methods  Advanced Vehicular Technologies  Identification of workable urban freight solutions including roadmaps towards these  Adequate database (upto date)  Evolving appropriate tools  Encourage to carry out appropriate research (academia, R&D institutes etc.)  Frequent meetings/ discussions among stakeholders (researchers, policy makers, freight operators etc.)  Increase of the knowledge base including data collection, models and scenarios  Adopting advanced techniques for data collection  conducting skill development training/ short courses to various stakeholders and operators on sustainable city logistics (SCL)  Encourage to use of advanced analytical tools  Collaboration with other stakeholders to realize solutions towards sustainability  Formulation of a organizational body with all possible stakeholders  Members with shared awareness about sustainability The World Bank Group Page | 87 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Figure 6.9: Agenda of National Dissemination Workshop on MEGALOG held on 13th December 2017 at CSIR-CRRI, New Delhi The World Bank Group Page | 88 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Inaugural Address by Prof. Satish Chandra, Director (CSIR-CRRI) and Chief Guest Address by Prof. Russell G Thompson (Univ. of Melbourne, Australia) Signing to Support Commitment towards Sustainability City Logistics Delegates and Group Photo for the Participation in Workshop on MEGALOG Speakers and Discussion of Technical Session I (Moderated by Prof. Sanjay Gupta, SPA) The World Bank Group Page | 89 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Speakers and Discussion of Technical Session II (Moderated by Dr. Hans Quak, TNO, Netherlands) Panel Discussion on Way Forward for City Logistics (Chairman: Prof. Satish Chandra CSIR- CRRI) Figure 6.10: Some Views of National Dissemination Workshop on MEGALOG held on on 13th December 2017 at CSIR-CRRI, New Delhi 6.6. Design of Booklet on Sustainable City Logistics An important goal of the project is to create an impact in practice by reaching various stakeholders and operators on sustainable city logistics (SCL). Accordingly, a technical booklet has been designed to explain the issues related to need of sustainability, current logistics situation of Delhi, approaches to achieve sustainable city logistics etc. The cover page of the designed booklet is shown in Figure 6.11 and the full booklet is given Appendix 12. The World Bank Group Page | 90 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Figure 6.11: View of Designed Booklet on Sustainable City Logistics 6.7. Future Capacity Building Process on Sustainable City Logistics The CSIR-CRRI is well connected to all transport related actors in city logistics, including infrastructure management, the transport sector and government. Through the meetings, short course and workshops, the need for city logistics, current limitations/ problems for sustainable logistics and the way forward to achieve sustainable city logistics have been discussed with the potential stakeholders and accordingly finalised as given Section 6.5. The list of participant and the persons involved (part of the local research team or external stakeholders) in workshops and short course have been given in the Annexure 11. Knowledge sharing at this stage was limited to the workshops and final conference. It has been proposed that all participants will receive a copy of the report including a summary The World Bank Group Page | 91 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report leaflet of the project highlighting sustainable city logistics for the city of Delhi. This will be followed up with meetings with high level representatives of the city region government to gauge the willingness and availability of resources to continue the process with a more elaborate modelling and evidence based policy design cycle. As the transport logistics and administrative setups of different cities more or less matches with present study area i.e. Delhi, the methodology adopted can be replicated to achieve sustainable city logistics for the other cities as well. References Quak, H., Lindholm, M., Tavasszy, L., & Browne, M. (2016). From freight partnerships to city logistics living labs–Giving meaning to the elusive concept of living labs. Transportation Research Procedia, 12, 461-473. Quak, H., & Tavasszy, L. (2011). Customized solutions for sustainable city logistics: the viability of urban freight consolidation centres. In Transitions towards sustainable mobility (pp. 213-233). Springer Berlin Heidelberg. Anand, N. (2015). An Agent Based Modelling Approach for Multi-Stakeholder Analysis of City Logistics Solutions. PhD Thesis Delft University of Technology, Delft: TRAIL Research School. The World Bank Group Page | 92 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report 7. DECISION SUPPORT SYSTEM: POLICY ANALYSIS AND VISUALIZATION TOOLSET 7.1. Introduction 7.1.1. Background The road freight transport market represents a significant proportion of road use and economic activity in India. Fifteen to twenty percent of Gross Domestic Product (GDP) is spent on transport and logistics, significantly higher than for other developing countries. Around 63% of freight is sent by road, and both in cities and in rural areas, trucks can face significant delays due to poor road conditions, congestion and checkpoint delays. The average speed of trucks on Indian roads is around 20 km per hour. The urban road network of Delhi faces these same challenges. With a decennial population growth of approximately 20%, increased urbanization has led to increasing traffic problems such as congestion, air pollution, and reductions in safety. A range of measures have recently been proposed in order to overcome these problems, including the National Green Tribunal (NGT) of India ruling on banning trucks older than 10 years from entering the city of Delhi due to the high emission levels of those vehicles. The city of Delhi currently has other restrictions on freight vehicles, including time restrictions for most of the roads of Delhi and 24-hour bans on some roads. Urban freight in Delhi is primarily distributed by four primary types of vehicles: Heavy Commercial Vehicles (HCV) includes Heavy Truck (HT) and Multi-Axle Truck (MT), Light Commercial Vehicles (LCV) or Light Truck (LT), Goods Vans (GV), and Goods Auto Rickshaws (GA), as well as a range of animal-drawn, person-drawn and pedalled vehicles. In this chapter, an attempt has been made to build a Proof of Concept (PoC) of a decision support system and apply it on urban freight and emissions for New Delhi. This chapter describes how: - TNO built such a decision support system on the basis of Urban Strategy, an existing software instrument. The World Bank Group Page | 93 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report - The resulting decision support system was loaded with data on the current situation and possible future scenarios, including measures to limit the negative impact of freight movements. - The findings of the application of the decision support system to this casus. 7.1.2. Policy Analysis and Visualization Toolset The basis for the policy analysis and visualization toolset will be the instrument Urban Strategy, which has been developed by TNO. The decision support system specifications and pilot systems to be delivered are twofold. Firstly, the visualisation of the city logistics metrics for monitoring purposes is done via a Planning Support System (PSS) on the basis of the 3D city model Urban Strategy of TNO. This PSS (www.tno.nl/urbanstrategy) has been applied in different cities around the world (Amsterdam, Dubai, and Shenzhen) and allows tracing the generation and propagation of traffic and air pollutants. The typical analysis of Urban Strategy in the form of screenshot is shown in Figure 7.1. Figure 7.1: Screenshot of Urban Strategy outputs (Source: TNO) The World Bank Group Page | 94 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report On the basis of data specified by TNO and provided by CSIR-CRRI, TNO will build a structured piloting database and demonstrator of a PSS based on Urban Strategy. The functioning of Urban Strategy application will be demonstrated for a number of policy scenarios that directly affect freight flows within the city and results will be visualised with Urban Strategy. The specifications, the process and follow-up recommendations will be laid down in a report form at the end. 7.1.3. Urban Strategy Urban Strategy is a software tool developed by TNO for interactive spatial planning, in which calculation models are linked to databases. Using a set of interfaces, it is possible to gain insight into the effects of plans and measures in the time span of a workshop. Areas covered by Urban Strategy include traffic, transport, noise, air quality, safety and sustainability. Different scenarios and measures, such as altering traffic circulation, new buildings or land use or lowering speed limits, can be interactively explored with the system. It directly shows the updated detailed traffic calculations, air quality maps and noise contour maps as well as indicators describing the impact (such as annoyance) after a measure has been applied though the interactive interface. Because Urban Strategy integrates different from different sources, it also allows planners to evaluate the effects of measures on many different aspects. Because response times are very short, the instrument can be used in interactive workshops with specialists and stakeholders. In this way, authorities can formulate effective noise abatement strategies in a very short time span while involving the relevant parties. 7.2. Technical Background of Urban Strategy 7.2.1. Software architecture The starting point of the instrument Urban Strategy was to use existing state-of-the art models that were already implemented. In order to be able to let different models cooperate, the following elements were developed: A uniform data model in order to create one shared data The World Bank Group Page | 95 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report tore; a communication framework for communication between the models; interfaces to view and manipulate the data. The architecture for Urban Strategy is shown in Figure 7.2. Figure 7.2: Software architecture for Urban Strategy The framework (IMB) is based on the concept of a Service Oriented Architecture (SOA) or Enterprise Service Bus (ESB). These concepts are used in systems with a set of coupled services that interact with each other by sending messages (data) and events (signals). The IMB framework realizes the interconnection between services by means of a Publish/Subscribe concept. Each service can register the data and/or event it outputs to other services (Publish) and also register the data and/or events it needs as an input (Subscribe). The elements in this architecture, which are partially currently operable and partially being developed as described in the following sections. The World Bank Group Page | 96 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report 7.2.2. Interfaces In order to view and manipulate the data, a number interfaces have been developed, including a interface showing a number of indicators (the 1D interface), a Geographical Information Systems (GIS - or 2D interface) and a 3D interface. Indicators (1D) Interface The so-called 1D interface shows different indicators produced by the different models. Examples of indicators implemented in Urban Strategy are noise annoyance, travel times or the total emissions as shown in Figure 7.3. Figure 7.3: Indicators implemented in Urban Strategy such as Noise Annoyance, Travel Times or the Total Emissions GIS (2D) interface The 2D interface is a GIS (Geographical Information System) interface in Urban Strategy as shown in Figure 7.4 that enables the user to view, select, and edit objects in the database. With this interface, the operator can define scenarios by adding, deleting or changing objects. Road attributes can be changed (such as the road surface type or speed), roads can be added or closed. The building configuration can be altered and noise barriers can be added. The World Bank Group Page | 97 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Title bar Menu bar Toolbars Layer selection panel Model status panel Event panel Status bar Figure 7.4: 2D interface with GIS (Geographical Information System) in Urban Strategy 3D interface The Urban Strategy 3D interface (shown in Figure 7.5) is a viewer to show the geographic dataset and the calculation results in 3D. It allows the user to ‘fly’ around in the Urban Strategy data landscape. The calculation results are divided in layers which can be projected in the 3D world by user configurable keys. The interface is connected to the Urban Strategy framework and can be controlled from the 2D interface (setting the viewport), but is also able to communicate with other instances of 3D interfaces running. The World Bank Group Page | 98 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Figure 7.5: 3D interface with GIS (Geographical Information System) in Urban Strategy Web interface In order to share the output of calculation with a larger group of stakeholders on multiple locations, a web interface has been developed. This web interface allows multiple users to browse through the output data of multiple scenarios and calculate difference maps. The layers that can be accessed are defined in the database and generated in the ‘Mobile Client Connect’ module that is displayed in the architecture Figure 7.6 The World Bank Group Page | 99 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Figure 7.6: Screenshot of Web Interface of Urban Strategy 7.2.3. Data model and data store In order to be able to interconnect different models, one data model was made to describe all the objects in the database. In this way, the definition for each object for each model is the same. For example: noise calculations require detailed and accurate information of the geography of roads while the traffic model needs travel times from node to node on a coarser network. Also the output of the traffic model (overall traffic flows during rush hours) had to be transformed into the input data required by the noise model (traffic flows per day, evening, night for different vehicle classes). Therefore, the output of the traffic model can directly be used as input for the noise model. The data in the data store is actually two dimensional with an optional z-component. Therefore a 3D model of the environment can be built up. On the basis of these starting points, a data model was made to describe all the objects in the database. This data model is based upon the ESRI shape file data model. In this way, the definition for each object for each calculation model is identical and can be easily exchanged with third parties. The data model consists of the various objects with their main attributes and different layers: The World Bank Group Page | 100 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Main Attributes - Building (polygon): height, function, inhabitants, year, central heating - Road (line): height, width, intensity (per period, per vehicle type), road surface, speed, capacity, material - Coverage (polygon): land use code, height - Noise Barrier (line); height, material, width, slope, absorption - District (polygon): name - Node (point): traffic zone, - Noise receptor (point): height, noise level (per period), building-id - Air receptor (point): height, concentration level (per substance), building-id Additional optional layers - Link (Connection): cabling or piping energy grid part - Railway (line): height, width, intensity (per period, per vehicle type), surface, speed, breaking fraction, material - Noise source (point): height, emission (per frequency band, per angle, per period), description, plant - Source of external safety hazard (point): scenario descriptions - External safety hazards (grid): results from external safety calculations. - Node (point): Energy supply or consumption The technical solution that was chosen is an Oracle 11g database under a Windows Server System. Tables are (spatially) indexed, which enables simple spatial (GIS) queries directly on the database, such as linking buildings to districts in order to calculate indicators. More complex queries are, for reasons of performance performed by the calculation models. 7.3. Setup of Urban Strategy for Delhi City 7.3.1. Methodology In order to conduct further analysis of the impacts of potential traffic measures to improve logistics within the city of Delhi and to achieve the stated aims of this project, the first step is to import all available data into Urban Strategy, including locating addition (public) data The World Bank Group Page | 101 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report where possible, and ensuring those data can be used to produce a working model within Urban Strategy. Starting a project for an area that hasn’t previously been analysed in Urban Strategy requires a number of initial steps, including the selection of an appropriate coordinate system, the preliminary input of data, and the testing of that data to ensure that all models behave as expected. Figure 7.7 shows the overall set-up of the Decision Support System (DSS) on the basis of Urban Strategy. On the basis of the input, provided by CSIR-CRRI, traffic assignments and emission calculations are performed by the DSS. Therefore, changes in the road network can be assessed interactively. Figure 7.7: Overview of the Decision Support System on the basis of Urban Strategy 7.3.2. Choice of coordinate system In order to start an Urban Strategy, it is important to select an appropriate coordinate system. Urban Strategy uses a coordinate system in metric units (meters). The UTM (Universal Transverse Mercator) system has been selected, as it is a universal system that can be used anywhere in the world. It divides the world in 60 x 2 zones in which coordinates are defined in metres. The city of New Delhi lies completely in zone 43N. The EPSG code for zone 43N The World Bank Group Page | 102 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report (WGS-84) is epsg:32643. In this coordinate system, a central area of New Delhi defined by the following geographical coordinates: Longitude: 77.19 – 77.265 deg Latitude: 28.59 – 28.64 deg These can be defined in UTM43N by the following UTM coordinates (in metres): x: 714,000 – 721,500 y: 3,165,000 – 3,170,000 7.3.3. Assignment method A volume averaging method has been used to assign the transportation demand (OD Matrix provided to the network. This means that the traffic is divided over several routes, depending on the available capacity and travel times on alternative routes. The assignment module allows multiple transportation modes to be assigned simultaneously. Therefore, roads can be opened or closed for different type of vehicles. 7.3.4. Emission Factors and Emission Loads In the present study, CPCB (Central Pollution Control Board), India (CPCB, 2000) Emission factors for four types of freight vehicles and passenger cars in g/km for different age classes with deterioration factors have been utilised. On the basis of the age distribution, final emission factors were derived. Traffic assignment of the transportation demand yields traffic volumes on the road network [vehicles/24 hr] and multiplication of emission factors leads to emission per vehicle type per meter link [g/(km*24 hr)]. Summation over the vehicle types and links leads to the emission totals per 24 hr on entire road network. 7.3.5. Uploading the data into a new database A new database has been created for use by Urban Strategy, with the name US_NEW_DELHI_2017. The database contains the following information: - buildings - roads - nodes - traffic zones - basic coverage The World Bank Group Page | 103 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report 7.5.6. Input data Roads and Traffic CSIR-CRRI provided traffic data in a number of shape files, in latitude and longitude coordinates (WGS-84). The road network, including links and nodes, with information on link speed and capacity, were then converted to UTM43N. The centres of gravity were then determined for each traffic zone. Buildings and Basic coverage In order to graphically represent the buildings and land coverage for the study area, data were then imported from OpenStreetMaps (OSM) based on the selection criteria that the field “building” contained a non-empty value. The next step was to import areas into the basic coverage layer and assign a land use code (TDN code) to them. Mapping of OSM data to Urban Strategy categorization has been made and shown in Table 7.1. This list is used in a script file (*.ops) for the US Loader. Table 7.1: Mapping of OSM data to Urban Strategy categorization S. TDN S. TDN Query (condition) Query (condition) No code No code 1 amenity='bus_station' 3902 16 landuse='recreation_ground' 5212 2 amenity='college' 5262 17 landuse='residential' 1012 3 amenity='fairgrounds' 5212 18 leisure='common' 5212 4 amenity='grave_yard' 5303 19 leisure='garden' 5212 5 amenity='hospital' 5262 20 leisure='golf_course' 5020 6 amenity='parking' 3902 21 leisure='park' 5212 7 amenity='place_of_worship' 5262 22 leisure='pitch' 5212 8 amenity='school' 5262 23 leisure='stadium' 1023 9 landuse='cemetery' 5303 24 natural='scrub' 5020 10 landuse='commercial' 1073 25 natural='water' 6112 11 landuse='farmland' 5212 26 natural='wood' 5020 12 landuse='grass' 5212 27 tourism='museum' 5262 13 landuse='industrial' 1073 28 tourism='zoo' 5020 14 landuse='railway' 5263 29 osm_id='162295' 6112 15 landuse='retail' 1073 30 boundary='administrative' 5262 The World Bank Group Page | 104 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Road attributes The link details for the road network were then mapped to the appropriate fields within Urban Strategy. In order to do so, certain assumptions needed to be made: a. Mismatch between the number of traffic zones between the shape file and OD matrices. The shape file with traffic zones contained 360 traffic zones, while the OD matrices all consist of 368x368 entries. On inspection, it was determined that the 8 ‘missing’ zones were the zones (feed points) lying outside of the region of interest. These ‘external’ zones were identified from the shape file that contains the connectors (the virtual links from the zone centroid to a node that is connected to the road network). These zone ID’s were numbered 361 – 368. b. Road attributes needed for assignment and further processing. The links that form the road network have been assigned the necessary attribute values based on the following: i. The speed values for all links (attributes V0PRT and R_V0PRT) are 50 km/h and have been assigned to the US fields SPEED_L and SPEED_R. ii. The capacity values for all links (CAPPRT and R_CAPPRT) have been assigned to the US fields CAPACITY_L and CAPACITY_R under the assumption that R_CAPPRT is the capacity in the reverse direction. iii. The values for link length will be recalculated on the fly (as the original data had lengths encoded as a text field including units). iv. All attributes for which no data is available have been assigned default values. v. Other attributes for which we have no input data are assigned default values. 2D visualisation of traffic links, nodes and traffic zone centroids Figure 7.8 shows the screen shot from the 2D module which was analysed in Urban Strategy based on the data discussed in previous sections. From the Figure 7.8, it can be seen that nodes are denoted by red dots, and the centroids of traffic zones by blue dots. Urban Strategy is capable of showing all results in a 3D interface. The 3D graphics can be built on the basis of a range of data types, however the most basic 3D graphical representation can be built on the basis of building shapes and building heights. Building shapes have been imported from OSM, and in the absence of better data on building heights, all heights have been set to a default of 10m. The World Bank Group Page | 105 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Figure 7.8: Screenshot from the 2D module analysed in Urban Strategy for New Delhi City 3D Module Using the available data from OSM and using a nominal building height of 10m, a basic 3D view for the city of Delhi has been constructed and is shown in Figure 7.9. The World Bank Group Page | 106 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Figure 7.9: City of Delhi loaded in the 3D interface of Urban Strategy 7.4. Policy Evaluation with Urban Strategy 7.4.1. Freight Transport Policies/ Scenarios The first step to see the effectiveness of the developed evaluation tool Urban Strategy is building up of various scenarios. A number of related scenarios have been defined, that either affect the emission factors of the traffic circulation: Scenario 1 – Elimination of diesel goods vehicles older than 10 years Scenario 2 – Placement of a number of freight hubs in outer areas Scenario 3 – Restriction of heavy trucks from entering a city centre Scenario 4 – Elevated high-density corridor Scenario 5 – Improved connectivity to Railway Stations / Airports Scenario 6 – Fleet conversion to low-pollution vehicles The World Bank Group Page | 107 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Scenario 1: Elimination of diesel goods vehicles older than 10 years Under this scenario, emissions factors for heavy, medium and light trucks altered based on removal of diesel vehicles older than 10 years from the vehicle fleet. Assumptions:  The resultant vehicle fleet would reflect the current distribution of vehicles that are either not-diesel, or younger than 10 years old.  No other changes to the vehicle fleet composition.  Vehicle emissions based on 2017 emission factors. Scenario 2: Placement of a number of freight hubs in outer areas Under this scenario, new logistics hubs at 5 locations are proposed as shown in Figure 7.10. All these locations lie outside of the city which has 360 ‘internal’ traffic zones, and correspond roughly to one of the 8 ‘external’ traffic zones. Assumptions: 20% of the external traffic originating from (i.e. entering the city through-) the corresponding external traffic zone, travels to the hub, and the freight continues on in light trucks or goods vans. The load from one heavy truck corresponds to 4 goods vans, or 2 light/medium trucks Scenario 3 – Restriction of heavy trucks from entering a city centre In this, assessment of the traffic impacts of restricting a certain area of Delhi for heavy trucks have been carried out. Assumptions: Under this scenario, area has been selected based on the Delhi city centre as shown in Figure 7.11. From this, major roads are forming an internal ‘ring’ around the closed-off area. All heavy traffic entering the cordon converted to light trucks and vans. All other heavy traffic diverted around the cordon. The World Bank Group Page | 108 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Figure 7.10: Considered Locations of Freight Hubs in Scenario 2 The World Bank Group Page | 109 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Figure 7.11: Considered Closed-off Area in New Delhi City Center under Scenario 3 Scenario 4 – Elevated high-intensity corridor A North – South high capacity elevated corridor without intersections, open for all modes has been considered as shown in Figure 7.12. And the speed limit 60 km/hr has been considered for this proposed corridor. Scenario 5 – Improved connectivity to Rail hubs and airports  Difficulty in predicting impacts on emissions without details on intended operations.  This could be further analysed in a future project. The World Bank Group Page | 110 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Figure 7.12: Proposed High Capacity Elevated Corridor in Scenario 4 Scenario 6 – Fleet conversion to low-pollution vehicles In this option, conversion of freight vehicles to electric transmission, based on a future scenario where 100% of auto rickshaws to electric, along with 17% of light, medium and heavy trucks. This would correspond to a large-scale conversion of auto rickshaws, plus 4% per year converted to electric over a 4 year period. Assumptions: All other vehicle types and emissions stay the same (model uses 2017 emissions). Emissions of CO, NOx, Benzene and Hydro Carbon (HC) assumed to be zero for electric vehicles, and PM10 remaining approximately the same as for the existing vehicle fleet. 7.4.2. Evaluation Results of Policies Base Scenario In order to evaluate the proposed policies, it is necessary to have base scenario where it shows existing condition with no measures. Accordingly the Urban Strategy has been applied The World Bank Group Page | 111 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report with existing travel demand with no measures and estimated the evaluation parameters which are given below: - Traffic volumes [#/24h] - Traffic volume / capacity [V/C] - emissions for each vehicle type, per substance [g/km] - emission totals per substance [g/km] Figure 7.13 shows the web interface that was built according to the set-up description given in Section 7.3. In this interface, spatial distributions can be viewed for the above evaluation parameters and the Traffic V/C values and NOx Emission Loads are shown in Figure 7.14 and 7.15 respectively. From the Figure 7.14 of traffic flows with respect to capacity in the base scenario, it can be seen that the links with a high probability of congestion (blue coloured links) are very high. Figure 7.13: Web Interface of Urban Strategy of New Delhi The World Bank Group Page | 112 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Figure 7.14: Result of Traffic Flows In Relation To Capacity in Base Scenario. Figure 7.15: Spatial Distribution of NOx Emissions due to Road Traffic in the Base Scenario The World Bank Group Page | 113 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Policy Scenarios Scenario 1 – Elimination of diesel goods vehicles older than 10 years Figure 7.16 shows the spatial distribution of NOx emission reduction due to the elimination of 10 year old trucks. This measure will reduce the emissions of all substances in the entire city. Figure 7.16: Spatial Distribution of NOx Emission Reduction due to Scenario 1 Scenario 2 – Placement of a number of freight hubs in outer areas Figure 7.17 shows the spatial distribution of NOx emission change due to the placement of freight hubs in outer areas. Because the same freight is carried in smaller vehicles, the emissions will be redistributed and show a slight overall increase. The World Bank Group Page | 114 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Figure 7.17: Spatial Distribution of Change in NOx Emission due to Scenario 2 Scenario 3 – Restriction of heavy trucks from entering a city centre Figure 7.18 shows the spatial distribution of NOx emission change due to the restriction of heavy vehicles from entering the city centre. Partially because truck routes are diverted around the city emissions will be redistributed and show only slight overall decrease. Figure 7.18: Spatial Distribution of Change in NOx Emission due to Scenario 3 The World Bank Group Page | 115 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Scenario 4 – Elevated high-intensity corridor Figure 7.19 shows the spatial distribution of NOx emission change due to the introduction of a high intensity corridor. The emission of the traffic on corridor itself is not visible on the difference map). Because the traffic will be routed more efficiently, there is a slight decrease of emissions. Figure 7.19: Spatial Distribution of Change in NOx Emission due to Scenario 4 Scenario 6 – Fleet conversion to low-pollution vehicles Figure 7.20 shows the spatial distribution of NOx emission reduction due fleet conversion. This scenario will have an overall positive effect on the air pollution reduction. The World Bank Group Page | 116 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Figure 7.20: Spatial Distribution of Change in NOx Emission due to Scenario 6 Emission totals The emissions calculated per road segment per substance per vehicle type are presented in the previous section is also summarized in the form of indicators of emission totals. Figure 7.21 shows the breakdown of the emission totals for road transport vehicle types in Delhi for different substances for base scenario. From the Figure 7.21, it can be seen that Passenger Car Equivalents (PCE) account for most of the road traffic emissions for all substances. This is due to the fact that the traffic volume of passenger transport is much larger than the other categories. The graph also shows that Heavy truck do contribute substantially to the NOx emissions, followed by the light trucks as shown in Figure 7.21. Figure 7.22 shows the comparison of emission totals for road transport in Delhi for different scenarios. Because the contribution of freight transport is limited, the effect of the different scenarios can be seen more clearly when only looking at the freight emission totals as shown in Figure 7.23. The World Bank Group Page | 117 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Figure 7.21: Breakdown of Emission Totals for Road Transport Vehicle Types in Delhi for Different Substances Figure 7.22: Comparison of Emission Totals for Road Transport in Delhi for Different Scenarios The World Bank Group Page | 118 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Figure 7.23: Comparison of emission totals for road freight transport in Delhi for different scenarios. From the Figure 7.23, it can be seen that removal of diesel vehicles older than 10 years, Scenario 1, shows 4 – 11% decrease in total CO, NOx and PM10 emissions, and negligible difference in Benzene or Hydrocarbon levels. The freight hubs (Scenario 2) and heavy vehicle restrictions (Scenario 3) may lead to increased overall emissions if there is no change to the emissions profile for the vehicles that replace them. Introduction of a high capacity corridor (Scenario 4) will lead to a slight decrease of emissions. Introduction of electric freight vehicles (Scenario 6) shows promising results for reduction in emissions, dependent on the penetration rate achieved. 7.5. Summary of Findings 7.5.1. Urban Strategy for Delhi This chapter describes Urban Strategy, the steps needed in order to use Urban Strategy to model different traffic measures in the city of Delhi, and demonstrates that TNO have been able to use the available data to construct a basic working model within Urban Strategy and distribute traffic on the basis of that model. The data provided by CRRI has been successfully uploaded, and in combination with open-source data from OpenStreetMaps, has been used to The World Bank Group Page | 119 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report conduct an initial traffic assignment, and the results displayed in the 2D and 3D interfaces and the Web interface. The developed system can be utilised as decision support system to evaluate various transport policies by estimating traffic loads and emission loads from vehicular traffic. 7.5.2. Policy Findings Based on the results of estimated traffic loads and emission loads from vehicular traffic from Urban Strategy System, it can be said that the contribution of passenger car movements to the road transportation emissions is higher that the freight emissions. Removal of diesel vehicles older than 10 years shows 4 – 11% decrease in total CO, NOx and PM10 emissions, and negligible difference in Benzene or Hydrocarbon levels. Freight hubs and heavy vehicle restrictions may lead to increased overall emissions if there is no change to the emissions profile for the vehicles that replace them. Introduction of electric freight vehicles shows promising results for reduction in emissions, dependent on the penetration rate achieved. 7.5.3. Suggestion for further research The findings of this study provide insight in the effects of measures on the traffic flows and related emissions. These insights can be developed further on this topic by: - Refinement of the link capacity and speed profiles. Measurements on actual link capacities and speed – flow diagrams, improve the output in terms of congestion prediction. - Refinement of emission factors. In this study, fixed emission factors are used in g/km. In the actual situation, emissions are dependent on speed and congestion factors. Therefore, actual emissions are likely to be underestimated in this study. Taking this into account, scenarios that improve the traffic flow will show impact on emissions. - In future study, concentration and exposure calculations can be added. This will provide a different comparison of scenarios, because shifting emissions out of densely populated will affect health without affecting emission totals. The World Bank Group Page | 120 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report - Adding noise emissions and exposure to the simulation will provide a more integrated view on the situation and the impact of scenarios. 7.5.4. Discussion It has been shown in this chapter that a Decision Support System on the basis of Urban Strategy is feasible for Delhi. Although a number of topics for further research are identified, the study and system can already provide insight in the matter and provide insight in the effect of scenarios On the basis of these findings it appears that some measures, such as freight hubs, will only be effective if they are combined with measures to lower fleet emissions, such as the use of electric vehicles. Another finding is that the impact of measures only targeting freight movements will be limited, due to the relative small contribution to air pollution. Therefore, it would be valuable to apply this system on the integrated challenge of the city of Delhi with its challenges with regards to air pollution, noise traffic. More in general: balancing between urban planning, mobility planning, environmental planning in order to accommodate (economic) growth and improve the quality of life of its citizens. References CPCB (2000) Transport Fuel Quality for the Year 2005. Mc Graw Hill Publications, New Delhi. The World Bank Group Page | 121 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report The World Bank Group Page | 122 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report 8. FEASIBILITY OF AGENT BASED SIMULATION MODEL FOR URBAN FREIGHT ACTIVITIES IN DELHI 8.1 Complexity in Urban Freight Transportation Urban freight activities involve movements of freight vehicles, loading and unloading of goods, parking of freight vehicles. Each of these activities is decided under influence of many factors and by many actors (i.e. Stakeholders). For instance, if goods to be unloaded are very heavy or high in quantity then delivery vehicle is parked in proximity to delivery point even resulting in hindrance to on-going traffic. Similarly, the delivery time of the goods is decided based on requirements of receiver even if that mean the freight vehicle has to travel during peak hours adding to congestion. There are thousands of freight vehicles coming to Delhi NCR every day driving on different routes, in different areas, possessing different characteristics (weight, size, emission) operating in different styles (parking, loading or unloading). The system that emerges from such multitude of activities is very complex and unpredictable. Urban freight transportation is concern of multiple stakeholders-who often has conflicting objectives- focusing on individual gain resulting in unorganized and inefficient freight activities. The complexity of urban freight domain and diverse interest of various stakeholders demand well designed consultation and active participation of all stakeholders for effective urban freight policy-making process. 8.2 Agent Based Modelling Approach An Agent Based Modelling (ABM) approach can simulate the details of continuously changing urban freight characteristic in efficient way and coin emergent behaviour of the dynamically changing urban freight processes. At micro level, urban freight movement is based on supply and demand of goods whereas at macro level it is represented by traffic volume or vehicle kilometre generated by freight activities. Focusing solely on the macro level blurs all trend breaking events and consequent forecast results in major inaccuracy. Similarly micro level structure evaluation of urban freight activities describes the functioning of individual stakeholders, which, however, lacks interaction among different stakeholders. ABM focuses on understanding urban freight activities processes at ‘meso’ level – an The World Bank Group Page | 123 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report intermediate level – using information from macro and micro level. Accordingly, we take information about individual stakeholders at micro level and analyse the interactions between urban freight entities at meso level to understand resulting movements of freight delivery vehicles. At an abstract level ABM is a representation of the many simple agents and relationship among them. Here agents represent entities (e.g. Stakeholders, vehicles, goods) and the ways these entities are connected represent relationships. 8.3 ABM for Urban Freight Activities in Delhi NCR 8.3.1. Conceptual Framework An ABM for urban freight activities in Delhi NCR can include a variety of agents representing stakeholders and other entities of UFT domain. Each agent is an autonomous entity that observes and acts (takes decisions and perform activities) towards achieving goals (e.g. goods ordering, delivery). The autonomous stakeholder-agents also possess other characteristics such as they can learn, move, enter and exit the system. These characteristics mirroring real life as new companies enter the urban freight market, old companies may exit etc. The parallel between ABM and urban freight domain explain suitability of agent-based simulation for modelling Delhi NCR urban freight activities. To develop an ABM for urban freight activities in Delhi NCR, it is imperative to have knowledge about decision structure in urban freight activities. As this decision structure does not necessarily follow the hierarchy of supply chain structure, it is very important to know which stakeholder takes decisions at what level and in which conditions. For example, urban freight movement is result of decision making with respect to the asset use instead of asset control. In essence, during the interaction of stakeholders with varying preferences, the one who governs the influence structure will control the decision making process. In this view, we must integrate this relationship meticulously to get the best model. To understand the feasibility of ABM, a conceptual framework for developing ABM is proposed that is presented in Figure 8.1. The framework begins with understanding urban freight problems in Delhi. Although urban freight activities create different issues concerning congestion, pollution, safety, different city The World Bank Group Page | 124 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report has different need and priority. Once the problem is clearly defined, it gives correct direction for data collection and identifying associated stakeholders. For instance, if illegally parked freight vehicles are the focus of the problem then drivers are important stakeholders. Whereas in case of pollution created by freight vehicles trucking companies and drivers are important stakeholders. With help of data and stakeholder analysis, we can get urban freight profiles for Delhi NCR. Urban freight profiles represent the supply chain structure and concerned activities for particular types of shop, product or receiver. For instance, the urban freight profile of a multi brand retail store is different than that of a store selling one type of product. An urban freight profile is a description of the existing urban freight domain showing associated details such as stakeholders, resources, relationships between entities. Knowledge from other urban freight literature (e.g. research and project reports, pilot) can be used to refine urban freight profiles generated for Delhi. Figure 8.1: Conceptual framework of agent based model for urban freight domain in Delhi NCR The World Bank Group Page | 125 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report The urban freight profile is used to list stakeholder attributes, decision structures and the logistics asset management methodology, which will serve as knowledge base for ABM. Information about stakeholder attributes is used to define behaviour of stakeholder agents. Knowledge about decision structure is used to establish relationships between different stakeholders and other urban freight entities. For example, a carrier-agent is connected with receiver-agent by goods delivery process. Similarly, a freight vehicle and carrier-agent are connected by relationship of ‘owner’ and ‘resource’. Knowledge about logistics asset management helps to model decisions about urban freight activities in ABM. Accordingly, this knowledge can be used to define activities such as goods order management by a receiver-agent or vehicle assignment by a carrier-agent in ABM. With knowledge base ready in terms of stakeholder attributes, decision structure and logistics asset management methodology, development of agent-based model can be started. The proposed development stages of agent based model for urban freight domain in Delhi NCR is shown in Figure 8.2. Figure 8.2: Development stages of agent based model for urban freight in Delhi NCR Once we have gathered information required to develop an ABM, the first step is model specification. In this step, various important components of ABM will be laid out. For instance, main components of ABM for urban freight in Delhi NCR would comprise of (1) Numerous agents specified at various scales; (2) Decision-making heuristics; (3) Learning rules or adaptive processes; (4) An interaction topology; and (5) A non-agent environment. Once the specification of the model is clear, the next step starts with implementing the structure of model by developing agents (e.g. stakeholders, freight vehicles) and environment (e.g. road network, interaction platform). Next, behaviour and decision-making heuristics are embedded in the agents. At this moment, the agents in the model have knowledge about ‘what to do’. Subsequently, we add communication and interactions protocols between the agents and between agents and environment. By doing so agents’ behaviour gets connected with various situations so now agents know ‘what to do when’ and ‘whom to contact’. For example, a shop-agent knows that when goods level reaches at certain point ‘place an order’ by ‘contacting the supplier-agent’. Communication and interactions between agents follow certain protocols that vary depending on the platform of software. After implementing agents, The World Bank Group Page | 126 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report communication protocols and environment, we can use survey data to parameterize different decision variables. Thus, after parameterization, the shop-agents also know ‘how much to order’ based on associated factors. In the urban freight domain, knowledge about number of freight vehicle travelling in certain part of road network is very useful. The agent-based model can be designed to get different types of output. Furthermore, the model can be used for various scenario analyses in Delhi NCR. Model output and its analysis can provide with important information about impacts of certain situation, policy or event on urban freight transport for Delhi NCR. Insights and knowledge gather from ABM can be useful for policy making, pilot test preparation or facilitating urban goods movements in other ways for creating sustainable and efficient urban freight domain. 8.3.2. Application A large variety of autonomous stakeholders operate in the urban freight domain creating a complex behaviour as collective, together creating emergent behaviour in the system that is difficult to predict due to uncertainty and dynamics. An Agent based model can explicitly model the complexity of urban freight domain arising from actions of and interactions between stakeholders. An analysis of the urban freight domain using ABM can provide insight into decision making processes to identify and evaluate the reasons, situations and phenomena that give rise to problems in the domain such as congestion. Accordingly, ABM can be used as policy analysis tool to see how different stakeholders and system will react to changes brought by certain policies such as regulations and tolls. Feasibility of creating role-playing game from an ABM Agents in the model are playing roles of different stakeholders. Thus, also a role playing game can be created from an ABM that can be used for educational purposes. In the game a real life stakeholder can take over decision-making activities of a representative agent through an interface. Stakeholders generally do not have clear idea as how their certain behaviour or activities leads to negative externalities in urban freight domain. In such a game, roles of agents are being played by representative stakeholders who are competing against not only agents, but also other stakeholders. By playing such game, stakeholders can learn how The World Bank Group Page | 127 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report decisions of other stakeholders affect their activities and vice versa. Such a role-playing game can act as platform to experience the complexity and emergence effect of decision-making by different stakeholders. 8.3.3. Proof of concept ABM for Delhi A full-fledged ABM for entire Delhi region requires lot of data and may take long time to develop. It is wise to develop a proof-of concept model for a small section of the city. The pitfalls and problems arising during developments of such model can provide useful guidelines towards developing a full-scale model. ABM represents the real world in micro setting. To start with, we can select a small area of Delhi as test case. Next, we take a simplified demand model in a macro setting (e.g. the full city models described in this report) and unfold it in a top down way, breaking the system down into parts. We can replace parts of the macro level model one-by-one by autonomous entities (i.e. agent) that follow certain rules, have certain goals to achieve. The behaviours of the agents are based on data collected from the sample area. With this technique, at every stage we can replace some part of macro structure with the autonomous agents and we can (eventually) develop a fully functional agent based model depicting micro details and behavioural aspects of real world. 8.3.4. Data needed for urban freight ABM in Delhi Due to the microscopic bottom up approach of ABM, a variety of high quality data is needed. There are many single-truck companies operating in Indian freight domain. These are highly unorganized companies and collecting data from them can be big challenge. On the other hand, with the introduction of modern information technology, many companies operating on Internet are already collecting various types of data. Such companies are doing extensive data collection and analysis to meet higher service demands of urban consumers. However, it is clear from our discussion with researchers with experience working with logistics companies that companies will share data only if they see benefits (e.g. improved service, operation) in return. Conclusively, companies should be integrated in project at early stage with clear benefits. These data may then complement what is already available and described in this report at macro level with a more detailed view at company level. Obviously, a careful sampling approach will still be needed as full data availability will not be reached. The World Bank Group Page | 128 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report 8.3.5. Research capabilities for ABM development India is home to some of the finest technical institutes (e.g. IITs, IISc, NITs) in Asia. There is an IIT and School of Planning and Architecture in Delhi with faculties working on transportation research. Furthermore, CSIR-CRRI, which is the lead partner in this MEGALOG project, has been carrying out research and development projects on traffic and transportation planning of mega and medium cities. Researchers at these institutes have years of experience in areas such as transportation modelling (e.g. four step approach), micro simulation and optimization. Although there is no full-fledged ABM developed for passenger/freight transportation, the researchers are familiar with ABM and some of them have worked with it. For instance, Volvo Research and Educational Foundations have Centre of Excellence in New Delhi, which has explored problems of urban freight domain for Delhi. Accordingly, there is sufficient talent for development of an ABM for urban freight in Delhi. 8.3.6. Challenges for ABM development for Delhi The use of ABM is rewarding due to quality of analysis it provides. Nonetheless, ABM also poses certain challenges to overcome. In the text below we list important challenges.  Validation: Due to characteristics such as path dependency, emergence and multiple interactions, validation is a big challenge for ABMs. Furthermore, the complexity of social processes does not guarantee that each simulation run follows the same sequence, leading to conflict in final output and making the concept of validation for ABM different than in a well-controlled experiment. Traditional validation of comparing model output can partially validate the model however validation of decision-making process is also essential. There are attempts made my researchers to validate such processes. For further reading refer to Anand (2016) and Ligtenberg (2010).  Clarity of agent roles: An agent may have several roles depending on situation. For instance, a typical retailer deals differently with logistics service provider and differently with supplier. Thus it is noteworthy challenge to put different behaviour or decisional characteristics and the agent should be capable of understanding what behaviour it should adopt in what situation.  Agent’s access to information: As different agents have different scope and different degrees of freedom, the service and information available to them varies. By clearly The World Bank Group Page | 129 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report defining the access level for each agent, an ABM should be able to restrict the information flow and ability of agent to influence the whole system. 8.4 Summary A large variety of activities (e.g. freight vehicle movements, parking, loading/unloading goods) is associated with urban freight transportation, which differs with respect to location, types of goods and stakeholders’ characteristics and creates much unorganized system. The emerging complex system is a direct result of conflicting objectives of multiple stakeholders. A well-designed modelling approach that includes the perspectives of multiple stakeholders within the domain is essential to find effective solutions (e.g. policy, regulation, facilitating schemes) for the above-mentioned problems. Agent based modelling is a promising approach to understand the urban freight domain. By taking information about individual stakeholders and entities at micro level an agent-based model (ABM) for Delhi can be created to analyse the interactions between urban freight entities to understand resulting movements of freight delivery vehicles. Such a model can be used to understand cumulative and serialized effects of the decision making by multiple stakeholders to discover effective policy for urban freight related problems. Furthermore, a role-playing game can be developed using an ABM as an interactive tool for urban freight stakeholders to understand the decision-making processes and complexity of urban freight activities. Such a role-playing game can act as platform to experience the complexity and emergence effect of decision-making by different stakeholders. As a bottom-up approach an ABM for Delhi can use variety of data. Amount data used in the model defined the fineness of the model and therefore, the complexity of emergence can be studied using ABM with help of limited data. Data collection is the most challenging part of ABM development. Accordingly, in the beginning, a proof-of-concept model should be developed for a zone in Delhi. The conceptual framework provided in this report may be a good starting point for model development. The framework describes different stages that are comprehensive in nature and can be treated as a guide for the systematic development of an agent based model for the urban freight domain of Delhi. Data collection for small area The World Bank Group Page | 130 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report should be relatively easy and such small model allows understanding other challenges (e.g. research competencies) that one will face during ABM development. In conclusion, agent-based modelling can be useful for many unrequited policy analysis problems in the urban freight domain. However, the time and precision required makes the development of such a system a challenging task. Overcoming these challenges requires painstaking efforts but assures in-depth understanding about urban freight transportation process for successful urban freight policy analysis. References Delhi Air Pollution: Real-time Air Quality Index (2017). http://aqicn.org/city/delhi/ Air Now (2017). https://airnow.gov/index.cfm?action=aqibasics.aqi MoUD (2016), “Decongesting Traffic in Delhi”, Report of the High Powered Committee, Ministry of Urban Development, Govt. of India, June 2016. Gupta, S. (2017), “Role of Non -Motorized Transport in Distribution of Goods in the Metropolitan City of Delhi”, Transportation Research Procedia, Elsevier Publishers, Vol. 25, pp. 978–984 Nilanjana De-Bakshi, Nomesh B. Bolia, Geetam Tiwari and Jose Holguin-Veras, (2017) “Urban Freight in Delhi: Characteristics and Mobility Restrictions”, VREF Research Brief 8 (RB08-2017), Volvo Research and Educational Foundations (VREF). Nilesh Anand, David Meijer, J.H.R. van Duin, Lóránt Tavasszy, Sebastiaan Meijer, (2016) “Validation of an agent based model using a participatory simulation gaming approach: the case of city logistics”, Transportation Research Part C: Emerging Technologies, Vol. 71 (2016), pp. 489-499. Arend Ligtenberga, Ron J.A.van Lammerena, Arnold K.Bregta and Adrie J.M.Beulensb (2010), “Validation of an agent-based model for spatial planning: A role-playing approach”, Computers, Environment and Urban Systems, Vol. 34 (5), 2010, pp. 424-434. The World Bank Group Page | 131 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report The World Bank Group Page | 132 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report 9. CONCLUDING REMARKS Understanding and forecasting freight movements is critical to plan for future transportation in terms of capacity augmentation, operation, preservation, safety and security, energy and economy investment needs. Many demand forecasting models and data sources are more appropriate for passenger transportation than for freight transportation in terms of understanding freight travel behaviour and forecasting freight movements. Creating better data and models is needed to enable planners to better predict freight movement and design better informed policies. In view of this, the present study have been conceptualised on Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG). An important goal of the project is to create an impact in practice. An extensive pilot study is carried out for the city of New Delhi, India with a transferable modelling approach. The city of New Delhi i.e. National Capital Territory of Delhi (NCTD) has been selected as study area for this study. By conducting extensive field surveys, metrics of city logistics, design of measurement system and data acquisition in the city of Delhi have been developed. The metrics are focused on logistics activity indicators (external and internal flows), logistics efficiency (vehicular and trip characteristics) and city livability (traffic loads in terms of vehicle kilometers travelled and emissions of pollutants). The activities carried out in the present study include:  Development of city logistics metrics - measurements of key performance indicators of a city, in relation to freight transportation and logistics, Quantification of Freight Traffic in the city of Delhi and their vehicular and travel characteristics;  Capacity Development for Sustainable City Logistics - assessment carried out among key stakeholders, including public and private parties. Evaluation including policy objectives, work programmes and general knowledge position in the field of sustainable logistics for megacities;  Development of freight transport demand model and logistics flow model for the city of New Delhi - visualization of the city logistics metrics for monitoring purposes done in 3D city model; The World Bank Group Page | 133 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report  Knowledge Sharing - organised short courses on sustainable city logistics (strategies, metrics, and tools), stakeholders meetings and national dissemination workshops; In the present study, NCT of Delhi has been taken as study area and measured possible freight metrics from the various field studies and the summary is given below:  The journey speed of traffic stream is varying between 17 and 40 kmph and average journey speeds are around 27 kmph on the road network of Delhi. The journey times are around 2.3 minutes per km which shows that the road network of Delhi is moderately congested all the time.  On a normal working day, a total of about 1.24 million vehicles enter and leave Delhi city which has grown with 3% per annum (about 1.02 million vehicles in 2009). The freight traffic forms about 10% of the total traffic with another 4% of traffic is composed of slow moving vehicles like bicycle, cycle rickshaws, animal carts etc.  Maximum number of vehicles in the order of about 354 thousands entering and exiting through Rajokri Border followed by Ghazipur Border with an entry/ exit traffic volume of about 163 thousands and Kalindi Kunj Border with an entry/ exit traffic volume of about 126 thousands.  A total of about 100 Thousand freight vehicles enter and leave Delhi city on a normal working day and about 21% of these freight vehicles are found to be passing through the city which was almost same in 2009. Though the total traffic increased, freight traffic remain stagnated at outer cordons because of new bypass roads come around the city of Delhi such as Noida-Greater Noida Expressway, Yamuna Expressway, Kundli-Manesar-Palwal (KMP) Expressway etc.  The freight vehicle types namely Goods Auto (GA), Goods Van (GV), LT, HT and MT are found at entry and exit locations of outer cordons. In case of passing through freight traffic, HT has almost 50% share followed by MT and LT has share of about 18% each. Smaller Goods Vehicles (GA and GV) has a share of about 14% of passing through traffic. This can be attributed to the fact that the heavy vehicles travel long distances compared to light and small vehicles.  From focal points studies within the city, it has been observed that maximum number of vehicles per day is in the order of about 8 thousands entering and exiting through Ghanta Ghar Sabzi Mandi followed by Azadpur Sabzi Mandi with an entry/ exit The World Bank Group Page | 134 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report volume of about 7 thousands and Chandini Chowk Area with an entry/ exit volume of about 5 thousands. It has also been found that about 40% are consisting of Goods Auto (GA) and Goods Van (GV) in that. The vehicle types of LT, HT and MT are in the range of 24%, 11% and 8% respectively. The other freight vehicles are about 18%.  The mid block traffic studies reveled that the total daily volume (24 hours) on Ring Road (Naraina) is almost 190 thousands with a peak volume of about 16 thousands (19:00 ~ 20:00 Hrs). The summary of traffic on all the mid block locations shows about 80% are consisting of private vehicles mainly cars and two wheelers. The freight transport is about 7% mainly consist of Goods Autos, LT, HT and MT.  The mean age of different freight vehicles is almost same at outer cordons and within the city varying between 4.5 and 5.0 years and the share of 10 year and more old vehicles within the city is ranging from 1 to 6% and 5 to 9% at outer cordons.  The fuel usage distribution of different freight vehicles at outer cordons and within the city results shows that Heavy Vehicles (HT and MT) mostly use Diesel where as Goods Auto and Goods Van almost use CNG as fuel. In case of LT, about 45% and 75% use Diesel as fuel at outer cordons and within city respectively.  The ownership of different freight vehicles at outer cordons and within the city has been analysed and found that private company freight vehicles are high in case of heavy vehicles (HT and MT) at outer cordons and within the city. The private company and personal freight vehicle share is almost same for light vehicles (LT, GA and GV) within the city whereas private company freight vehicle share is higher at outer cordons.  The mileage (fuel efficiency in terms of km/litre) of different freight vehicles has been observed that light vehicles (LT, GA and GV) have higher fuel efficiency which are mostly run on CNG. Heavy freight vehicles have fuel efficiency about 6.5 and 4.8 km/litre for HT and MT respectively. Light vehicles namely LT has about 11 km/litre, where as GA and GV has more than 14 km/kg of CNG.  The freight vehicle types travels about 20-25 km within the city and the maximum average distance travelled in a day by these freight vehicle types is about 200 km. This clearly indicate that these freight vehicles face lot of congestion and other The World Bank Group Page | 135 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report problems to travel more distances in a day experiencing lot of delays and increased operating costs.  The frequency of trips of different freight vehicles analysis shows that Light Vehicles are having more daily trips and Heavy Vehicles are more in Occasional trips.  From the results of weight carried by different freight vehicles, it has been observed that MT Vehicles are carrying average weight more than 13 tonne where as HT vehicle is carrying average load of 5-6 tonne. The LT is carrying average weight about 2 tonne and smaller vehicles like GA and GV are carrying less than a tonne.  The share of empty vehicles is about 10-20% across different freight vehicle types. Further the total weight carried by these freight vehicles on the entire road network of Delhi has been estimated to be about 2.480 Million Metric Tonne (MMT) per day.  In the present study, freight transport demand model has been developed considering traditional approach of four-stage modelling (Freight Trip Generation, Freight Modal Split, Freight Trip Distribution and Freight Traffic Assignment). Accordingly, the total trips generated daily in the city of Delhi from all the zones are estimated to be about 500 thousands of freight trips. The final freight modal split for different freight vehicles namely GA, LT, HT and MT shows almost equal share varying between 22- 25% where as GV has about 5% share. The Freight O-D Matrix estimated from Freight Trip Distribution adopting Gravity Model.  The majority of freight trips are Internal - Internal (I-I) which is almost 80%. The Internal-External (I-E) and External-Internal (E-I) are almost same about 8% each and External-External (E-E) trips (passing through) are about 4%.  The analysis of modal split of these freight trips shows that heavy freight vehicle share is about 26% in case of I-I Trips, about 43% in case of I-E Trips, about 53% in case of E-I Trips and about 61% in case of E-E Trips.  The share of freight trips is only about 3% and passenger trips are about 97% in the city of Delhi. Though the share of freight trips is very insignificant, it is going to influence huge in traffic congestion, air pollution and road safety related issues of the city of Delhi.  The freight trips are estimated to increase to about 572 thousands by the year 2021 with a growth rate of 4% per annum. The World Bank Group Page | 136 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report  The estimated traffic loads in terms of vehicle kilometers travelled (VKT) on the road network of Delhi for the year 2017 and forecasted VKT for the year 2021 are about 240 Millions and 300 Millions respectively. The VKT by freight vehicles are going to be about 10 Million and 13 Millions in 2017 and 2020 respectively which is having a share of about 4%. The growth of total VKT is increasing with 7% per annum growth whereas freight vehicles growth is about 8% per annum. Taking into account the findings from the inventory of the literature, a list of indicators to measure New Delhi’s performance in the area of SCL has been proposed which also included suggested units and sources for measurement. A questionnaire has been designed and proposed to use the same for assessment of the level of knowledge in the area of SCL among all the local authorities and policy makers, freight operators and experts. With respect to decision support systems, the Urban Strategy (Software developed by TNO) has been customised to visualise freight patterns and to model the impacts of different traffic measures in the city of Delhi. In the present study, it has been demonstrated that Urban Strategy is able to use the available data to construct a basic working model and distribute traffic on the basis of that model. The appropriate data collected in this study has been successfully uploaded, and in combination with open-source data from OpenStreetMaps, Urban Strategy has been used to carry out an initial traffic assignment, and the results displayed in the 2D and 3D interfaces and the Web interface. It can be conclude that the developed system can be utilised as decision support system to evaluate various transport policies by estimating traffic loads and emission loads from vehicular traffic. The following findings have emerged in the present study through the development of the decision support system for the city of Delhi and evaluation of different scenarios:  Based on the results of estimated traffic loads and emission loads from vehicular traffic, it can be said that the contribution of passenger car movements to road transportation emissions is dominant in comparison to road freight movements.  It has been shown that a Decision Support System on the basis of Urban Strategy is feasible for Delhi. Although a number of topics for further research are identified, the The World Bank Group Page | 137 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report study and system has already provided insight in the status quo and the effect of policy scenarios.  On the basis of these findings it appears that some measures, such as freight hubs, will only be effective if they are combined with measures to lower fleet emissions, such as the use of electric vehicles.  Removal of diesel vehicles older than 10 years shows 4 - 11% decrease in total COx, NOx and PM10 emissions, and negligible difference in Benzene or Hydrocarbon levels caused by freight traffic.  Freight hubs and heavy vehicle restrictions may lead to increased overall emissions if there is no change to the emissions profile for the vehicles that replace them.  Introduction of electric freight vehicles shows promising results for reduction in emissions, dependent on the penetration rate achieved.  The impact of measures only targeting freight movements will be limited, due to its relatively small contribution to air pollution. Therefore, it would be valuable to apply this system on the integrated challenge of the city of Delhi with regards to air pollution and traffic noise. More in general: balancing between urban planning, mobility planning, environmental planning in order to accommodate (economic) growth and improve the quality of life of its citizens. In the present study, an attempt has been made to see the feasibility to apply Agent Based Modelling (ABM) for City Logistics. A large variety of activities (e.g. freight vehicle movements, parking, loading/unloading goods) and stakeholders (consumer, retail, forwarding, trade, manufacturing) is associated with urban freight transportation, which differ with respect to location, types of goods and stakeholders’ characteristics. Altogether this creates a complex system that is difficult to manage. The emerging system is a direct result of colliding decisions and often conflicting objectives of different stakeholders. A well-designed agent based modelling approach that includes the business models and perception of multiple stakeholders of the domain would be useful to identify effective solutions (e.g. policy, regulation, facilitating schemes) for the above mentioned problems. By collecting and synthesizing information about individual stakeholders and entities at micro level, an agent-based model (ABM) for Delhi can be created to analyse the interactions The World Bank Group Page | 138 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report between urban freight entities to understand the background of movements of freight delivery vehicles and responses of these agents to policies for urban freight related problems. Furthermore, a role-playing game can be developed using an ABM as an interactive tool for urban freight stakeholders to understand the decision-making processes and complexity of urban freight activities. Such a role-playing game can act as platform to experience the complexity and emergence effect of decision-making by different stakeholders. As data collection is challenging part of ABM development, we recommend that, initially, a proof-of- concept model is developed for a zone in Delhi. A conceptual framework provided in this report as starting point for model development. Data collection for a small area should be relatively easy and allow addressing the research challenges of ABM development. From the above, it can be said that agent-based modelling can be useful for many unrequited policy analysis problems in the urban freight domain. However, the time and precision required for developing such a system is a challenging task. Overcoming these challenges requires painstaking efforts but assures in-depth understanding about urban freight transportation process for successful urban freight policy analysis. In conclusion, in the present study, four important priorities for the future have been identified, which could be part of a joint mission statement of the collective of stakeholders to achieve sustainable urban freight systems:  Reduction of negative effects of urban freight transport while maintaining productivity.  Identification of workable urban freight solutions including roadmaps towards data, tools and appropriate research.  Increase of the knowledge base including data collection, models and scenarios.  Collaboration with other stakeholders to realize solutions towards sustainability. In the final workshop of the project, these points were signed symbolically by all participants, as an expression of the start of a shared effort to create a follow-up to this project including further elaborated policy information, based on a process of joint fact finding and alignment of ideas by industry, governmental and knowledge partners. The World Bank Group Page | 139 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report The World Bank Group Page | 140 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report 10. ANNEXURES Appendix 1 Number of Registered Vehicles in New Delhi Vehicles registered upto 31-dec-2016 class-wise (Excl. NOC taken/ Scarpped Vehicle/ Surrendered RC/ RC Cancellation) Class description No. of Veh. Agricultural Tractor 206 Ambulance 3035 Bus 35046 Cash Van 23 Construction Equipment Vehicle 1 Crane Mounted Vehicle 285 Educational Institution Bus 2 e-Rickshaw(P) 24958 e-Rickshaw with Cart (G) 1 Fire Fighting Vehicle 10 Goods Carrier 221003 Invalid Carriage 597 Luxury Cab 2255 Maxi Cab 30128 M-Cycle/Scooter 6340136 M-Cycle/Scooter-With Side Car 913 Mobile Workshop 19 Moped 113375 Motor Cab 111445 Motor Car 3044883 Motor Cycle/Scooter-With Trailer 1 Motorised Cycle (CC > 25cc) 8 Omni Bus 43 Omni Bus (Private Use) 4 Private Service Vehicle (Individual Use) 12 Recovery Vehicle 620 Three Wheeler (Goods) 66741 Three Wheeler (Passenger) 104969 Three Wheeler (Personal) 48 Tractor (Commercial) 6024 sum 10106791 (Source: www.delhi.gov.in/wps/wcm/connect/doit_transport/Transport/Home/Statistics) Appendix 2 Estimated Vehicular Emission Load in Delhi (in 2009) Pollutant Pollution load (in ton/day) Carbon Monoxide 391.52 Hydrocarbons 181.90 Nitrogen oxides 126.23 Particulate Mater 14.13 (Source: CRRI, 2009) The World Bank Group Page | 141 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Appendix 3 The World Bank Group Page | 142 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report The World Bank Group Page | 143 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report The World Bank Group Page | 144 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report The World Bank Group Page | 145 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report The World Bank Group Page | 146 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report The World Bank Group Page | 147 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report The World Bank Group Page | 148 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report The World Bank Group Page | 149 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Appendix 4 Table A4.1: Observed Journey Speed on Tees Hazari to Kundli (S-1) in Up Direction S. Avg. Speed Name of the Section Distance (Km) No (Km/Hr) 1 Tees Hazari to Mori Gate Crossing 0.9 10.59 2 Mori Gate Crossing to ISBT 1.0 19.34 3 ISBT to Matkaf 1.0 43.97 4 Matkaf to Majnu Ka Tilla 2.0 38.56 5 Majnu Ka Tilla to Nehru Vihar 1.4 38.12 6 Nehru Vihar to Gopal Pur Crossing 1.1 17.79 7 Gopal Pur Crossing to Burari Crossing 2.6 25.24 8 Burari Crossing to Mukund Pur Crossing 1.5 28.23 9 Mukund Pur Crossing to Mubarka Chowk 2.8 49.85 10 Mubarka Chowk to Swaroop Nagar 2.4 59.54 11 Swaroop Nagar to Budhpur 2.7 53.31 12 Budhpur to Palla More 2.4 12.90 13 Palla More to Khampur 2.6 63.22 14 Khampur to Singhola Crossing 2.0 49.48 15 Singhola Crossing to Kundli 2.5 40.39 Total 28.9 36.70 Table A4.2: Observed Journey Speed on Kundli to Tees Hazari (S-1) in Down Direction S. Avg. Speed Name of the Section Distance (Km) No (Km/Hr) 1 Kundli to Singhola Crossing 2.5 18.33 2 Singhola Crossing to Khampur 2.0 48.43 3 Khampur to Palla More 2.6 18.44 4 Palla More to Budhpur 2.4 26.41 5 Budhpur to Swaroop Nagar 2.7 47.84 6 Swaroop Nagar to Mubarka Chowk 2.4 59.32 7 Mubarka Chowk to Mukund Pur Crossing 2.8 39.73 8 Mukund Pur Crossing to Burari Crossing 1.5 13.22 9 Burari Crossing to Gopal Pur Crossing 2.6 28.29 10 Gopal Pur Crossing to Nehru Vihar 1.1 29.23 11 Nehru Vihar to Majnu Ka Tilla 1.4 37.28 12 Majnu Ka Tilla to Matkaf 2.0 29.27 13 Matkaf to ISBT 1.0 41.24 14 ISBT to Mori Gate Crossing 1.0 42.21 15 Mori Gate Crossing to Tees Hazari 0.9 40.68 Total 28.9 34.66 The World Bank Group Page | 150 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Table A4.3: Observed Journey Speed on Prashant Vihar (Ring Road Crossing) to Ferozpur Bangar (S-2) in Up Direction S. Avg. Speed Name of the Section Distance (Km) No (Km/Hr) 1 Prasant Vihar to Vidhya Marg Crossing 1.36 15.52 2 Vidhya Marg Crossing to DTC Rohini Depot 0.92 19.11 3 DTC Rohini Depot to BPIT 1.13 24.10 4 BPIT to St. Xavier School 1.42 32.71 5 St. Xavier School to Prahladpur 2.00 18.81 6 Prahladpur to Prahladpur School 0.90 26.82 7 Prahladpur School to Anand Vihar Barwala 1.90 20.06 8 Anand Vihar Barwala to Poot Khurd 1.25 30.96 9 Poot Khurd to Bawana Gas Agency 1.50 14.08 10 Bawana Gas Agency to Bawana Sec 1 1.25 43.66 11 Bawana Sec 1 to Bawana Chowk 1.04 23.62 12 Bawana Chowk to Daryapur 2.80 35.50 13 Daryapur to Ferozpur Bangar 3.10 25.18 Total 20.57 25.39 Table A4.4: Observed Journey Speed on Ferozpur Bangar to Prashant Vihar (Ring Road Crossing) (S-2) in Down Direction S. Avg. Speed Name of the Section Distance (Km) No (Km/Hr) 1 Ferozpur Bangar to Daryapur 3.10 32.46 2 Daryapur to Bawana Chowk 2.80 35.91 3 Bawana Chowk to Bawana Sec 1 1.04 17.00 4 Bawana Sec 1 to Bawana Gas Agency 1.25 44.65 5 Bawana Gas Agency to Poot Khurd 1.50 22.43 6 Poot Khurd to Anand Vihar Barwala 1.25 27.90 7 Anand Vihar Barwala to Prahladpur School 1.90 26.50 8 Prahladpur School to Prahladpur 0.90 22.71 9 Prahladpur to St. Xavier School 2.00 18.96 10 St. Xavier School to BPIT 1.42 35.08 11 BPIT to DTC Rohini Depot 1.13 27.05 12 DTC Rohini Depot to Vidya Marg Crossing 0.92 38.14 13 Vidya Marg Crossing to Prasant Vihar 1.36 16.73 Total 20.57 28.12 The World Bank Group Page | 151 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Table A4.5: Observed Journey Speed on GT Road Shahadara to Bahadurgarh (S-3) in Up Direction S. Avg. Speed Name of the Section Distance (Km) No (Km/Hr) 1 Shahdara to Jhilmil Crossing 0.40 8.90 2 Jhilmil Crossing to Mansarovar Park 1.29 24.55 3 Mansarovar Park to Kranti Nagar Extension 2.20 34.82 4 Kranti Nagar Extension to Parsavnath Metro Mall 1.10 6.69 5 Parsavnath Metro Mall to Shastri Park Crossing 1.40 8.21 6 Shastri Park Crossing to ISBT 2.00 26.10 7 ISBT to Mori Gate Crossing 1.05 37.53 8 Mori Gate Crossing to Lodhi Chowk 1.20 7.40 9 Lodhi Chowk to Sadar Bazaar Station 0.60 11.98 10 Sadar Bazaar Station to New Delhi Railway Station 1.50 6.040 11 New Delhi Railway Station to Paharganj 0.90 8.80 12 Paharganj to Jhandewalan 0.50 5.60 13 Jhandewalan to Guru Govind Singh Crossing 0.90 11.73 14 Guru Govind Singh Crossing to Karol Bhag 0.60 17.39 15 Karol Bhag to Ordinance Depot 1.52 20.13 16 Ordinance Depot to Multan Nagar 0.89 14.63 17 Multan Nagar to Peeragarhi Chowk 1.00 10.65 18 Peeragarhi Chowk to Jwalapur 1.46 9.33 19 Jwalapur to Police Station Nangoli 1.66 10.23 20 Police Station Nangoli to Nangoli Colony 1.41 17.36 21 Nangoli Colony to Mundka Crossing 1.83 14.56 22 Mundka Crossing to Mundka Main Road 1.82 36.56 23 Mundka Main Road to Ghevra Mor 2.23 14.56 24 Ghevra Mor to Sarvodaya Kanya Vidyalaya 2.92 36.56 25 Sarvodaya Kanya Vidyalaya to Nh-10 1.79 26.36 26 Nh-10 to Old Sabzi Mandi 1.49 29.63 27 Old Sabzi Mandi to Bahadurgarh 1.28 15.23 Total 36.94 17.46 The World Bank Group Page | 152 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Table A4.6: Observed Journey Speed on GT Road Bahadurgarh to Shahadara (S-3) in Down Direction S. Avg. Speed Name of the Section Distance (Km) No (Km/Hr) 1 Bahadurgarh to Old Sabzi Mandi 1.28 10.61 2 Old Sabzi Mandi to Nh-10 1.49 28.59 3 Nh-10 to Sarvodaya Kanya Vidyalaya 1.79 27.41 4 Sarvodaya Kanya Vidyalaya to Ghevra Mor 2.92 41.44 5 Ghevra Mor to Mundka Main Road 2.23 7.05 6 Mundka Main Road to Mundka Crossing 1.82 33.63 7 Mundka Crossing to Nangoli Colony 1.83 15.86 8 Nangoli Colony to Police Station Nangoli 1.41 15.68 9 Police Station Nangoli to Jwalapur 1.66 6.33 10 Jwalapur to Peeragarhi Chowk 1.46 7.55 11 Peeragarhi Chowk to Multan Nagar 1.00 12.35 12 Multan Nagar to Ordinance Depot 0.89 15.65 13 Ordinance Depot to Karol Bhag 1.52 22.6 14 Karol Bhag to Guru Govind Singh Crossing 0.60 11.73 15 Guru Govind Singh Crossing to Jhandewalan 0.90 10.62 16 Jhandewalan to Paharganj 0.50 4.11 17 Paharganj to New Delhi Railway Station 0.90 11.63 18 New Delhi Railway Station to Sadar Bazaar Station 1.50 4.41 19 Sadar Bazaar Station to Lodhi Chowk 0.60 3.64 20 Lodhi Chowk to Mori Gate Crossing 1.20 10.88 21 Mori Gate Crossing to ISBT 1.05 16.74 22 ISBT to Shastri Park Crossing 2.00 36.95 23 Shastri Park Crossing to Parsavnath Metro Mall 1.40 22.09 24 Parsavnath Metro Mall to Kranti Nagar Extension 1.10 10.39 25 Kranti Nagar Extension to Mansarovar Park 2.20 28.45 26 Mansarovar Park to Jhilmil Crossing 1.29 31.98 27 Jhilmil Crossing to Shahdara 0.40 13.18 Total 36.94 17.09 The World Bank Group Page | 153 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Table A4.7: Observed Journey Speed on Rajeev Chowk (Connaught Place) to Dhansa (Najafgarh Road) (S-4) in Up Direction S. Avg. Speed Name of the Section Distance (Km) No (Km/Hr) 1 Rajiv Chowk to Rama Krishna Marg 1.25 9.19 2 Rama Krishna Marg to Videocon tower 0.60 13.40 3 Videocon tower to Rani Jhansi 0.40 9.59 4 Rani Jhansi to Idgah 0.65 22.15 5 Idgah to Railway Over Bridge 2.50 11.71 6 Railway Over Bridge to Zakhira Village 1.65 18.01 7 Zakhira Village to Ashoka Park 1.05 35.74 8 Ashoka Park to Zakhira 1.30 10.48 9 Zakhira to Moti Nagar 2.00 27.32 10 Moti Nagar to Raja Garden 2.25 11.48 11 Raja Garden to Subhash Nagar 1.90 21.41 12 Subhash Nagar to Tilak Nagar 1.25 24.68 13 Tilak Nagar to Janakpuri 1.50 15.69 14 Janakpuri to Uttamnagar Bus Depot 1.50 9.36 15 Uttamnagar Bus Depot to Dwaraka More 3.00 8.50 16 Dwaraka More to Indira Park 3.00 23.34 17 Indira Park to Najafgarh 1.50 11.28 18 Najafgarh to Dansa Stand 1.10 20.43 19 Dansa Stand to Desu Office Mitraun 2.00 28.46 20 Desu Office Mitraun to Surhera Crossing 2.00 25.92 21 Surhera Crossing to Rawta Crossing 1.65 44.18 22 Rawta Crossing to Mundhela Crossing 2.00 41.05 23 Mundhela Crossing to Kazipur 2.50 33.59 24 Kazipur to Issapur Crossing 1.00 40.49 25 Issapur Crossing to Dansa Village 1.00 44.27 26 Dansa Village to Dansa Border 2.50 35.22 Total 43.05 22.96 The World Bank Group Page | 154 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Table A4.8: Observed Journey Speed on Dhansa (Najafgarh Road) to Rajeev Chowk (Connaught Place) (S-4) in Down Direction S. Avg. Speed Name of the Section Distance (Km) No (Km/Hr) 1 Dansa Border to Dansa Village 2.50 29.19 2 Dansa Village to Isapur Crossing 1.00 37.83 3 Issapur Crossing to Kazipur 1.00 43.15 4 Kazipur to Mundhela Crossing 2.50 40.28 5 Mundhela Crossing to Rawta Crossing 2.00 42.21 6 Rawta Crossing to Surhera Crossing 1.65 38.72 7 Surhera Crossing to Desu Office Mitraun 2.00 40.92 8 Desu Office Mitraun to Dansa Stand 2.00 27.68 9 Dansa Stand to Najafgarh 1.10 26.39 10 Najafgarh to Indira Park 1.50 7.45 11 Indira Park to Dwaraka More 3.00 17.34 12 Dwaraka More to Uttam Nagar Bus Depot 3.00 14.06 13 Uttam Nagar Bus Depot to Janakpuri 1.50 6.57 14 Janakpuri to Tilak Nagar 1.50 9.22 15 Tilak Nagar to Subhash Nagar 1.25 11.77 16 Subhash Nagar to Raja Garden 1.90 12.38 17 Raja Garden to Moti Nagar 2.25 18.48 18 Moti Nagar to Zakhira 2.00 14.81 19 Zakhira to Ashoka Park 1.30 14.54 20 Ashoka Park to Zakhira Village 1.05 28.78 21 Zakhira Village to Railway Over Bridge 1.65 27.27 22 Railway Over Bridge to Idgah 2.50 9.46 23 Idgah to Rani Jhansi 0.65 14.38 24 Rani Jhansi Ti Videocon tower 0.40 8.60 25 Videocon tower to Rama Krishna Marg 0.60 11.80 26 Rama Krishna Marg to Rajiv Chowk 1.25 17.18 Total 43.05 21.94 The World Bank Group Page | 155 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Table A4.9: Observed Journey Speed on Rajokri Border (Delhi –Gurgaon Expressway) to Rajeev Chowk (Connaught Place) (S-5) in Up Direction S. Avg. Speed Name of the Section Distance (Km) No (Km/Hr) 1 Rajokri Border to Rose Garden Extension 2.2 47.90 2 Rose Garden Extension to Mahipalpur 0.8 68.92 3 Mahipalpur to Mahipalpur Extension 0.7 78.67 4 Mahipalpur Extension to Shankar Vihar 2.5 26.09 5 Shankar Vihar to APS 1.2 27.82 6 APS to Suborto Park 2.0 34.07 7 Suborto Park to Dhaula Kuan 1.3 46.36 8 Dhaula Kuan to Simon Bolivar Margh 2.0 38.71 9 Simon Bolivar Margh to Sardar Patel Crossing 1.3 21.63 10 Sardar Patel Crossing to Dr. Rani Hospital 2.0 47.60 11 Dr. Rani Hospital to St. Colombus School 0.9 19.05 12 St. Colombus School to Rajeev Chowk 1.2 21.71 Total 18.1 39.87 Table A4.10: Observed Journey Speed on Rajeev Chowk (Connaught Place) to Rajokri Border (Delhi –Gurgaon Expressway) (S-5) in Down Direction S. Avg. Speed Name of the Section Distance (Km) No (Km/Hr) 1 Rajeev Chowk to St. Colombus School 1.2 5.90 2 St. Colombus School to Dr. Rani Hospital 0.9 27.28 3 Dr. Rani Hospital to Sardar Patel Crossing 2.0 14.20 4 Sardar Patel Crossing to Simon Bolivar Margh 1.3 9.60 5 Simon Bolivar Margh to Dhaula Kuan 2.0 16.23 6 Dhaula Kuan to Subroto Park 1.3 45.56 7 Subroto Park to APS 2.0 55.95 8 APS to Shankar Vihar 1.2 56.18 9 Shankar Vihar to Mahipalpur Extension 2.5 66.05 10 Mahipalpur Extension to Mahipalpur 0.7 65.41 11 Mahipalpur to Rose Garden Extension 0.8 66.60 12 Rose Garden Extension to Rajokri Border 2.2 15.02 Total 18.1 36.99 The World Bank Group Page | 156 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Table A4.11: Observed Journey Speed on Aurobindo Marg to Arjan Garh (Aya Nagar Border) (S-6) in Up Direction S. Avg. Speed Name of the Section Distance (Km) No (Km/Hr) 1 Aurobindo Marg to Bhismpitama Margh 1.40 16.37 2 Bhismpitama Margh to Lodhi Road Crossing 0.51 16.17 3 Lodhi Road Crossing to Jor Bagh Metro Station 1.07 5.58 4 Jor Bagh Metro Station to Shri Ganga Nath Margh 1.09 14.44 5 Shri Ganga Nath Margh to INA Colony 1.03 23.83 6 INA Colony to Ina Metro Station 1.25 9.59 7 Ina Metro Station to Kidwai Nagar Metro Station 0.18 14.47 8 Kidwai Nagar Metro Station to Near AIIMS Metro 1.54 43.21 9 Near AIIMS Metro Station to Yusuf Sarai Metro 1.01 31.48 10 Yusuf Sarai Metro Station to Green Park Metro 0.56 51.72 11 Green Park Metro to Green Park 0.71 31.96 12 Green Park to Hauz Khas Metro Stn 0.47 42.06 13 Hauz Khas Metro Stn to IIT Delhi Gate 1.49 14.60 14 IIT Delhi Gate to Near Qutab Hotel 0.73 57.32 15 Near Qutab Hotel to Near Pts & Lado Sarai 0.33 16.95 16 Near Pts & Lado Sarai Crossing to Mosque Qutab 0.26 5.22 17 Mosque Qutab to Arjangarh Metro Station 5.83 15.42 Total 19.46 24.14 Table A4.12: Observed Journey Speed on Arjan Garh (Aya Nagar Border) to Aurobindo Marg (S-6) in Down Direction S. Avg. Speed Name of the Section Distance (Km) No (Km/Hr) 1 Arjangarh Metro Station to Mosque Qutab 5.83 14.45 2 Mosque Qutab to Lado Sarai Crossing 0.26 6.52 3 Lado Sarai Crossing to Near Qutab Hotel 0.33 17.95 4 Near Qutab Hotel to IIT Delhi Gate 0.73 52.60 5 IIT Delhi Gate to Hauz Khas Metro Stn 1.49 13.45 6 Hauz Khas Metro Stn to Green Park 0.47 40.12 7 Green Park to Green Park Metro 0.71 32.45 8 Green Park Metro to Yusuf Sarai Metro Stn 0.56 45.46 9 Yusuf Sarai Metro Stn to Near AIIMS Metro 1.01 30.46 10 Near AIIMS Metro to Kidwai Nagar Metro Stn 1.54 38.21 11 Kidwai Nagar Metro Station to INA Metro Stn 0.18 15.06 12 INA Metro Station to INA Colony 1.25 10.94 13 INA Colony to Shri Ganga Nath Margh 1.03 24.63 14 Shri Ganga Nath Margh to Jor Bhag Metro Stn 1.09 15.23 15 Jor Bhag Metro Station to Lodhi Road Crossing 1.07 6.23 16 Lodhi Road Crossing to Bhismpitama Margh 0.51 15.23 17 Bhismpitama Margh to Aurobindo Marg 1.40 14.66 Total 19.46 23.15 The World Bank Group Page | 157 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Table A4.13: Observed Journey Speed on Badarpur Border to Rajeev Chowk (Connaught Place) (S-7) in Up Direction S. Avg. Speed Name of the Section Distance (Km) No (Km/Hr) 1 Badarpur Border to Aali Gaon 2.14 26.06 2 Aali Gaon to Madanpur Khadar 1.54 35.19 3 Madanpur Khaddar to Apollo Hospital 1.40 31.39 4 Apollo Hospital to Kalka Mor 2.30 34.76 5 Kalka Mor to Ashram 2.00 15.36 6 Ashram to Hazrat Nizamuddin 2.20 16.17 7 Hazrat Nizamuddin to Golf Club 2.00 26.09 8 Golf Club to Baroda House 2.20 19.91 9 Baroda House to Rajeev Chowk 2.00 15.81 Total 17.78 24.52 Table A4.14: Observed Journey Speed on Rajeev Chowk (Connaught Place) to Badarpur Border (S-7) in Down Direction S. Avg. Speed Name of the Section Distance (Km) No (Km/Hr) 1 Rajeev Chowk to Baroda House 2.00 18.50 2 Baroda House to Golf Club 2.20 40.73 3 Golf Club to Hazrat Nizamuddin 2.00 24.61 4 Hazrat Nizamuddin to Ashram 2.20 10.15 5 Ashram to Kalka Mor 2.00 12.39 6 Kalka Mor to Apollo Hospital 2.30 35.33 7 Apollo Hospital to Madanpur Khaddar 1.40 26.39 8 Madanpur Khaddar to Aali Gaon 1.54 20.30 9 Aali Gaon to Badarpur Border 2.14 20.90 Total 17.78 23.25 The World Bank Group Page | 158 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Table A4.15: Observed Journey Speed on Inner Ring Road (Ashram Chowk to Ashram Chowk) (S-8) in Up Direction S. Avg. Speed Name of the Section Distance (Km) No (Km/Hr) 1 Ashram Chowk to Vikas Marg 3.83 29.26 2 Vikas Marg to Rose Garden 3.48 24.73 3 Ross Garden to Vijayghat 0.75 53.56 4 Vijayghat to Indira Gandhi IT 2.76 29.11 5 Indira Gandhi IT to Matkaf Metro Station 1.98 22.29 6 Matkaf Metro Station to Naya Azadpur 0.99 15.69 7 Naya Azadpur to Pitampura 6.61 20.95 8 Pitampura to Britania Chowk 2.11 53.48 9 Britania Chowk to Punjabi Bagh Chowk 3.60 14.01 10 Punjabi Bagh Chowk to Punjabi Bagh Bus Stop 0.64 12.03 11 Punjabi Bagh Bus Stop to Sardana Eye Institute 2.42 24.90 12 Sardana Eye Institute to Army Medical College 4.59 32.53 13 Army Medical College to Sardar Patel Marg 3.48 32.51 14 Sardar Patel Marg to Safdarjung 3.68 20.77 15 Safdarjung to AIIMS Metro 1.90 52.32 16 AIIMS Metro to Moolchand Metro Station 2.54 26.21 17 Moolchand Metro Station to Ashram Chowk 2.59 30.12 Total 47.95 29.09 Table A4.16: Observed Journey Speed on Inner Ring Road (Ashram Chowk to Ashram Chowk) (S-8) in Down Direction S. Avg. Speed Name of the Section Distance (Km) No (Km/Hr) 1 Ashram Chowk to Moolchand Metro Station 2.59 34.71 2 Moolchand to AIIMS Metro Station 2.54 15.56 3 AIIMS to Safdarjung Metro Station 1.90 40.96 4 Safdarjung to Sardar Patel Marg 3.68 35.52 5 Sardar Patel Marg to Army Medical School 3.48 43.72 6 Army Medical College to Sardana Eye Institute 4.59 50.07 7 Sardana Eye Institute to Punjabi Bagh Bus Stop 2.42 39.42 8 Punjabi Bagh Bus Stop to Punjabi Bagh Chowk 0.64 51.23 9 Punjabi Bagh Chowk to Britania Chowk 3.60 40.50 10 Britania Chowk to Pitampura 2.11 44.80 11 Pitampura to Naya Azad Pur 6.61 15.72 12 Naya Azadpur to Matkaf Metro Station 0.99 14.96 13 Matkaf Metro to Indra Gandhi I.T 1.98 24.56 14 Indra Gandhi I.T to Vijayghat 2.76 41.33 15 Vijayghat to Rose Garden 0.75 49.68 16 Rose Garden to Vikas Marg 3.48 36.38 17 Vikas Marg to Ashram Chowk 3.83 40.31 Total 47.95 36.43 The World Bank Group Page | 159 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Table A4.17: Observed Journey Speed on Outer Ring Road (Dwarka Mor Metro Station to Kalindi Kunj Border) (S-9) in Up Direction S. Avg. Speed Name of the Section Distance (Km) No (Km/Hr) 1 Dwarka Mor to Palam Vihar 4.50 25.24 2 Palam Vihar to Hanuman Mandir 5.40 38.31 3 Hanuman Mandir to APS Colony 3.00 20.40 4 APS Colony to Ber Sarai 3.80 43.50 5 Ber Sarai to IIT Gate 3.40 21.55 6 Iit Gate to Panchsheel Enclave 0.80 33.65 7 Panchsheel Enclave to Greater Kailash 3.50 32.10 8 Greater Kailash to Nehru Palace 1.15 41.52 9 Nehru Palace to Kalkaji Temple Bus Stop 1.50 23.55 10 Kalkaji Temple Bus Stop to Kalka Mor 1.10 19.64 11 Kalka Mor to Apollo Metro 2.81 21.85 12 Apollo Metro to Kalindi Kunj 2.80 40.61 Total 33.76 30.16 Table A4.18: Observed Journey Speed on Outer Ring Road (Kalindi Kunj Border to Dwarka Mor Metro Station) (S-9) in Down Direction S. Avg. Speed Name of the Section Distance (Km) No (Km/Hr) 1 Kalindi Kunj to Apollo Metro Station 2.8 19.74 2 Apollo Metro Station to Kalka Mor 2.81 23.55 3 Kalka Mor to Kalkaji Temple Bus Stop 1.10 41.52 4 Kalkaji Temple Bus Stop to Nehru Palace 1.50 33.10 5 Nehru Palace to Greater Kailash 1.15 33.34 6 Greater Kailash to Panchsheel Enclave 3.50 21.55 7 Panchsheel Enclave to IIT Gate 0.80 44.23 8 IIT Gate to Ber Sarai 3.40 44.70 9 Ber Sarai to APS Colony 3.80 20.04 10 APS Colony to Hanuman Mandir 3.00 43.88 11 Hanuman Mandir to Palam Vihar 5.40 40.61 12 Palam Vihar to Dwarka Mor 4.50 26.24 Total 33.76 32.70 The World Bank Group Page | 160 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Table A4.19: Observed Journey Speed on GT Road INA to Shahadara (Via Ghazipur, DND, Barapulla Elevated Road) (S-10) in Up Direction S. Avg. Speed Name of the Section Distance (Km) No (Km/Hr) 1 INA to Sewa Nagar Railway 1.74 20.42 2 Sewa Nagar Railway to CGO Complex 1.37 40.13 3 CGO Complex to Nizamuddin 0.70 39.19 4 Nizamuddin to Gurudwara Bangla Sahib 1.72 40.98 5 Gurudwara Bangla Sahib to Mayur Place 4.16 39.38 6 Mayur Place to Kalyanpuri Temple 3.70 21.71 7 Kalyanpuri Temple to Khichdipur Crossing 1.62 20.10 8 Khichdipur Crossing to Gazipur Village 1.06 19.03 9 Gazipur Village to Anand Vihar ISBT 1.08 24.72 10 Anand Vihar ISBT to Shresta Vihar 1.56 39.21 11 Shresta Vihar to Ramprastha Mandir 0.54 40.43 12 Ramprastha Mandir to Shahadara 1.40 37.51 Total 20.65 31.90 Table A4.20: Observed Journey Speed on GT Road Shahadara TO INA (Via Ghazipur, DND, Barapulla Elevated Road) (S-10) in Down Direction S. Avg. Speed Name of the Section Distance (Km) No (Km/Hr) 1 Shahadara to Ramprastha Mandir 1.40 13.44 2 Ramprastha Mandir to Shrestha Vihar 0.54 14.29 3 Shrestha Vihar to Anand Vihar ISBT 1.56 41.76 4 Anand Vihar ISBT to Gazipur Village 1.08 31.19 5 Gazipur Village to Khichdipur Crossing 1.06 44.28 6 Khichdipur Crossing to Kalyanpuri Terminal 1.62 21.69 7 Kalyanpuri Terminal to Mayur Place 3.70 14.30 8 Mayur Place to Gurudwara Bangla Sahib 4.16 34.95 9 Gurudwara Bangla Sahib to Nizamuddin 1.72 33.39 10 Nizamuddin to CGO Complex 0.70 49.52 11 CGO Complex to Sewa Nagar 1.37 44.70 12 Sewa Nagar Railway to Ina 1.74 47.56 Total 20.65 32.58 The World Bank Group Page | 161 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Table A4.21: Observed Journey Speed on Nizamuddin Bridge to Ghazipur (S-11) in Up Direction S. Avg. Speed Name of the Section Distance (Km) No (Km/Hr) 1 Nizamuddin Road Crossing to PWD Office 1.4 34.24 2 PWD Office to Noida More 1.5 44.43 3 Noida More to Patparganj Crossing 0.8 40.13 4 Patparganj Crossing to Trilokpuri 0.8 32.61 5 Trilokpuri to Khichdipur Village 1.4 37.27 6 Khichdipur Village to Khichdipur Crossing 0.9 32.13 7 Khichdipur Crossing to Ghazipur 1.1 21.27 Total 7.9 34.58 Table A4.22: Observed Journey Speed on Ghazipur to Nizamuddin Bridge (S-11) in Down Direction S. Avg. Speed Name of the Section Distance (Km) No (Km/Hr) 1 Ghazipur to Khichdipur Crossing 1.1 25.97 2 Khichdipur Crossing to Khichdipur Village 0.9 38.88 3 Khichdipur Village to Trilokpuri 1.4 46.51 4 Trilokpuri to Patparganj Crossing 0.8 40.25 5 Patparganj Crossing to Noida More 0.8 37.96 6 Noida More to PWD Office 1.5 43.8 7 PWD Office to Nizamuddin Road Crossing 1.4 36.99 Total 7.9 38.62 Table A4.23: Observed Journey Speed on Wazirabad to Mandoli Border (S-12) in Up Direction S. Avg. Speed Name of the Section Distance (Km) No (Km/Hr) 1 Wazirabad to Nanaksha 1.4 5.50 2 Nanaksha to Khajuri Khas 1.6 22.09 3 Khajuri Khas to Bhajanpura 0.7 11.04 4 Bhajanpura to Yamuna Vihar 1.5 20.74 5 Yamuna Vihar to Yamuna Vihar Crossing 0.8 39.01 6 Yamuna Vihar Crossing to Loni Road Crossing 0.8 37.51 7 Loni Road Crossing to Mandoli Sewadham 1.0 30.28 8 Mandoli Sewadham to Gagan Cinema 1.3 23.34 9 Gagan Cinema to Mandoli Border 0.7 15.05 Total 9.8 22.72 The World Bank Group Page | 162 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Table A4.24: Observed Journey Speed on Mandoli Border to Wazirabad (S-12) in Down Direction S. Avg. Speed Name of the Section Distance (Km) No (Km/Hr) 1 Mandoli Border to Gagan Cinema 0.7 22.35 2 Gagan Cinema to Mandoli Sewadham 1.3 30.15 3 Mandoli Sewadham to Loni Road Crossing 1.0 35.48 4 Loni Road Crossing to Yamuna Vihar Crossing 0.8 35.46 5 Yamuna Vihar Crossing to Yamuna Vihar 0.8 41.03 6 Yamuna Vihar to Bhajanpura 1.5 20.23 7 Bhajanpura to Khajuri Khas 0.7 39.16 8 Khajuri Khas to Nanaksha 1.6 6.09 9 Nanaksha to Wazirabad 1.4 3.40 Total 9.8 25.92 Table A4.25: Observed Journey Speed on Loni Border to Old Delhi Railway Station (Via Vikas Marg, Sadar Bazar) (S-13) in Up Direction S. Avg. Speed Name of the Section Distance (Km) No (Km/Hr) 1 Loni Road to Loni Road Crossing 1.1 7.80 2 Loni Road Crossing to Jyoti Nagar 1.3 11.80 3 Jyoti Nagar to DTC Shahadara Terminal 2.1 8.35 4 DTC Shahadara Terminal to Kranti Nagar 1.0 16.72 5 Kranti Nagar to Krishna Nagar A Block 1.2 6.93 6 Krishna Nagar A Block to Radhey Puri 1.1 14.96 7 Radhey Puri to Gagan Vihar 1.1 10.95 8 Gagan Vihar to Nirman Vihar 1.6 11.55 9 Nirman Vihar to Laxmi Nagar Metro Station 1.4 22.38 10 Laxmi Nagar Metro Station to ITI 2.0 45.95 11 ITO to Ram Charan Agarwal Bus Stop 0.6 12.14 12 Ram Charan Agarwal to Deen Dayal Upadhyay 1.3 18.07 13 Deen Dayal Upadhyay to Shivaji Park 0.8 38.24 14 Shivaji Park to Connaught Place 1.2 23.53 15 Connaught Place to New Delhi Railway Station 1.2 17.63 16 New Delhi Railway Station to Old Delhi Railway 2.1 8.30 Total 21.1 17.20 The World Bank Group Page | 163 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Table A4.26: Observed Journey Speed on Old Delhi Railway Station to Loni Border (Via Vikas Marg, Sadar Bazar) (S-13) in Down Direction S. Avg. Speed Name of the Section Distance (Km) No (Km/Hr) 1 Old Delhi Railway to New Delhi Railway Station 2.1 3.40 2 New Delhi Railway Station to Connaught Place 1.2 23.65 3 Connaught Place to Shivaji Park 1.2 14.70 4 Shivaji Park to Deen Dayal Upadhyay 0.8 45.34 5 Deen Dayal Upadhyay to Ram Charan Agarwal 1.3 12.80 6 Ram Charan Agarwal to ITO 0.6 44.08 7 ITO to Laxmi Nagar Metro Station 2.0 34.93 8 Laxmi Nagar Metro Station to Nirman Vihar 1.4 20.39 9 Nirman Vihar to Gagan Vihar 1.6 19.79 10 Gagan Vihar to Radheypuri 1.1 23.55 11 Radheypuri to Krishna Nagar A Block 1.1 3.94 12 Krishna Nagar A Block to Kranti Nagar 1.2 19.46 13 Kranti Nagar to DTC Shahadara Terminal 1.0 17.00 14 DTC Shahadara Terminal to Jyoti Nagar 2.1 16.17 15 Jyoti Nagar to Loni Road Crossing 1.3 18.16 16 Loni Road Crossing to Loni Road 1.1 14.47 Total 21.1 20.73 Table A4.27: Observed Journey Speed on Chilla Border to Bhagpat Road (S-14) in Up Direction S. Avg. Speed Name of the Section Distance (Km) No (Km/Hr) 1 Chilla to Mayur Place 1.5 13.39 2 Mayur Place to Mayur Vihar Phase-1 1.4 15.13 3 Mayur Vihar Phase-1 to Noida Mor 1.3 23.46 4 Noida Mor to Lalita Park 2.4 25.16 5 Lalita Park to Geeta Colony 1.8 22.12 6 Geeta Colony to Kailash Nagar 1.5 36.12 7 Kailash Nagar to Shastri Park 1.4 29.46 8 Shastri Park to Khajuri Khas 3.6 15.13 9 Khajuri Khas to Karwal Nagar 2.8 20.13 10 Karwal Nagar to Baghpat Road 1.1 25.13 Total 18.8 23.52 The World Bank Group Page | 164 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Table A4.28: Observed Journey Speed on Bhagpat Road to Chilla Border (S-14) in Down Direction S. Avg. Speed Name of the Section Distance (Km) No (Km/Hr) 1 Baghpat Road to Karwal Nagar 1.1 15.57 2 Karwal Nagar to Khajuri Khas 2.8 24.27 3 Khajuri Khas to Shastri Park 3.6 11.64 4 Shastri Park to Kailash Nagar 1.4 17.25 5 Kailash Nagar to Geeta Colony 1.5 23.42 6 Geeta Colony to Lalita Park 1.8 37.48 7 Lalita Park to Noida Mor 2.4 19.39 8 Noida Mor to Mayur Vihar Phase-1 1.3 22.50 9 Mayur Vihar Phase-1 to Mayur Place 1.4 31.02 10 Mayur Place to Chilla 1.5 20.16 Total 18.8 22.27 Table A4.29: Observed Journey Speed on Kashmere gate to Khanpur Junction (S-15) in Up Direction S. Avg. Speed Name of the Section Distance (Km) No (Km/Hr) 1 Kashmere Gate to Kela Ghat Near Mori Gate 0.91 34.28 2 Kela Ghat Marg to Netaji Subhash Marg 0.36 39.22 3 Netaji Subhash Marg to Red fort Metro 0.45 7.87 4 Red fort Metro to Jama Masjid Metro 0.73 31.51 5 Jama Masjid Metro to Delhi Gate 1.06 19.50 6 Delhi Gate to Ram Charan Agarwal Chowk 1.40 21.38 7 RamCharan Agarwal Chowk to National Stadium 1.63 35.08 8 National Stadium to Oberoi Hotel 1.76 33.07 9 Oberoi Hotel to ISPAT Bhawan 0.50 35.52 10 ISPAT Bhawan to Pant Nagar 1.20 35.49 11 Pant Nagar to Moolchand Hospital 2.50 17.48 12 Moolchand Hospital to Sir fort 1.35 23.06 13 Sir fort to Panchsheel Enclave 1.50 6.82 14 Panchsheel Enclave to Sheikh Sarai 0.95 6.13 15 Sheikh Sarai to Ambedkar Nagar Terminal 2.11 13.39 Total 18.41 23.98 The World Bank Group Page | 165 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Table A4.30: Observed Journey Speed on Khanpur Junction to Kashmere gate (S-15) in Down Direction S. Avg. Speed Name of the Section Distance (Km) No (Km/Hr) 1 Ambedkar Nagar Terminal to Sheikh Sarai 2.11 15.57 2 Sheikh Sarai to Panchsheel Enclave 0.95 24.27 3 Panchsheel Enclave to Sir fort 1.50 11.69 4 Sir fort to Moolchand Hospital 1.35 17.25 5 Moolchand Hospital to Pant Nagar 2.50 23.40 6 Pant Nagar to ISPAT Bhawan 1.20 37.42 7 ISPAT Bhawan to Oberoi Hospital 0.50 19.39 8 Oberoi Hospital to National Stadium 1.76 31.05 9 National Stadium to Ram Charan Agarwal Chowk 1.63 22.00 10 Ram Charan Agarwal Chowk to Delhi Gate 1.40 35.09 11 Delhi Gate to Jama Masjid Metro 1.06 33.07 12 Jama Masjid Metro to Red fort Metro 0.73 21.38 13 Red fort Metro to Netaji Subhash Marg 0.45 19.50 14 Netaji Subhash Marg to Kela Ghat Marg 0.36 32.51 15 Kela Ghat Marg to Kashmere Gate 0.91 39.41 Total 18.41 25.53 Table A4.31: Observed Journey Speed on Badarpur Border to Lado Sarai (S-16) in Up Direction S. Avg. Speed Name of the Section Distance (Km) No (Km/Hr) 1 Badarpur Border to MCD Park 1.72 21.89 2 MCD Park to Aali Village Crossing 1.87 25.31 3 Aali Village Crossing to Suraj Kund Crossing 1.13 18.29 4 Suraj Kund Crossing to Lal Kuan Village 0.77 9.20 5 Lal Kuan Village to Okhla Mor Bus Stop 0.68 38.47 6 Okhla Mor Bus Stop to Tuglakabad Village 1.05 28.74 7 Tuglakabad Village to Hamdard Nagar 1.19 33.81 8 Hamdard Nagar to Ambedkar Nagar Terminal 2.08 12.90 9 Ambedkar Nagar Terminal to Saket Crossing 1.17 17.18 10 Saket Crossing to Dhaula Peer 1.80 27.68 11 Dhaula Peer to DDA Flat 1.10 14.61 12 DDA Flat to Lado Sarai Crossing 0.70 16.15 Total 15.26 22.01 The World Bank Group Page | 166 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Table A4.32: Observed Journey Speed on Lado Sarai to Badarpur Border (S-16) in Down Direction S. Avg. Speed Name of the Section Distance (Km) No (Km/Hr) 1 Lado Sarai Crossing to DDA Flat 0.70 15.16 2 DDA Flat to Dhaula Peer 1.10 16.18 3 Dhaula Peer to Saket Crossing 1.80 25.60 4 Saket Crossing to Ambedkar Nagar Terminal 1.17 20.80 5 Ambedkar Nagar Terminal to Hamdard Nagar 2.08 10.82 6 Hamdard Nagar to Tuglakabad Village 1.19 15.76 7 Tuglakabad Village to Okhla Mor Bus Stop 1.05 23.56 8 Okhla Mor Bus Stop to Lal Kuan Village 0.68 30.23 9 Lal Kuan Village to Suraj Kund Crossing 0.77 27.14 10 Suraj Kund Crossing to Aali Village Crossing 1.13 29.16 11 Aali Village Crossing to MCD Park 1.87 17.15 12 MCD Park to Badarpur Border 1.72 21.16 Total 15.26 21.06 Table A4.33: Observed Journey Speed on Outer Ring Road (Station Road to ISBT) (S- 17) in Up Direction S. Avg. Speed Name of the Section Distance (Km) No (Km/Hr) 1 Dhaula Kuan to Golf Sport 0.73 9.60 2 Golf Sport to Khimya Park 2.50 21.61 3 Khimya Park to Kirby Place 0.78 25.62 4 Kirby Place to Lajwanti Garden 2.25 33.22 5 Lajwanti Garden to Hari Nagar Depot 1.28 5.70 6 Hari Nagar Depo to Tilak Nagar 1.46 14.98 7 Tilak Nagar to Janakpuri Bus Stop 1.94 20.72 8 Janakpuri Bus Stop to CRPF 2.15 55.42 9 CRPF to Paschim Vihar 2.68 53.28 10 Paschim Vihar to Peera Garhi Chowk 1.17 28.04 11 Peera Garhi Chowk to B Block Mangolpuri 1.01 42.47 12 B Block Mangolpuri to Deepali Chowk 2.70 30.78 13 Deepali Chowk to Madhuban Chowk 1.26 12.08 14 Madhuban Chowk to Prashant Vihar 1.69 47.26 15 Prashant Vihar to Mukarba Chowk 2.45 43.86 16 Mukarba Chowk to Mukundpur Crossing 2.88 48.12 17 Mukundpur Crossing to Burari Crossing 1.44 48.77 18 Burari Crossing to Gandhi Vihar 1.91 34.61 19 Gandhi Vihar to Majnu Ka Tila 3.37 27.11 20 Majnu Ka Tila to ISBT 4 31.47 Total 39.65 31.65 The World Bank Group Page | 167 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Table A4.34: Observed Journey Speed on Outer Ring Road (ISBT to Station Road) (S- 17) in Down Direction S. Avg. Speed Name of the Section Distance (Km) No (Km/Hr) 1 ISBT TO Majnu KA Tila 4 18.45 2 Majnu Ka Tila to Gandhi Vihar 3.37 37.68 3 Gandhi Vihar to Burari Crossing 1.91 50.29 4 Burari Crossing to Mukundpur Crossing 1.44 46.30 5 Mukundpur Crossing to Mukarba Chowk 2.88 22.08 6 Mukarba Chowk to Prashant Vihar 2.45 48.06 7 Prashant Vihar to Madhuban Chowk 1.69 26.05 8 Madhuban Chowk to Deepali Chowk 1.26 39.97 9 Deepali Chowk to B Block Mangolpuri 2.70 18.07 10 B Block Mangolpuri to Peera Garhi Chowk 1.01 21.70 11 Peera Garhi Chowk to Paschim Vihar 1.17 22.80 12 Paschim Vihar to CRPF 2.68 47.75 13 CRPF to Janakpuri Bus Stop 2.15 37.52 14 Janakpuri Bus Stop to Tilak Nagar 1.94 14.42 15 Tilak Nagar to Hari Nagar Depo 1.46 22.50 16 Hari Nagar Depot to Lajwanti Garden 1.28 40.62 17 Lajwanti Garden to Kirby Place 2.25 30.19 18 Kirby Place to Khimya Park 0.78 15.98 19 Khimya Park to Golf Sport 2.50 13.14 20 Golf Sport to Dhaula Kuan 0.73 14.98 Total 39.65 29.42 The World Bank Group Page | 168 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Appendix 5 The World Bank Group Page | 169 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report The World Bank Group Page | 170 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Appendix 6 Table A6.1: Classified Traffic Volume at Badarpur Border Road Name: Mathura Road NH-2 Location: Badarpur Border Date: 07.10.2017 - 08.10.2017 Outer Cordon 1 Goods Light Two Axle Multi Axle Cycle Small Two Auto/ Cycle Rickshaws Total FMV Total SMV Grand Time Big Cars Auto Buses Mini Bus Commercial Trucks Trucks Percentage PCU Cars Wheeler Goods Van Vihicles (LT) (CYC) and Other Total (HT) (MT) (GAV) (CY SMV) 08:00-09:00 1664 1097 780 150 89 1591 450 138 42 15 713 251 6016 964 6980 5.9% 7327 09:00-10:00 2192 1328 1214 71 45 1971 239 171 31 39 503 259 7301 762 8063 6.8% 8282 10:00-11:00 1793 959 1071 69 25 1772 228 127 48 34 217 118 6126 335 6461 5.5% 6738 11:00-12:00 1839 1165 846 42 13 1644 289 132 145 61 100 118 6176 218 6394 5.4% 6972 12:00-13:00 1526 966 883 72 23 1431 201 116 168 95 98 167 5481 265 5746 4.8% 6593 13:00-14:00 1539 793 789 70 38 1549 304 121 207 109 64 141 5519 205 5724 4.8% 6719 14:00-15:00 1484 632 821 72 37 1692 267 162 215 129 86 166 5511 252 5763 4.9% 6821 15:00-16:00 1376 745 732 71 33 1442 205 157 166 89 85 197 5016 282 5298 4.5% 6139 16:00-17:00 1128 579 852 77 32 1468 232 195 146 86 128 205 4795 333 5128 4.3% 5962 17:00-18:00 1754 1064 1039 76 32 1654 235 191 70 46 185 199 6161 384 6545 5.5% 7044 18:00-19:00 2090 1326 1036 128 33 1689 207 195 51 32 364 278 6787 642 7429 6.3% 7875 19:00-20:00 1749 1002 1055 106 42 1548 119 146 37 12 354 269 5816 623 6439 5.4% 6723 20:00-21:00 1641 668 921 82 27 1205 72 51 63 26 341 160 4756 501 5257 4.4% 5519 21:00-22:00 1476 595 937 83 21 1371 79 68 65 45 186 91 4740 277 5017 4.2% 5362 22:00-23:00 1098 501 587 66 14 1011 102 150 301 327 66 41 4157 107 4264 3.6% 6135 23:00-24:00 827 393 281 33 23 674 114 245 400 668 49 12 3658 61 3719 3.1% 6995 00:00-01:00 686 250 166 47 15 291 66 224 469 717 13 6 2931 19 2950 2.5% 6608 01:00-02:00 521 195 108 35 13 109 37 209 534 754 14 3 2515 17 2532 2.1% 6434 02:00-03:00 534 207 67 37 6 60 47 173 636 775 1 0 2542 1 2543 2.1% 6715 03:00-04:00 509 185 76 33 12 54 39 163 526 718 2 0 2315 2 2317 2.0% 6062 04:00-05:00 559 227 111 33 15 75 42 147 506 687 7 16 2402 23 2425 2.0% 6025 05:00-06:00 675 261 249 70 34 201 42 166 267 447 37 27 2412 64 2476 2.1% 4847 06:00-07:00 953 358 530 163 54 689 74 166 221 273 124 75 3481 199 3680 3.1% 5487 07:00-08:00 1691 720 674 236 87 1189 63 141 55 37 386 111 4893 497 5390 4.5% 5991 Total 31304 16216 15825 1922 763 26380 3753 3754 5369 6221 4123 2910 111507 7033 118540 100.0% 155376 Percentage 26.4% 13.7% 13.3% 1.6% 0.6% 22.3% 3.2% 3.2% 4.5% 5.2% 3.5% 2.5% 94.1% 5.9% 100.0% Peak Volume= 8063 Peak Time= 09:00-10:00 Figure A6.1: Hourly Distribution of Classified Traffic Volume and Traffic Composition at Badarpur Border The World Bank Group Page | 171 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Table A6.2: Classified Traffic Volume at Rajokri Border Road Name: Delhi - Gurgaon Expressway Location: Rajokari Border Date: 26.07.2017 - 27.07.2017 Outer Cordon OC-03 G o o ds Light Two Axle Multi Axle C yc le Small Two A ut o / Cycle R ic k s ha ws Grand Time Big Cars Auto Buses Mini Bus G o o ds V a n Commercial Trucks Trucks a nd O t he r Total FMV Total SMV Percentage PCU Cars Wheeler (CYC) Total (GA V) Vihicles (LT) (HT) (MT) (C Y SM V) 08:00-09:00 10872 1462 40 152 64 3047 51 73 32 18 102 0 15811 102 15913 4.5% 15665 09:00-10:00 12043 2016 41 183 77 3840 71 86 12 6 81 0 18375 81 18456 5.2% 18030 10:00-11:00 10715 1916 48 154 54 3888 60 106 65 11 64 0 17017 64 17081 4.8% 16700 11:00-12:00 12291 1743 44 143 53 2877 166 215 157 32 26 0 17721 26 17747 5.0% 17979 12:00-13:00 11417 2062 29 94 23 3379 225 247 130 24 6 0 17630 6 17636 5.0% 17585 13:00-14:00 14797 2053 48 99 9 2692 284 313 118 67 1 0 20480 1 20481 5.8% 20793 14:00-15:00 11540 2289 66 103 12 3197 329 321 178 74 30 0 18109 30 18139 5.1% 18496 15:00-16:00 11611 2446 38 137 18 4417 318 241 122 63 7 0 19411 7 19418 5.5% 19354 16:00-17:00 14868 2543 39 126 49 3166 262 215 149 27 9 0 21444 9 21453 6.1% 21597 17:00-18:00 14140 2428 28 293 98 4646 117 93 32 8 13 0 21883 13 21896 6.2% 21615 18:00-19:00 15354 2236 20 234 86 5763 42 39 15 0 23 0 23789 23 23812 6.7% 22988 19:00-20:00 11456 2328 22 145 43 8085 52 41 10 6 29 0 22188 29 22217 6.3% 20606 20:00-21:00 12696 2489 23 102 17 4016 74 59 9 15 73 0 19500 73 19573 5.5% 18895 21:00-22:00 10473 1945 26 73 4 2979 75 117 25 24 93 0 15741 93 15834 4.5% 15428 22:00-23:00 9291 1652 18 37 2 1705 93 202 106 185 128 0 13291 128 13419 3.8% 14015 23:00-24:00 6520 1470 2 31 7 693 125 240 347 428 76 0 9863 76 9939 2.8% 12172 00:00-01:00 4742 1540 9 39 2 611 93 259 381 478 53 0 8154 53 8207 2.3% 10721 01:00-02:00 3096 1243 13 24 2 504 62 296 333 385 29 0 5958 29 5987 1.7% 8092 02:00-03:00 2985 1315 28 13 7 353 87 282 327 385 4 0 5782 4 5786 1.6% 7920 03:00-04:00 1601 834 6 25 18 362 67 270 254 299 0 0 3736 0 3736 1.1% 5438 04:00-05:00 2435 1387 6 76 12 597 52 222 326 233 8 0 5346 8 5354 1.5% 6970 05:00-06:00 5009 1892 14 73 12 615 34 225 303 143 52 0 8320 52 8372 2.4% 9589 06:00-07:00 7205 2191 18 96 33 862 55 123 151 45 159 0 10779 159 10938 3.1% 11420 07:00-08:00 7957 2977 16 147 52 1059 44 84 34 10 252 0 12380 252 12632 3.6% 12757 Total 225114 46457 642 2599 754 63353 2838 4369 3616 2966 1318 0 352708 1318 354026 100.0% 364826 Percentage 63.6% 13.1% 0.2% 0.7% 0.2% 17.9% 0.8% 1.2% 1.0% 0.8% 0.4% 0.0% 99.6% 0.4% 100.0% Peak Volume= 23812 Peak Time= 18:00-19:00 Figure A6.2: Hourly Distribution of Classified Traffic Volume and Traffic Composition at Rajokri Border The World Bank Group Page | 172 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Table A6.3: Classified Traffic Volume at Ayanagar Border Road Name: Mehrauli - Gurgaon Road Location: Arjun Garh (Ayanagar Border) Date: 18.07.2017 - 19.07.2017 Outer Cordon OC-02 G o o ds Light Two Axle Multi Axle C yc le Small Two A ut o / Cycle R ic k s ha ws Grand Time Big Cars Auto Buses Mini Bus G o o ds V a n Commercial Trucks Trucks a nd O t he r Total FMV Total SMV Percentage PCU Cars Wheeler (CYC) Total (GA V) Vihicles (LT) (HT) (MT) (C Y SM V) 08:00-09:00 1410 900 2 37 23 991 0 3 0 0 40 2 3366 42 3408 4.1% 3240 09:00-10:00 2216 1161 5 16 11 1606 2 5 6 2 56 5 5030 61 5091 6.2% 4731 10:00-11:00 2576 764 14 20 18 1580 12 1 0 0 27 6 4985 33 5018 6.1% 4680 11:00-12:00 2105 660 2 7 6 1052 8 0 5 13 22 15 3858 37 3895 4.7% 3708 12:00-13:00 1830 717 5 17 6 1061 29 15 18 2 12 3 3700 15 3715 4.5% 3551 13:00-14:00 2180 783 5 16 9 1185 27 1 15 13 13 6 4234 19 4253 5.2% 4085 14:00-15:00 2117 813 2 21 6 1054 62 0 7 25 17 6 4107 23 4130 5.0% 4042 15:00-16:00 2060 779 5 29 11 769 60 0 15 4 9 12 3732 21 3753 4.6% 3706 16:00-17:00 2236 804 4 18 24 799 45 0 12 8 18 5 3950 23 3973 4.8% 3902 17:00-18:00 2870 772 2 34 21 1580 5 0 0 3 42 10 5287 52 5339 6.5% 5030 18:00-19:00 3132 839 4 38 37 1399 6 0 1 0 51 1 5456 52 5508 6.7% 5252 19:00-20:00 2730 894 1 32 14 1658 2 0 0 0 51 9 5331 60 5391 6.6% 5035 20:00-21:00 2642 561 0 2 2 1342 2 0 0 0 98 32 4551 130 4681 5.7% 4320 21:00-22:00 2195 596 1 2 0 1338 3 3 0 0 65 12 4138 77 4215 5.1% 3861 22:00-23:00 1242 298 0 1 2 988 14 0 1 1 47 20 2547 67 2614 3.2% 2370 23:00-24:00 1365 236 0 0 0 1179 22 6 5 17 6 11 2830 17 2847 3.5% 2638 00:00-01:00 912 463 7 0 1 790 8 21 4 18 6 2 2224 8 2232 2.7% 2120 01:00-02:00 689 505 8 0 0 442 5 32 5 23 3 2 1709 5 1714 2.1% 1714 02:00-03:00 334 354 1 0 0 438 9 11 8 29 3 3 1184 6 1190 1.4% 1208 03:00-04:00 299 336 1 0 0 416 0 12 12 25 9 6 1101 15 1116 1.4% 1128 04:00-05:00 317 383 2 0 0 487 0 23 8 19 16 4 1239 20 1259 1.5% 1226 05:00-06:00 544 427 2 9 3 694 12 4 4 8 25 13 1707 38 1745 2.1% 1631 06:00-07:00 644 656 2 23 4 767 3 15 0 15 36 6 2129 42 2171 2.6% 2076 07:00-08:00 1043 967 3 37 21 788 21 11 2 15 46 25 2908 71 2979 3.6% 2940 Total 39688 15668 78 359 219 24403 357 163 128 240 718 216 81303 934 82237 100.0% 78194 Percentage 48.3% 19.1% 0.1% 0.4% 0.3% 29.7% 0.4% 0.2% 0.2% 0.3% 0.9% 0.3% 98.9% 1.1% 100.0% Peak Volume= 5508 Peak Time= 18:00-19:00 Figure A6.3: Hourly Distribution of Classified Traffic Volume and Traffic Composition at Ayanagar Border The World Bank Group Page | 173 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Table A6.4: Classified Traffic Volume at Tikri Border Road Name: Bahadurgarh Road NH -10 Location: Tikri Border Date: 19.07.2017 - 20.07.2017 Outer Cordon OC-04 G o o ds Light Two Axle Multi Axle C yc le Small Two A ut o / Cycle R ic k s ha ws Grand Time Big Cars Auto Buses Mini Bus G o o ds V a n Commercial Trucks Trucks a nd O t he r Total FMV Total SMV Percentage PCU Cars Wheeler (CYC) Total (GA V) Vihicles (LT) (HT) (MT) (C Y SM V) 08:00-09:00 1496 577 232 74 14 1442 99 83 32 9 282 33 4058 315 4373 7.9% 4283 09:00-10:00 1419 429 182 61 18 1328 87 58 32 19 188 35 3633 223 3856 7.0% 3827 10:00-11:00 1749 400 140 57 4 1347 80 109 35 26 98 28 3947 126 4073 7.4% 4103 11:00-12:00 1115 349 151 45 7 1256 70 126 37 13 41 12 3169 53 3222 5.8% 3238 12:00-13:00 1094 323 151 46 5 1360 67 77 32 19 68 32 3174 100 3274 5.9% 3246 13:00-14:00 913 225 127 51 10 1139 106 155 29 16 67 34 2771 101 2872 5.2% 2953 14:00-15:00 790 221 119 44 11 943 83 163 18 18 33 23 2410 56 2466 4.5% 2570 15:00-16:00 778 191 146 59 9 1132 67 134 23 17 21 22 2556 43 2599 4.7% 2679 16:00-17:00 835 279 94 51 3 905 77 87 28 15 25 6 2374 31 2405 4.4% 2484 17:00-18:00 1270 306 121 51 5 748 104 147 38 20 127 12 2810 139 2949 5.3% 3107 18:00-19:00 1232 310 76 49 6 833 73 168 26 10 132 16 2783 148 2931 5.3% 2991 19:00-20:00 1157 200 117 50 4 1227 63 107 24 12 155 26 2961 181 3142 5.7% 3073 20:00-21:00 1321 189 74 52 1 801 76 64 27 38 108 33 2643 141 2784 5.1% 2923 21:00-22:00 957 136 89 42 6 579 76 45 42 33 63 37 2005 100 2105 3.8% 2315 22:00-23:00 884 113 47 26 3 145 47 51 55 68 24 6 1439 30 1469 2.7% 1885 23:00-24:00 701 79 19 26 0 66 39 72 96 121 5 13 1219 18 1237 2.2% 1951 00:00-01:00 243 29 9 7 0 49 28 52 109 98 4 4 624 8 632 1.1% 1237 01:00-02:00 133 26 5 11 0 53 13 41 76 114 0 14 472 14 486 0.9% 1081 02:00-03:00 60 21 2 7 0 47 4 51 89 113 0 12 394 12 406 0.7% 1016 03:00-04:00 75 20 8 10 0 36 14 59 80 68 0 10 370 10 380 0.7% 832 04:00-05:00 177 36 11 12 0 68 27 72 68 57 6 3 528 9 537 1.0% 930 05:00-06:00 350 157 43 31 2 359 51 73 72 57 49 5 1195 54 1249 2.3% 1615 06:00-07:00 612 301 109 38 8 570 68 81 51 51 133 16 1889 149 2038 3.7% 2298 07:00-08:00 1113 470 209 74 11 1224 123 101 71 46 182 19 3442 201 3643 6.6% 3871 Total 20474 5387 2281 974 127 17657 1542 2176 1190 1058 1811 451 52866 2262 55128 100.0% 60507 Percentage 37.1% 9.8% 4.1% 1.8% 0.2% 32.0% 2.8% 3.9% 2.2% 1.9% 3.3% 0.8% 95.9% 4.1% 100.0% Peak Volume= 4373 Peak Time= 08:00-09:00 Figure A6.4: Hourly Distribution of Classified Traffic Volume and Traffic Composition at Tikri Border The World Bank Group Page | 174 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Table A6.5: Classified Traffic Volume at Singhu Border Road Name: NH -1 Location: Singhu Border Date: 04.07.2017 - 05.07.2017 Outer Cordon OC-05 Goods Light Two Axle Multi Axle Cycle Small Two Auto/ Cycle Rickshaws Total FMV Total SMV Grand Time Big Cars Auto Buses Mini Bus Commercial Trucks Trucks Percentage PCU Cars Wheeler Goods Van Vihicles (LT) (CYC) and Other Total (HT) (MT) (GAV) (CY SMV) 08:00-09:00 1356 1134 72 113 55 801 243 148 27 28 34 35 3977 69 4046 5.3% 4489 09:00-10:00 1446 1023 56 112 24 817 257 163 32 23 36 40 3953 76 4029 5.3% 4440 10:00-11:00 1357 801 66 87 27 794 201 151 17 33 31 30 3534 61 3595 4.7% 3936 11:00-12:00 1011 694 58 39 30 679 224 80 12 30 35 29 2857 64 2921 3.8% 3149 12:00-13:00 881 609 194 46 31 664 185 60 15 33 41 43 2718 84 2802 3.7% 3067 13:00-14:00 912 690 85 28 22 745 170 77 17 37 35 46 2783 81 2864 3.8% 3065 14:00-15:00 1016 701 111 34 17 758 88 54 18 37 37 41 2834 78 2912 3.8% 3068 15:00-16:00 1136 647 44 38 25 635 88 61 32 31 28 49 2737 77 2814 3.7% 3023 16:00-17:00 1458 998 81 29 15 662 94 82 25 34 43 56 3478 99 3577 4.7% 3764 17:00-18:00 1265 838 69 34 23 748 101 46 18 32 40 52 3174 92 3266 4.3% 3411 18:00-19:00 1295 988 85 29 28 715 67 39 15 18 40 33 3279 73 3352 4.4% 3419 19:00-20:00 1064 880 44 35 26 686 79 83 46 55 75 36 2998 111 3109 4.1% 3388 20:00-21:00 1055 524 40 19 11 1000 79 126 101 60 81 15 3015 96 3111 4.1% 3400 21:00-22:00 1060 794 53 26 16 717 100 158 104 35 17 5 3063 22 3085 4.1% 3438 22:00-23:00 1015 974 27 20 14 315 168 192 160 86 5 3 2971 8 2979 3.9% 3760 23:00-24:00 938 755 25 21 10 464 167 203 238 56 0 13 2877 13 2890 3.8% 3695 00:00-01:00 740 754 28 26 15 477 128 146 151 114 0 12 2579 12 2591 3.4% 3388 01:00-02:00 845 446 23 25 16 202 117 234 243 216 0 23 2367 23 2390 3.1% 3839 02:00-03:00 905 526 27 16 16 152 107 266 253 245 0 23 2513 23 2536 3.3% 4113 03:00-04:00 774 496 30 12 20 169 39 100 288 280 2 67 2208 69 2277 3.0% 3943 04:00-05:00 790 685 50 19 11 205 97 113 266 271 14 34 2507 48 2555 3.4% 4158 05:00-06:00 997 804 62 22 15 258 114 106 235 285 54 86 2898 140 3038 4.0% 4638 06:00-07:00 1190 1136 68 78 30 485 205 168 225 1005 60 72 4590 132 4722 6.2% 8960 07:00-08:00 1250 1340 89 99 28 681 186 229 274 275 65 84 4451 149 4600 6.0% 6401 Total 25756 19237 1487 1007 525 13829 3304 3085 2812 3319 773 927 74361 1700 76061 100.0% 95952 Percentage 33.9% 25.3% 2.0% 1.3% 0.7% 18.2% 4.3% 4.1% 3.7% 4.4% 1.0% 1.2% 97.8% 2.2% 100.0% Peak Volume= 4722 Peak Time= 06:00-07:00 Figure A6.5: Hourly Distribution of Classified Traffic Volume and Traffic Composition at Singhu Border The World Bank Group Page | 175 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Table A6.6: Classified Traffic Volume at Loni Border Road Name: Saharanpur road Location: Loni Border Date: 24.07.2017 - 25.07.2017 Outer Cordon OC-06 G o o ds Light Two Axle Multi Axle C yc le Small Two A ut o / Cycle R ic k s ha ws Grand Time Big Cars Auto Buses Mini Bus G o o ds V a n Commercial Trucks Trucks a nd O t he r Total FMV Total SMV Percentage PCU Cars Wheeler (CYC) Total (GA V) Vihicles (LT) (HT) (MT) (C Y SM V) 08:00-09:00 590 67 889 25 8 2036 11 45 14 0 383 78 3685 461 4146 5.8% 3776 09:00-10:00 581 61 1553 22 9 1901 8 45 7 0 497 74 4187 571 4758 6.6% 4475 10:00-11:00 440 48 1471 19 9 1911 10 32 5 0 324 57 3945 381 4326 6.0% 4087 11:00-12:00 423 56 1423 22 6 1823 3 29 8 2 179 95 3795 274 4069 5.7% 3945 12:00-13:00 449 34 1320 15 12 1909 7 38 10 0 224 158 3794 382 4176 5.8% 4014 13:00-14:00 310 61 985 9 10 2104 32 31 4 3 174 225 3549 399 3948 5.5% 3723 14:00-15:00 284 59 1029 23 18 1763 19 17 0 0 121 200 3212 321 3533 4.9% 3420 15:00-16:00 316 66 1098 24 13 1780 15 19 0 0 108 143 3331 251 3582 5.0% 3452 16:00-17:00 330 59 1187 10 14 1741 27 29 4 0 110 169 3401 279 3680 5.1% 3582 17:00-18:00 366 72 1570 11 10 2238 16 21 3 2 199 222 4309 421 4730 6.6% 4560 18:00-19:00 502 97 1641 13 9 2280 9 12 1 0 374 321 4564 695 5259 7.3% 5038 19:00-20:00 471 97 2463 14 7 1877 1 0 0 1 278 235 4931 513 5444 7.6% 5485 20:00-21:00 365 89 1483 10 10 1458 14 14 5 1 462 118 3449 580 4029 5.6% 3847 21:00-22:00 278 71 965 11 5 1585 22 22 20 1 587 73 2980 660 3640 5.1% 3272 22:00-23:00 218 69 569 7 1 1348 12 19 53 4 260 38 2300 298 2598 3.6% 2414 23:00-24:00 185 72 284 5 0 816 3 33 65 16 43 17 1479 60 1539 2.2% 1593 00:00-01:00 95 44 67 2 2 313 3 23 44 8 13 8 601 21 622 0.9% 690 01:00-02:00 66 36 43 5 0 113 3 32 45 22 5 6 365 11 376 0.5% 551 02:00-03:00 41 16 18 6 1 104 0 35 49 25 3 3 295 6 301 0.4% 495 03:00-04:00 34 20 28 1 0 59 6 22 61 10 2 68 241 70 311 0.4% 508 04:00-05:00 60 38 65 14 3 164 6 20 54 7 22 37 431 59 490 0.7% 646 05:00-06:00 223 39 158 21 6 326 6 37 43 5 43 12 864 55 919 1.3% 1027 06:00-07:00 349 48 308 28 18 778 9 37 36 9 81 33 1620 114 1734 2.4% 1778 07:00-08:00 445 78 753 58 12 1673 12 52 24 7 174 66 3114 240 3354 4.7% 3265 Total 7421 1397 21370 375 183 32100 254 664 555 123 4666 2456 64442 7122 71564 100.0% 69641 Percentage 10.4% 2.0% 29.9% 0.5% 0.3% 44.9% 0.4% 0.9% 0.8% 0.2% 6.5% 3.4% 90.0% 10.0% 100.0% Peak Volume= 5444 Peak Time= 19:00-20:00 Figure A6.6: Hourly Distribution of Classified Traffic Volume and Traffic Composition at Loni Border The World Bank Group Page | 176 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Table A6.7: Classified Traffic Volume at Apsara Border Road Name: GT Road Location: Apsara Border (Dilshad Garden) Date: 25.07.2017 - 26.07.2017 Outer Cordon OC-07 G o o ds Light Two Axle Multi Axle C yc le Small Two A ut o / Cycle R ic k s ha ws Grand Time Big Cars Auto Buses Mini Bus G o o ds V a n Commercial Trucks Trucks a nd O t he r Total FMV Total SMV Percentage PCU Cars Wheeler (CYC) Total (GA V) Vihicles (LT) (HT) (MT) (C Y SM V) 08:00-09:00 680 146 81 88 15 790 24 18 0 0 72 91 1842 163 2005 2.4% 2045 09:00-10:00 2474 315 251 200 13 1752 72 70 8 3 82 190 5158 272 5430 6.4% 5607 10:00-11:00 2553 329 210 140 7 1851 73 104 13 2 95 126 5282 221 5503 6.5% 5506 11:00-12:00 2588 243 223 141 7 1667 146 92 2 2 51 138 5111 189 5300 6.2% 5390 12:00-13:00 2271 203 208 158 4 1721 122 84 1 1 80 143 4773 223 4996 5.9% 5067 13:00-14:00 2281 242 184 158 5 2166 105 124 3 2 73 81 5270 154 5424 6.4% 5372 14:00-15:00 1741 217 161 136 5 2119 102 138 1 1 39 108 4621 147 4768 5.6% 4707 15:00-16:00 2288 216 253 172 6 2054 134 68 3 0 62 118 5194 180 5374 6.3% 5396 16:00-17:00 2386 236 270 197 6 2438 166 103 4 0 91 142 5806 233 6039 7.1% 6052 17:00-18:00 2231 230 201 208 13 2697 142 97 1 0 110 127 5820 237 6057 7.1% 5982 18:00-19:00 2497 231 228 157 8 2410 88 11 0 0 71 176 5630 247 5877 6.9% 5744 19:00-20:00 2471 239 353 190 9 2776 105 21 6 0 148 168 6170 316 6486 7.6% 6337 20:00-21:00 1642 198 226 120 4 1773 87 15 12 3 65 83 4080 148 4228 5.0% 4168 21:00-22:00 1176 298 229 93 12 1199 63 22 41 5 31 51 3138 82 3220 3.8% 3316 22:00-23:00 658 337 233 106 6 746 26 48 140 50 3 7 2350 10 2360 2.8% 2932 23:00-24:00 602 275 159 67 4 441 18 43 97 79 0 3 1785 3 1788 2.1% 2350 00:00-01:00 345 64 110 38 1 97 23 70 112 112 1 1 972 2 974 1.1% 1711 01:00-02:00 249 39 99 24 2 36 6 52 119 52 1 4 678 5 683 0.8% 1194 02:00-03:00 147 34 74 29 0 33 4 54 86 58 4 2 519 6 525 0.6% 993 03:00-04:00 115 29 164 55 1 29 5 39 71 77 0 0 585 0 585 0.7% 1155 04:00-05:00 136 50 199 43 3 76 1 50 115 72 1 6 745 7 752 0.9% 1372 05:00-06:00 345 70 254 79 9 273 23 74 118 29 17 7 1274 24 1298 1.5% 1829 06:00-07:00 666 106 250 117 30 438 23 59 109 53 10 14 1851 24 1875 2.2% 2526 07:00-08:00 1609 206 260 245 77 534 45 95 123 24 48 31 3218 79 3297 3.9% 4174 Total 34151 4553 4880 2961 247 30116 1603 1551 1185 625 1155 1817 81872 2972 84844 100.0% 90926 Percentage 40.3% 5.4% 5.8% 3.5% 0.3% 35.5% 1.9% 1.8% 1.4% 0.7% 1.4% 2.1% 96.5% 3.5% 100.0% Peak Volume= 6486 Peak Time= 19:00-20:00 Figure A6.7: Hourly Distribution of Classified Traffic Volume and Traffic Composition at Apsara Border The World Bank Group Page | 177 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Table A6.8: Classified Traffic Volume at Ghazipur Border Road Name: Ghazipur Road (NH-24 Bypass) Location: Ghazipur Border Date: 14.07.2017 - 15.07.2017 Outer Cordon OC-08 Goods Light Two Axle Multi Axle Cycle Small Two Auto/ Cycle Rickshaws Total FMV Total SMV Grand Time Big Cars Auto Buses Mini Bus Commercial Trucks Trucks Percentage PCU Cars Wheeler Goods Van Vihicles (LT) (CYC) and Other Total (HT) (MT) (GAV) (CY SMV) 08:00-09:00 2934 2239 614 219 69 2173 391 106 304 52 401 227 9101 628 9729 6.0% 10767 09:00-10:00 2739 2015 701 143 23 2246 475 132 148 37 446 235 8659 681 9340 5.7% 9851 10:00-11:00 2576 2149 589 153 32 2131 350 129 196 76 326 246 8381 572 8953 5.5% 9734 11:00-12:00 2512 2348 539 127 36 2096 379 67 128 49 246 148 8281 394 8675 5.3% 9150 12:00-13:00 2016 1813 571 134 21 2049 321 134 179 100 189 191 7338 380 7718 4.7% 8545 13:00-14:00 2154 1877 530 100 21 1885 284 156 146 79 141 182 7232 323 7555 4.6% 8220 14:00-15:00 2020 1908 499 133 24 1770 348 138 82 77 124 91 6999 215 7214 4.4% 7821 15:00-16:00 1760 1198 330 109 14 1644 262 78 56 24 54 66 5475 120 5595 3.4% 5854 16:00-17:00 1981 1254 386 102 14 1530 324 63 42 67 52 38 5763 90 5853 3.6% 6271 17:00-18:00 2124 1393 404 118 17 1475 337 51 46 24 185 51 5989 236 6225 3.8% 6493 18:00-19:00 2526 1442 573 184 15 1590 320 105 65 33 282 131 6853 413 7266 4.5% 7749 19:00-20:00 2161 1456 547 272 18 1521 316 50 22 22 190 60 6385 250 6635 4.1% 7165 20:00-21:00 1940 1375 426 184 35 1206 242 140 103 67 151 80 5718 231 5949 3.6% 6732 21:00-22:00 2060 1101 362 76 38 1270 274 106 100 107 110 28 5494 138 5632 3.5% 6300 22:00-23:00 2356 1129 276 55 39 1057 222 64 140 223 105 41 5561 146 5707 3.5% 6818 23:00-24:00 1883 1011 193 87 33 985 176 63 337 257 46 40 5025 86 5111 3.1% 6800 00:00-01:00 1891 757 176 36 22 648 182 105 453 450 43 36 4720 79 4799 2.9% 7387 01:00-02:00 1832 934 123 25 18 770 204 78 544 516 44 31 5044 75 5119 3.1% 8048 02:00-03:00 1740 734 90 81 14 737 202 105 481 493 61 43 4677 104 4781 2.9% 7623 03:00-04:00 1748 784 133 105 18 748 215 92 525 543 87 47 4911 134 5045 3.1% 8197 04:00-05:00 1857 1134 169 204 40 976 204 63 499 393 112 113 5539 225 5764 3.5% 8509 05:00-06:00 2018 1245 301 203 38 1374 181 135 442 602 193 266 6539 459 6998 4.3% 10344 06:00-07:00 2016 1533 453 257 46 1705 225 175 419 549 292 285 7378 577 7955 4.9% 11135 07:00-08:00 2690 1866 582 283 161 1900 254 139 475 548 375 328 8898 703 9601 5.9% 13010 Total 51534 34695 9567 3390 806 35486 6688 2474 5932 5388 4255 3004 155960 7259 163219 100.0% 198524 Percentage 31.6% 21.3% 5.9% 2.1% 0.5% 21.7% 4.1% 1.5% 3.6% 3.3% 2.6% 1.8% 95.6% 4.4% 100.0% Peak Volume= 9729 Peak Time= 08:00-09:00 Figure A6.8: Hourly Distribution of Classified Traffic Volume and Traffic Composition at Ghazipur Border The World Bank Group Page | 178 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Table A6.9: Classified Traffic Volume at Chilla Border Road Name: Mayur Vihar - Noida Link Road Location: Chilla Boarder Date: 12.07.2017 - 13.07.2017 Outer Cordon OC-09 G o o ds Light Two Axle Multi Axle C yc le Small Two A ut o / Cycle R ic k s ha ws Grand Time Big Cars Auto Buses Mini Bus G o o ds V a n Commercial Trucks Trucks a nd O t he r Total FMV Total SMV Percentage PCU Cars Wheeler (CYC) Total (GA V) Vihicles (LT) (HT) (MT) (C Y SM V) 08:00-09:00 3519 1202 313 112 17 2276 156 110 171 6 14 9 7882 23 7905 7.7% 8133 09:00-10:00 3374 1092 257 81 18 2022 129 101 113 3 7 5 7190 12 7202 7.0% 7278 10:00-11:00 2563 866 161 78 11 1571 72 107 95 10 5 9 5534 14 5548 5.4% 5671 11:00-12:00 2091 711 176 116 34 1465 74 82 45 3 4 12 4797 16 4813 4.7% 4930 12:00-13:00 1912 632 157 95 30 1472 66 103 24 11 3 10 4502 13 4515 4.4% 4573 13:00-14:00 1838 588 160 85 84 1316 76 96 45 9 3 11 4297 14 4311 4.2% 4480 14:00-15:00 2169 925 171 59 42 1337 85 96 50 5 4 8 4939 12 4951 4.8% 5021 15:00-16:00 2648 860 137 48 40 1346 88 58 23 4 3 9 5252 12 5264 5.1% 5227 16:00-17:00 3428 556 161 69 55 1450 63 53 44 5 15 9 5884 24 5908 5.8% 5931 17:00-18:00 3067 388 130 84 70 1400 60 25 35 2 12 3 5261 15 5276 5.2% 5305 18:00-19:00 3172 483 199 92 43 1242 32 38 28 6 1 2 5335 3 5338 5.2% 5407 19:00-20:00 2334 648 232 113 46 1303 72 19 30 11 3 5 4808 8 4816 4.7% 4954 20:00-21:00 2315 1006 156 30 7 757 44 30 29 8 2 0 4382 2 4384 4.3% 4415 21:00-22:00 1601 791 96 40 10 675 62 54 26 9 0 0 3364 0 3364 3.3% 3446 22:00-23:00 1463 605 65 33 1 249 73 43 41 34 0 0 2607 0 2607 2.5% 2884 23:00-24:00 1369 381 50 31 2 148 61 52 41 44 0 0 2179 0 2179 2.1% 2509 00:00-01:00 963 288 29 7 4 132 54 42 42 41 0 0 1602 0 1602 1.6% 1868 01:00-02:00 653 209 32 8 1 92 53 53 44 133 0 2 1278 2 1280 1.3% 1888 02:00-03:00 652 180 27 8 2 90 57 58 51 74 0 1 1199 1 1200 1.2% 1620 03:00-04:00 577 188 35 7 0 49 58 76 112 53 0 6 1155 6 1161 1.1% 1649 04:00-05:00 1284 265 47 22 0 246 54 71 149 36 0 5 2174 5 2179 2.1% 2660 05:00-06:00 1839 586 91 44 20 769 62 89 131 48 5 6 3679 11 3690 3.6% 4130 06:00-07:00 2039 1039 188 204 135 1023 83 99 157 63 1 16 5030 17 5047 4.9% 6005 07:00-08:00 3317 1434 355 233 186 1658 169 124 197 90 14 16 7763 30 7793 7.6% 8958 Total 50187 15923 3425 1699 858 24088 1803 1679 1723 708 96 144 102093 240 102333 100.0% 108941 Percentage 49.0% 15.6% 3.3% 1.7% 0.8% 23.5% 1.8% 1.6% 1.7% 0.7% 0.1% 0.1% 99.8% 0.2% 100.0% Peak Volume= 7905 Peak Time= 08:00-09:00 Figure A6.9: Hourly Distribution of Classified Traffic Volume and Traffic Composition at Chilla Border The World Bank Group Page | 179 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Table A6.10: Classified Traffic Volume at Kalindi Kunj Border Road Name: Kalindi Kunj - Sarita Vihar Link Road Location: Kalindi Kunj Border Date: 11.07.2017 - 12.07.2017 Outer Cordon OC-10 G o o ds Light Two Axle Multi Axle C yc le Small Two A ut o / Cycle R ic k s ha ws Grand Time Big Cars Auto Buses Mini Bus G o o ds V a n Commercial Trucks Trucks a nd O t he r Total FMV Total SMV Percentage PCU Cars Wheeler (CYC) Total (GA V) Vihicles (LT) (HT) (MT) (C Y SM V) 08:00-09:00 2447 1108 337 117 37 1782 424 363 339 328 345 180 7282 525 7807 6.2% 9837 09:00-10:00 2357 1413 407 138 48 2547 580 100 81 96 285 18 7767 303 8070 6.4% 8543 10:00-11:00 2490 1539 406 162 41 2645 134 42 45 60 66 20 7564 86 7650 6.1% 7800 11:00-12:00 2030 1304 331 121 26 2358 156 235 200 69 42 14 6830 56 6886 5.5% 7454 12:00-13:00 1865 1095 309 147 46 1909 73 232 188 57 21 8 5921 29 5950 4.7% 6596 13:00-14:00 1755 1214 228 79 47 1282 84 240 231 46 3 11 5206 14 5220 4.1% 5939 14:00-15:00 1744 931 316 122 77 1353 150 254 257 53 3 8 5257 11 5268 4.2% 6218 15:00-16:00 1769 747 220 110 60 1506 161 224 197 44 13 14 5038 27 5065 4.0% 5754 16:00-17:00 1680 621 215 103 49 1327 126 155 156 35 11 14 4467 25 4492 3.6% 5035 17:00-18:00 1726 864 272 104 47 1348 94 248 193 29 16 3 4925 19 4944 3.9% 5568 18:00-19:00 1821 1017 273 98 68 1930 58 191 145 19 8 4 5620 12 5632 4.5% 5947 19:00-20:00 2030 1110 251 98 63 1971 48 56 35 34 7 9 5696 16 5712 4.5% 5770 20:00-21:00 2500 767 320 72 18 1898 313 92 59 103 79 28 6142 107 6249 5.0% 6656 21:00-22:00 2723 811 268 79 12 2157 259 59 51 217 54 22 6636 76 6712 5.3% 7401 22:00-23:00 2427 780 191 67 0 2338 230 119 156 330 52 12 6638 64 6702 5.3% 7911 23:00-24:00 1808 563 89 24 4 1095 140 100 158 261 24 15 4242 39 4281 3.4% 5422 00:00-01:00 1436 303 77 4 0 626 58 124 226 383 25 14 3237 39 3276 2.6% 5021 01:00-02:00 1690 182 60 2 6 357 32 90 181 378 56 15 2978 71 3049 2.4% 4707 02:00-03:00 1335 209 35 4 6 278 92 93 182 341 56 14 2575 70 2645 2.1% 4226 03:00-04:00 1568 168 33 3 2 355 44 84 192 318 67 28 2767 95 2862 2.3% 4329 04:00-05:00 1325 253 62 6 8 474 52 74 218 352 98 23 2824 121 2945 2.3% 4552 05:00-06:00 1353 341 121 61 18 950 82 97 219 241 133 19 3483 152 3635 2.9% 4876 06:00-07:00 1798 591 184 62 25 1328 98 125 185 201 160 21 4597 181 4778 3.8% 5747 07:00-08:00 2131 665 232 108 35 2143 101 142 194 158 204 16 5909 220 6129 4.9% 6859 Total 45808 18596 5237 1891 743 35957 3589 3539 4088 4153 1828 530 123601 2358 125959 100.0% 148169 Percentage 36.4% 14.8% 4.2% 1.5% 0.6% 28.5% 2.8% 2.8% 3.2% 3.3% 1.5% 0.4% 98.1% 1.9% 100.0% Peak Volume= 8070 Peak Time= 09:00-10:00 Figure A6.10: Hourly Distribution of Classified Traffic Volume and Traffic Composition at Kalindi Kunj Border The World Bank Group Page | 180 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Appendix 7 The World Bank Group Page | 181 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report The World Bank Group Page | 182 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Appendix 8 Table A8.1: Classified Freight Traffic Volume at Azadpur Sabzi Mandi Location Name: Azadpur Sabzi Mandi Date: 14.09.2017 - 15.09.2017 Focal Point Code: FP-01 Light Two Axle Multi Axle Cycle Rickshaws Total Goods Goods E-Rickshaw Hand Cart Animal Cart Total FMV- Total SMV- Time Trucks Trucks Trucks and Other (CY Vehicles- Percentage PCU Van (GV) Auto (GA) (EA) Goods (HC) (AC) Goods Goods (LT) (HT) (MT) SMV) Goods Goods 08:00-09:00 9 20 4 6 12 3 41 0 15 54 56 110 1.6% 222 09:00-10:00 4 25 12 12 14 4 24 0 23 71 47 118 1.7% 236 10:00-11:00 13 32 9 22 16 5 39 0 38 97 77 174 2.6% 340 11:00-12:00 7 31 2 12 5 1 18 0 31 58 49 107 1.6% 199 12:00-13:00 14 17 0 12 4 0 14 0 17 47 31 78 1.2% 140 13:00-14:00 14 7 1 3 0 0 1 0 7 25 8 33 0.5% 51 14:00-15:00 2 15 2 3 2 0 0 0 7 24 7 31 0.5% 57 15:00-16:00 2 25 1 5 2 1 2 0 12 36 14 50 0.7% 96 16:00-17:00 4 13 3 2 0 0 2 0 8 22 10 32 0.5% 54 17:00-18:00 1 16 0 2 0 0 1 0 16 19 17 36 0.5% 63 18:00-19:00 3 7 1 2 0 0 2 0 8 13 10 23 0.3% 39 19:00-20:00 0 29 0 16 18 13 3 0 4 76 7 83 1.2% 215 20:00-21:00 0 137 14 131 108 140 8 0 0 530 8 538 7.9% 1523 21:00-22:00 0 135 2 128 107 125 8 0 0 497 8 505 7.4% 1428 22:00-23:00 0 148 0 145 127 88 25 0 0 508 25 533 7.9% 1413 23:00-24:00 0 108 0 138 138 139 52 0 0 523 52 575 8.5% 1636 00:00-01:00 0 112 0 112 116 115 37 0 0 455 37 492 7.3% 1388 01:00-02:00 0 90 0 132 95 118 16 0 0 435 16 451 6.7% 1292 02:00-03:00 0 99 0 95 105 95 24 0 0 394 24 418 6.2% 1179 03:00-04:00 0 121 0 137 110 107 18 0 0 475 18 493 7.3% 1364 04:00-05:00 0 140 0 132 103 119 17 0 0 494 17 511 7.5% 1423 05:00-06:00 0 133 0 115 122 113 37 0 0 483 37 520 7.7% 1445 06:00-07:00 6 86 0 82 89 93 14 0 0 356 14 370 5.5% 1057 07:00-08:00 3 99 0 127 115 137 17 0 0 481 17 498 7.3% 1451 Total 82 1645 51 1571 1408 1416 420 0 186 6173 606 6779 100% 18307 Percentage 1.2% 24.3% 0.8% 23.2% 20.8% 20.9% 6.2% 0.0% 2.7% 91.1% 8.9% 100.0% Peak Volume= 575 Peak Time= 23:00-24:00 Figure A8.1: Hourly Distribution of Classified Traffic Volume and Traffic Composition at Azadpur Sabzi Mandi The World Bank Group Page | 183 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Table A8.2: Classified Freight Traffic Volume at Okhla Sabzi Mandi Location Name: Okhla Mandi Date: 15.09.2017 - 16.09.2017 Focal Point Code: FP-02 Light Two Axle Multi Axle Cycle Rickshaws Total Goods Goods E-Rickshaw Hand Cart Animal Cart Total FMV- Total SMV- Time Trucks Trucks Trucks and Other (CY Vehicles- Percentage PCU Van (GV) Auto (GA) (EA) Goods (HC) (AC) Goods Goods (LT) (HT) (MT) SMV) Goods Goods 08:00-09:00 10 10 0 18 5 2 0 0 4 45 4 49 5.5% 98 09:00-10:00 9 9 1 14 3 2 0 0 7 38 7 45 5.0% 87 10:00-11:00 9 3 0 12 1 0 0 0 5 25 5 30 3.3% 51 11:00-12:00 11 6 1 7 2 0 0 0 0 27 0 27 3.0% 46 12:00-13:00 9 2 0 8 0 0 0 0 8 19 8 27 3.0% 43 13:00-14:00 10 5 0 17 2 0 0 0 1 34 1 35 3.9% 64 14:00-15:00 12 4 1 13 0 0 0 0 2 30 2 32 3.6% 53 15:00-16:00 3 5 0 12 1 0 0 0 1 21 1 22 2.5% 42 16:00-17:00 3 2 0 14 1 0 0 0 5 20 5 25 2.8% 46 17:00-18:00 2 2 1 14 1 0 0 0 8 20 8 28 3.1% 51 18:00-19:00 0 1 0 4 2 0 0 0 5 7 5 12 1.3% 24 19:00-20:00 0 0 0 6 6 0 0 0 6 12 6 18 2.0% 39 20:00-21:00 6 10 0 1 0 0 1 0 0 17 1 18 2.0% 31 21:00-22:00 6 6 0 0 0 0 0 0 0 12 0 12 1.3% 19 22:00-23:00 0 11 0 8 15 0 0 0 5 34 5 39 4.4% 91 23:00-24:00 0 19 0 14 32 13 0 0 6 78 6 84 9.4% 230 00:00-01:00 0 9 0 37 19 17 0 0 4 82 4 86 9.6% 232 01:00-02:00 0 7 0 18 29 12 0 0 0 66 0 66 7.4% 191 02:00-03:00 0 10 0 8 26 12 0 0 0 56 0 56 6.3% 168 03:00-04:00 0 9 0 12 12 10 0 0 0 43 0 43 4.8% 123 04:00-05:00 0 9 0 6 8 4 0 0 0 27 0 27 3.0% 72 05:00-06:00 0 11 0 9 8 4 0 0 0 32 0 32 3.6% 82 06:00-07:00 0 19 0 6 7 8 0 0 0 40 0 40 4.5% 107 07:00-08:00 2 8 0 12 13 8 0 0 0 43 0 43 4.8% 117 Total 92 177 4 270 193 92 1 0 67 828 68 896 100% 2105 Percentage 10.3% 19.8% 0.4% 30.1% 21.5% 10.3% 0.1% 0.0% 7.5% 92.4% 7.6% 100.0% Peak Volume= 86 Peak Time= 00:00-01:00 Figure A8.2: Hourly Distribution of Classified Traffic Volume and Traffic Composition at Okhla Sabzi Mandi The World Bank Group Page | 184 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Table A8.3: Classified Freight Traffic Volume at Arya Pura Sabzi Mandi Location Name: Arya Pura Sabzi Mandi Date: 14.09.2017 - 15.09.2017 Focal Point Code: FP-03 Light Two Axle Multi Axle Cycle Rickshaws Total Goods Goods E-Rickshaw Hand Cart Animal Cart Total FMV- Total SMV- Time Trucks Trucks Trucks and Other (CY Vehicles- Percentage PCU Van (GV) Auto (GA) (EA) Goods (HC) (AC) Goods Goods (LT) (HT) (MT) SMV) Goods Goods 08:00-09:00 51 71 0 11 0 0 4 10 14 133 28 161 4.1% 269 09:00-10:00 29 60 0 7 0 0 3 12 39 96 54 150 3.8% 251 10:00-11:00 27 38 0 9 0 0 7 14 48 74 69 143 3.6% 233 11:00-12:00 30 64 0 28 0 0 4 3 39 122 46 168 4.3% 291 12:00-13:00 28 74 0 45 0 0 28 9 29 147 66 213 5.4% 385 13:00-14:00 45 125 0 50 0 3 17 0 32 223 49 272 6.9% 500 14:00-15:00 35 105 0 52 0 3 14 0 27 195 41 236 6.0% 438 15:00-16:00 42 136 0 67 0 0 3 0 15 245 18 263 6.7% 485 16:00-17:00 77 164 0 48 0 0 7 0 26 289 33 322 8.2% 569 17:00-18:00 30 39 0 14 0 0 0 0 31 83 31 114 2.9% 189 18:00-19:00 15 20 0 13 3 0 0 0 52 51 52 103 2.6% 171 19:00-20:00 11 44 0 20 1 1 0 0 30 77 30 107 2.7% 194 20:00-21:00 6 151 0 6 1 2 0 0 12 166 12 178 4.5% 351 21:00-22:00 19 158 0 16 2 0 1 0 1 195 2 197 5.0% 380 22:00-23:00 7 115 0 1 0 0 2 0 0 123 2 125 3.2% 244 23:00-24:00 5 91 0 33 1 7 1 0 0 137 1 138 3.5% 291 00:00-01:00 1 66 0 42 9 5 1 0 0 123 1 124 3.2% 269 01:00-02:00 0 38 0 57 12 7 0 0 0 114 0 114 2.9% 258 02:00-03:00 0 31 0 70 9 11 0 0 0 121 0 121 3.1% 279 03:00-04:00 0 24 0 49 2 7 0 0 0 82 0 82 2.1% 184 04:00-05:00 0 9 0 61 1 5 2 0 0 76 2 78 2.0% 170 05:00-06:00 0 37 0 63 1 6 1 0 0 107 1 108 2.8% 232 06:00-07:00 3 94 0 110 0 11 1 0 0 218 1 219 5.6% 463 07:00-08:00 22 84 0 54 0 5 9 7 7 165 23 188 4.8% 364 Total 483 1838 0 926 42 73 105 55 402 3362 562 3924 100% 7458 Percentage 12.3% 46.8% 0.0% 23.6% 1.1% 1.9% 2.7% 1.4% 10.2% 85.7% 14.3% 100.0% Peak Volume= 322 Peak Time= 16:00-17:00 Figure A8.3: Hourly Distribution of Classified Traffic Volume and Traffic Composition at Arya Pura Sabzi Mandi The World Bank Group Page | 185 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Table A8.4: Classified Freight Traffic Volume at Ghanta Ghar Sabzi Mandi Location Name: Ghanta Ghar Sabzi Mandi Date: 12.09.2017 - 13.09.2017 Focal Point Code: FP-04 Light Two Axle Multi Axle Cycle Rickshaws Total Goods Goods E-Rickshaw Hand Cart Animal Cart Total FMV- Total SMV- Time Trucks Trucks Trucks and Other (CY Vehicles- Percentage PCU Van (GV) Auto (GA) (EA) Goods (HC) (AC) Goods Goods (LT) (HT) (MT) SMV) Goods Goods 08:00-09:00 32 22 0 13 0 0 0 0 0 67 0 67 0.8% 108 09:00-10:00 37 42 0 21 1 0 0 0 0 101 0 101 1.3% 173 10:00-11:00 42 54 0 41 4 1 0 0 0 142 0 142 1.8% 257 11:00-12:00 39 211 0 100 3 1 0 0 0 354 0 354 4.4% 682 12:00-13:00 50 283 0 184 3 1 0 0 0 521 0 521 6.5% 1008 13:00-14:00 13 260 0 121 1 0 0 0 0 395 0 395 4.9% 781 14:00-15:00 35 289 0 125 0 1 0 0 0 450 0 450 5.6% 875 15:00-16:00 42 331 0 201 2 1 0 0 0 577 0 577 7.2% 1125 16:00-17:00 25 217 0 150 0 3 0 0 0 395 0 395 4.9% 778 17:00-18:00 28 197 0 127 0 1 0 0 0 353 0 353 4.4% 686 18:00-19:00 11 60 0 72 0 0 0 0 0 143 0 143 1.8% 277 19:00-20:00 8 102 0 38 0 0 0 0 0 148 0 148 1.9% 290 20:00-21:00 9 261 0 39 13 0 0 0 0 322 0 322 4.0% 650 21:00-22:00 16 261 0 55 29 0 0 0 0 361 0 361 4.5% 738 22:00-23:00 13 205 0 68 56 2 0 0 0 344 0 344 4.3% 739 23:00-24:00 12 117 0 165 120 28 0 0 0 442 0 442 5.5% 1064 00:00-01:00 4 59 0 104 115 39 0 0 0 321 0 321 4.0% 851 01:00-02:00 2 42 0 109 84 96 0 0 0 333 0 333 4.2% 988 02:00-03:00 2 28 0 110 66 69 0 0 0 275 0 275 3.4% 787 03:00-04:00 0 37 0 87 60 67 0 0 0 251 0 251 3.1% 730 04:00-05:00 5 120 0 85 98 136 0 0 0 444 0 444 5.6% 1322 05:00-06:00 4 119 0 195 95 55 0 0 0 468 0 468 5.9% 1165 06:00-07:00 12 109 0 143 92 29 0 0 0 385 0 385 4.8% 925 07:00-08:00 11 161 0 125 70 23 0 0 0 390 0 390 4.9% 899 Total 452 3587 0 2478 912 553 0 0 0 7982 0 7982 100% 17897 Percentage 5.7% 44.9% 0.0% 31.0% 11.4% 6.9% 0.0% 0.0% 0.0% 100.0% 0.0% 100.0% Peak Volume= 577 Peak Time= 15:00-16:00 Figure A8.4: Hourly Distribution of Classified Traffic Volume and Traffic Composition at Ghanta Ghar Sabzi Mandi The World Bank Group Page | 186 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Table A8.5: Classified Freight Traffic Volume at Old Delhi Sabzi Mandi Location Name: Old Delhi Sabzi Mandi Date: 16.09.2017 - 17.09.2017 Focal Point Code: FP-05 Light Two Axle Multi Axle Cycle Rickshaws Total Goods Goods E-Rickshaw Hand Cart Animal Cart Total FMV- Total SMV- Time Trucks Trucks Trucks and Other (CY Vehicles- Percentage PCU Van (GV) Auto (GA) (EA) Goods (HC) (AC) Goods Goods (LT) (HT) (MT) SMV) Goods Goods 08:00-09:00 2 15 0 8 0 0 20 0 0 25 20 45 1.8% 88 09:00-10:00 7 43 0 22 5 0 38 0 0 77 38 115 4.6% 229 10:00-11:00 13 81 0 48 6 10 80 0 0 158 80 238 9.5% 497 11:00-12:00 10 120 0 62 8 8 55 0 0 208 55 263 10.5% 546 12:00-13:00 14 78 0 48 0 0 48 0 0 140 48 188 7.5% 365 13:00-14:00 5 66 0 56 0 0 40 0 0 127 40 167 6.6% 330 14:00-15:00 11 52 0 49 0 0 32 0 0 112 32 144 5.7% 279 15:00-16:00 12 60 0 41 2 0 37 0 0 115 37 152 6.0% 296 16:00-17:00 12 45 0 24 0 0 52 0 0 81 52 133 5.3% 256 17:00-18:00 11 24 0 7 0 0 41 0 0 42 41 83 3.3% 157 18:00-19:00 14 14 0 3 0 0 42 0 0 31 42 73 2.9% 135 19:00-20:00 5 15 0 0 0 0 44 0 0 20 44 64 2.5% 124 20:00-21:00 14 26 0 5 0 0 24 0 0 45 24 69 2.7% 127 21:00-22:00 12 22 0 25 17 6 16 0 0 82 16 98 3.9% 218 22:00-23:00 7 8 0 26 26 20 9 0 0 87 9 96 3.8% 262 23:00-24:00 1 1 0 52 41 52 6 0 0 147 6 153 6.1% 476 00:00-01:00 0 0 0 36 31 41 0 0 0 108 0 108 4.3% 350 01:00-02:00 0 1 0 27 21 27 0 0 0 76 0 76 3.0% 241 02:00-03:00 0 1 0 12 20 30 0 0 0 63 0 63 2.5% 221 03:00-04:00 0 1 0 15 21 22 0 0 0 59 0 59 2.3% 194 04:00-05:00 0 5 0 9 12 15 0 0 0 41 0 41 1.6% 132 05:00-06:00 0 7 0 8 0 5 0 0 0 20 0 20 0.8% 53 06:00-07:00 0 12 0 11 2 3 0 0 0 28 0 28 1.1% 66 07:00-08:00 5 9 0 9 0 3 11 0 0 26 11 37 1.5% 78 Total 155 706 0 603 212 242 595 0 0 1918 595 2513 100% 5719 Percentage 6.2% 28.1% 0.0% 24.0% 8.4% 9.6% 23.7% 0.0% 0.0% 76.3% 23.7% 100.0% Peak Volume= 263 Peak Time= 11:00-12:00 Figure A8.5: Hourly Distribution of Classified Traffic Volume and Traffic Composition at Old Delhi Sabzi Mandi The World Bank Group Page | 187 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Table A8.6: Classified Freight Traffic Volume at Shahadara Sabzi Mandi Figure A8.6: Hourly Distribution of Classified Traffic Volume and Traffic Composition at Shahadara Sabzi Mandi The World Bank Group Page | 188 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Table A8.7: Classified Freight Traffic Volume at Mandawali Sabzi Mandi Figure A8.7: Hourly Distribution of Classified Traffic Volume and Traffic Composition at Mandawali Sabzi Mandi The World Bank Group Page | 189 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Table A8.8: Classified Freight Traffic Volume at Shahadara Figure A8.8: Hourly Distribution of Classified Traffic Volume and Traffic Composition at Shahadara The World Bank Group Page | 190 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Table A8.9: Classified Freight Traffic Volume at Ghazipur Sabzi Mandi Figure A8.9: Hourly Distribution of Classified Traffic Volume and Traffic Composition at Ghazipur Sabzi Mandi The World Bank Group Page | 191 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Table A8.10: Classified Freight Traffic Volume at Connaught Place Figure A8.10: Hourly Distribution of Classified Traffic Volume and Traffic Composition at Connaught Place The World Bank Group Page | 192 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Table A8.11: Classified Freight Traffic Volume at Chandini Chowk Figure A8.11: Hourly Distribution of Classified Traffic Volume and Traffic Composition at Chandini Chowk The World Bank Group Page | 193 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Table A8.12: Classified Freight Traffic Volume at Sarojini Nagar Market Figure A8.12: Hourly Distribution of Classified Traffic Volume and Traffic Composition at Sarojini Nagar Market The World Bank Group Page | 194 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Table A8.13: Classified Freight Traffic Volume at Lajpat Nagar Market Figure A8.13: Hourly Distribution of Classified Traffic Volume and Traffic Composition at Lajpat Nagar Market The World Bank Group Page | 195 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Table A8.14: Classified Freight Traffic Volume at Pitampura Market Figure A8.14: Hourly Distribution of Classified Traffic Volume and Traffic Composition at Pitampura Market The World Bank Group Page | 196 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Table A8.15: Classified Freight Traffic Volume at Nehru Place Figure A8.15: Hourly Distribution of Classified Traffic Volume and Traffic Composition at Nehru Place The World Bank Group Page | 197 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Table A8.16: Classified Freight Traffic Volume at Gandhi Nagar Figure A8.16: Hourly Distribution of Classified Traffic Volume and Traffic Composition at Gandhi Nagar The World Bank Group Page | 198 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Table A8.17: Classified Freight Traffic Volume at Rajouri Garden Figure A8.17: Hourly Distribution of Classified Traffic Volume and Traffic Composition at Rajouri Garden The World Bank Group Page | 199 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Table A8.18: Classified Freight Traffic Volume at Narela Figure A8.18: Hourly Distribution of Classified Traffic Volume and Traffic Composition at Narela The World Bank Group Page | 200 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Table A8.19: Classified Freight Traffic Volume at Najafgarh Figure A8.19: Hourly Distribution of Classified Traffic Volume and Traffic Composition at Najafgarh The World Bank Group Page | 201 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Table A8.20: Classified Freight Traffic Volume at Keshopur Sabzi Mandi Figure A8.20: Hourly Distribution of Classified Traffic Volume and Traffic Composition at Keshopur Sabzi Mandi The World Bank Group Page | 202 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Appendix 9 Table A9.1: Classified Traffic Volume at Rajghat on Ring Road Road Name: Ring Road Location: Rajghat Date: 9/7/2017 Mid Block MB-01 Goods Light Two Axle Multi Axle Cycle Small Two Cycle Grand Time Big Cars Auto Buses Mini Bus Auto/Good Commercial Trucks Trucks Rickshaws Total FMV Total SMV Percentage PCU Cars Wheeler Vihicles (CYC) and Other Total s Va (GAV) (HT) (MT) (LT) (CY SMV) 08:00-09:00 1066 321 630 88 27 1663 65 13 1 2 31 21 3876 52 3928 2.8% 3884 09:00-10:00 2465 683 1020 113 60 3945 164 21 13 4 96 47 8488 143 8631 6.2% 8243 10:00-11:00 2975 939 1558 108 34 3478 188 25 7 8 86 45 9320 131 9451 6.8% 9271 11:00-12:00 2845 1028 2107 103 17 3290 300 41 12 1 77 49 9744 126 9870 7.1% 9876 12:00-13:00 2650 846 1855 95 17 2561 349 41 13 5 45 75 8432 120 8552 6.2% 8743 13:00-14:00 2628 758 2189 93 28 2401 416 28 14 6 36 58 8561 94 8655 6.3% 8989 14:00-15:00 2937 574 2077 58 12 2409 419 42 16 5 46 67 8549 113 8662 6.3% 8894 15:00-16:00 2270 505 1740 61 11 2973 394 47 17 7 71 71 8025 142 8167 5.9% 8184 16:00-17:00 2788 762 2111 63 5 3646 377 32 14 7 73 68 9805 141 9946 7.2% 9842 17:00-18:00 3128 853 1730 68 12 5118 210 24 12 7 180 83 11162 263 11425 8.3% 10757 18:00-19:00 3426 900 1664 77 14 5564 197 21 8 56 257 66 11927 323 12250 8.9% 11585 19:00-20:00 3223 1126 1921 59 7 3436 215 45 26 76 187 50 10134 237 10371 7.5% 10401 20:00-21:00 2278 606 1296 46 4 3371 305 66 101 69 23 38 8142 61 8203 5.9% 8352 21:00-22:00 1228 523 906 19 0 1980 275 95 53 69 4 34 5148 38 5186 3.7% 5458 22:00-23:00 629 363 409 8 3 1061 180 130 38 43 0 17 2864 17 2881 2.1% 3107 23:00-24:00 460 355 331 9 2 406 91 153 120 71 1 6 1998 7 2005 1.4% 2603 00:00-01:00 356 255 206 7 2 176 46 107 84 107 0 1 1346 1 1347 1.0% 1980 01:00-02:00 211 208 107 10 1 46 20 88 98 111 0 0 900 0 900 0.7% 1569 02:00-03:00 90 175 95 7 1 35 31 110 140 101 0 0 785 0 785 0.6% 1514 03:00-04:00 85 158 106 7 0 13 3 80 114 150 0 0 716 0 716 0.5% 1542 04:00-05:00 153 102 131 20 0 78 17 69 87 166 1 4 823 5 828 0.6% 1674 05:00-06:00 262 106 161 30 3 298 50 43 61 86 4 8 1100 12 1112 0.8% 1604 06:00-07:00 454 265 398 48 5 361 149 39 96 45 27 39 1860 66 1926 1.4% 2466 07:00-08:00 595 245 522 71 3 764 125 34 33 9 68 42 2401 110 2511 1.8% 2733 Total 39202 12656 25270 1268 268 49073 4586 1394 1178 1211 1313 889 136106 2202 138308 100.0% 143270 Percentage 28.3% 9.2% 18.3% 0.9% 0.2% 35.5% 3.3% 1.0% 0.9% 0.9% 0.9% 0.6% 98.4% 1.6% 100.0% Peak Volume= 12250 Peak Time= 18:00-19:00 Figure A9.1: Hourly Distribution of Classified Traffic Volume and Traffic Composition at Rajghat on Ring Road The World Bank Group Page | 203 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Table A9.2: Classified Traffic Volume at Rajghat at Connaught Place (Regal Cinema) Road Name: Cannaught Place Location: Regal Cinema(CP) Date: 9/11/2017 Mid Block MB-02 Goods Light Two Axle Multi Axle Cycle Small Two Cycle Grand Time Big Cars Auto Buses Mini Bus Auto/Good Commercial Trucks Trucks Rickshaws Total FMV Total SMV Percentage PCU Cars Wheeler Vihicles (CYC) and Other Total s Va (GAV) (HT) (MT) (LT) (CY SMV) 08:00-09:00 859 212 541 255 2 691 4 8 0 0 35 8 2572 43 2615 4.1% 3055 09:00-10:00 1411 227 1059 272 5 1299 9 4 3 0 57 17 4289 74 4363 6.9% 4792 10:00-11:00 1354 258 1186 269 3 1096 5 4 3 0 32 3 4178 35 4213 6.6% 4713 11:00-12:00 1362 420 1011 507 2 1298 16 9 7 1 22 10 4633 32 4665 7.3% 5583 12:00-13:00 1568 409 901 458 5 894 21 3 3 0 31 2 4262 33 4295 6.7% 5176 13:00-14:00 1089 313 699 235 6 718 13 5 6 1 34 1 3085 35 3120 4.9% 3564 14:00-15:00 1276 388 779 179 1 1045 10 8 3 0 37 1 3689 38 3727 5.9% 3978 15:00-16:00 1493 329 765 109 4 1412 24 7 7 0 37 3 4150 40 4190 6.6% 4225 16:00-17:00 1680 421 826 151 3 1174 27 3 1 0 48 4 4286 52 4338 6.8% 4510 17:00-18:00 1581 384 956 269 1 1044 8 8 1 0 31 5 4252 36 4288 6.7% 4754 18:00-19:00 1706 468 971 272 1 1365 18 6 0 0 20 8 4807 28 4835 7.6% 5239 19:00-20:00 1400 578 848 306 1 1229 20 6 0 0 30 9 4388 39 4427 7.0% 4905 20:00-21:00 717 374 362 254 2 917 13 2 1 0 26 0 2642 26 2668 4.2% 3018 21:00-22:00 684 221 311 84 1 484 2 1 2 1 23 4 1791 27 1818 2.9% 1928 22:00-23:00 599 204 282 43 1 406 3 3 2 0 9 4 1543 13 1556 2.4% 1602 23:00-24:00 820 188 312 15 0 232 11 10 7 2 12 2 1597 14 1611 2.5% 1672 00:00-01:00 460 173 238 9 0 137 1 7 6 2 1 0 1033 1 1034 1.6% 1088 01:00-02:00 259 121 171 6 0 87 0 2 1 0 0 0 647 0 647 1.0% 674 02:00-03:00 232 77 91 6 0 75 0 0 1 1 0 0 483 0 483 0.8% 500 03:00-04:00 169 46 68 2 0 63 3 13 8 2 0 0 374 0 374 0.6% 407 04:00-05:00 228 57 199 9 0 70 12 14 6 1 0 2 596 2 598 0.9% 668 05:00-06:00 445 97 274 34 2 106 3 1 1 2 5 0 965 5 970 1.5% 1077 06:00-07:00 386 146 216 107 9 165 1 3 0 1 21 1 1034 22 1056 1.7% 1276 07:00-08:00 610 195 284 209 16 417 3 3 0 0 8 0 1737 8 1745 2.7% 2131 Total 22388 6306 13350 4060 65 16424 227 130 69 14 519 84 63033 603 63636 100.0% 70533 Percentage 35.2% 9.9% 21.0% 6.4% 0.1% 25.8% 0.4% 0.2% 0.1% 0.0% 0.8% 0.1% 99.1% 0.9% 100.0% Peak Volume= 4835 Peak Time= 18:00-19:00 Figure A9.2: Hourly Distribution of Classified Traffic Volume and Traffic Composition at Connaught Place (Regal Cinema) The World Bank Group Page | 204 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Table A9.3: Classified Traffic Volume at Rajghat at Naraina (Ring Road) Road Name: Ring Road Location: Naraina Date: 9/7/2017 Mid Block MB-03 Goods Light Two Axle Multi Axle Cycle Small Two Cycle Grand Time Big Cars Auto Buses Mini Bus Auto/Good Commercial Trucks Trucks Rickshaws Total FMV Total SMV Percentage PCU Cars Wheeler Vihicles (CYC) and Other Total s Va (GAV) (HT) (MT) (LT) (CY SMV) 08:00-09:00 3438 766 290 195 43 1434 201 59 91 17 244 13 6534 257 6791 3.6% 7180 09:00-10:00 4643 1038 336 168 22 3646 161 90 110 13 131 19 10227 150 10377 5.5% 10226 10:00-11:00 4898 1057 460 245 21 5523 221 120 129 11 71 16 12685 87 12772 6.8% 12434 11:00-12:00 4009 1094 544 216 4 4217 426 145 266 18 89 23 10939 112 11051 5.9% 11389 12:00-13:00 3956 928 582 204 7 3148 531 375 352 41 91 12 10124 103 10227 5.5% 11232 13:00-14:00 2447 1077 602 225 7 3139 498 366 268 33 59 8 8662 67 8729 4.7% 9580 14:00-15:00 2599 1378 619 224 8 3375 427 294 197 19 26 15 9140 41 9181 4.9% 9733 15:00-16:00 3161 1456 727 203 11 3242 527 347 259 18 20 14 9951 34 9985 5.3% 10752 16:00-17:00 2915 1296 673 209 11 3357 473 332 192 16 23 9 9474 32 9506 5.1% 10066 17:00-18:00 4596 1650 698 219 17 3950 302 164 54 8 78 17 11658 95 11753 6.3% 11699 18:00-19:00 7636 2229 761 195 45 3875 196 166 54 7 128 4 15164 132 15296 8.2% 15166 19:00-20:00 7531 2323 718 321 33 3845 330 264 65 20 74 5 15450 79 15529 8.3% 15849 20:00-21:00 6036 1153 773 299 51 2579 417 179 120 79 30 6 11686 36 11722 6.3% 12683 21:00-22:00 3101 872 378 167 17 2087 381 248 169 164 53 0 7584 53 7637 4.1% 8742 22:00-23:00 3227 619 202 96 22 1105 290 227 248 164 10 2 6200 12 6212 3.3% 7515 23:00-24:00 1130 655 96 37 12 604 276 418 406 478 3 1 4112 4 4116 2.2% 6901 00:00-01:00 1388 518 80 5 2 235 143 321 398 533 2 1 3623 3 3626 1.9% 6488 01:00-02:00 786 301 63 5 3 62 231 293 355 427 0 0 2526 0 2526 1.3% 5003 02:00-03:00 750 149 58 0 0 47 230 245 315 414 2 0 2208 2 2210 1.2% 4525 03:00-04:00 680 175 71 3 2 185 343 300 340 424 3 0 2523 3 2526 1.3% 4986 04:00-05:00 720 345 77 17 7 128 253 278 326 396 1 2 2547 3 2550 1.4% 4878 05:00-06:00 930 569 64 65 12 245 237 239 273 263 6 5 2897 11 2908 1.6% 4706 06:00-07:00 1335 674 102 155 41 654 329 345 257 121 17 10 4013 27 4040 2.2% 5519 07:00-08:00 2436 1103 261 171 50 1035 384 271 184 41 21 5 5936 26 5962 3.2% 6978 Total 74348 23425 9235 3644 448 51717 7807 6086 5428 3725 1182 187 185863 1369 187232 100.0% 214228 Percentage 39.7% 12.5% 4.9% 1.9% 0.2% 27.6% 4.2% 3.3% 2.9% 2.0% 0.6% 0.1% 99.3% 0.7% 100.0% Peak Volume= 15529 Peak Time= 19:00-20:00 Figure A9.3: Hourly Distribution of Classified Traffic Volume and Traffic Composition at Naraina (Ring Road) The World Bank Group Page | 205 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Table A9.4: Classified Traffic Volume on ITO Bridge Road Name: ITO Barrage Bridge Location: ITO Date: 9/8/2017 Mod Block MB-04 Goods Light Two Axle Multi Axle Cycle Small Two Cycle Grand Time Big Cars Auto Buses Mini Bus Auto/Good Commercial Trucks Trucks Rickshaws Total FMV Total SMV Percentage PCU Cars Wheeler Vihicles (CYC) and Other Total s Va (GAV) (HT) (MT) (LT) (CY SMV) 08:00-09:00 1334 183 392 238 6 987 15 6 3 1 96 18 3165 114 3279 1.9% 3574 09:00-10:00 3969 378 740 202 4 3322 52 13 1 0 275 31 8681 306 8987 5.1% 8625 10:00-11:00 5291 619 960 267 10 5294 48 44 0 2 213 20 12535 233 12768 7.3% 12137 11:00-12:00 4467 938 1027 224 5 4262 76 44 1 1 96 14 11045 110 11155 6.4% 10772 12:00-13:00 4288 966 1170 221 8 2516 91 47 2 1 89 16 9310 105 9415 5.4% 9510 13:00-14:00 3558 856 1120 265 3 2663 144 81 4 0 35 23 8694 58 8752 5.0% 8958 14:00-15:00 4262 870 1068 181 8 2932 138 114 3 0 39 14 9576 53 9629 5.5% 9599 15:00-16:00 5096 902 920 200 14 3622 142 97 5 0 67 22 10998 89 11087 6.3% 10887 16:00-17:00 6196 925 820 273 8 4161 172 86 4 0 77 17 12645 94 12739 7.3% 12524 17:00-18:00 7977 1017 887 288 9 4014 100 57 2 0 181 15 14351 196 14547 8.3% 14305 18:00-19:00 9452 1282 881 261 8 4656 41 14 4 0 168 19 16599 187 16786 9.6% 16289 19:00-20:00 6480 1128 791 183 4 4713 26 19 36 2 169 27 13382 196 13578 7.7% 12958 20:00-21:00 3695 779 597 143 10 3030 78 43 33 10 95 5 8418 100 8518 4.9% 8292 21:00-22:00 3045 883 685 114 13 2075 43 48 47 19 36 0 6972 36 7008 4.0% 7055 22:00-23:00 2371 780 518 91 3 1049 37 89 79 21 37 0 5038 37 5075 2.9% 5377 23:00-24:00 1599 589 481 35 1 860 40 73 116 29 16 0 3823 16 3839 2.2% 4173 00:00-01:00 976 502 430 9 1 768 26 54 91 61 4 0 2918 4 2922 1.7% 3269 01:00-02:00 805 272 319 6 1 369 28 164 142 44 0 0 2150 0 2150 1.2% 2669 02:00-03:00 771 158 215 6 1 271 17 95 190 38 0 0 1762 0 1762 1.0% 2319 03:00-04:00 695 133 153 9 0 143 12 99 172 70 0 0 1486 0 1486 0.8% 2143 04:00-05:00 778 128 198 26 1 131 0 84 147 79 0 0 1572 0 1572 0.9% 2244 05:00-06:00 1008 156 371 75 7 139 33 147 242 35 24 0 2213 24 2237 1.3% 3118 06:00-07:00 1592 201 462 154 13 557 48 76 135 3 59 13 3241 72 3313 1.9% 3907 07:00-08:00 1106 299 366 254 12 712 50 53 77 1 67 27 2930 94 3024 1.7% 3628 Total 80811 14944 15571 3725 150 53246 1457 1647 1536 417 1843 281 173504 2124 175628 100.0% 178333 Percentage 46.0% 8.5% 8.9% 2.1% 0.1% 30.3% 0.8% 0.9% 0.9% 0.2% 1.0% 0.2% 98.8% 1.2% 100.0% Peak Volume= 16786 Peak Time= 18:00-19:00 Figure A9.4: Hourly Distribution of Classified Traffic Volume and Traffic Composition on ITO Bridge The World Bank Group Page | 206 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Table A9.5: Classified Traffic Volume at Toll Plaza on NH -24 Bypass Road Name: NH 24 Location: UP & UK RTO Toll Tax Date: 9/8/2017 Mid Block MB-05 Goods Light Two Axle Multi Axle Cycle Small Two Cycle Grand Time Big Cars Auto Buses Mini Bus Auto/Good Commercial Trucks Trucks Rickshaws Total FMV Total SMV Percentage PCU Cars Wheeler Vihicles (CYC) and Other Total s Va (GAV) (HT) (MT) (LT) (CY SMV) 08:00-09:00 3889 421 495 156 122 2092 6 27 6 8 179 7 7222 186 7408 5.1% 7389 09:00-10:00 5621 875 566 292 206 2019 7 17 18 4 133 14 9625 147 9772 6.8% 10173 10:00-11:00 4994 867 483 247 209 2065 24 10 12 2 83 3 8913 86 8999 6.2% 9290 11:00-12:00 4119 947 544 198 181 1900 96 64 44 13 36 3 8106 39 8145 5.6% 8553 12:00-13:00 3308 982 601 210 150 1504 171 183 105 22 45 7 7236 52 7288 5.0% 8047 13:00-14:00 2903 830 537 219 211 1430 157 163 66 15 14 4 6531 18 6549 4.5% 7287 14:00-15:00 2520 780 508 231 180 1488 186 140 93 18 29 10 6144 39 6183 4.3% 6957 15:00-16:00 3282 775 667 160 137 1405 214 149 78 18 15 10 6885 25 6910 4.8% 7547 16:00-17:00 3270 815 660 195 154 1356 156 118 53 8 34 8 6785 42 6827 4.7% 7422 17:00-18:00 4083 844 774 187 171 1740 147 165 64 7 40 10 8182 50 8232 5.7% 8790 18:00-19:00 4244 782 639 204 182 2107 170 148 10 8 53 4 8494 57 8551 5.9% 8925 19:00-20:00 3908 976 457 263 180 2013 109 134 4 1 26 1 8045 27 8072 5.6% 8487 20:00-21:00 3920 887 852 209 78 2279 235 175 8 4 10 0 8647 10 8657 6.0% 8984 21:00-22:00 3226 721 705 168 41 1547 265 141 44 32 9 5 6890 14 6904 4.8% 7436 22:00-23:00 2791 539 426 101 10 935 96 151 129 101 11 6 5279 17 5296 3.7% 6092 23:00-24:00 1989 583 275 45 12 287 139 362 487 318 0 3 4497 3 4500 3.1% 6924 00:00-01:00 1371 475 255 26 6 111 107 378 571 502 0 1 3802 1 3803 2.6% 7026 01:00-02:00 968 316 145 3 0 111 86 447 619 607 0 1 3302 1 3303 2.3% 6940 02:00-03:00 478 215 97 17 1 88 64 340 520 585 0 0 2405 0 2405 1.7% 5727 03:00-04:00 417 178 153 22 6 206 26 322 423 390 0 0 2143 0 2143 1.5% 4557 04:00-05:00 399 213 164 31 5 256 32 235 388 395 8 0 2118 8 2126 1.5% 4450 05:00-06:00 933 411 242 45 5 362 82 199 295 228 4 3 2802 7 2809 1.9% 4390 06:00-07:00 1345 430 497 102 16 956 109 235 217 197 60 1 4104 61 4165 2.9% 5511 07:00-08:00 2608 438 523 109 32 1142 108 201 70 146 70 13 5377 83 5460 3.8% 6306 Total 66586 15300 11265 3440 2295 29399 2792 4504 4324 3629 859 114 143534 973 144507 100.0% 173210 Percentage 46.1% 10.6% 7.8% 2.4% 1.6% 20.3% 1.9% 3.1% 3.0% 2.5% 0.6% 0.1% 99.3% 0.7% 100.0% Peak Volume= 9772 Peak Time= 09:00-10:00 Figure A9.5: Hourly Distribution of Classified Traffic Volume and Traffic Composition at Toll Plaza on NH -24 Bypass The World Bank Group Page | 207 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Appendix 10 Population Population Zone Area Zone Area Zone Name (2011) in Zone Name (2011) in ID (Km2) ID (Km2) '000 '000 1 Narela 41912 8.41 181 Khanpur 76517 3.31 2 Bankner 43928 17.10 182 Ambedkar nagar 51800 0.50 3 Alipur 29634 7.86 183 Madangir 54483 0.47 4 Bakhtawar pur 89451 56.03 184 Pushp vihar 46652 1.82 5 Bhalswa jahangir pur 58228 8.93 185 Tughlakabad extn. 50911 2.27 6 Mukund pur 92583 5.63 186 Sangam vihar west 46346 2.97 7 Burari 77533 19.48 187 Sangam vihar central 45811 1.68 8 Jharoda 82019 12.29 188 Sangam vihar east 48978 4.16 9 Malka ganj 74583 0.91 189 Chirag Delhi 50500 2.40 10 Timar pur 66949 4.42 190 Chitranjan park 63595 2.15 11 Mukaherjee nagar 60832 2.61 191 Shahpur jat 66565 4.17 12 G.T.B. nagar 54219 2.81 192 Greater kailash I 52498 3.87 13 Dhir pur 15151 4.54 193 Sriniwaspuri 57154 4.03 14 Adarsh 64635 1.29 194 East of kailash 45941 2.82 15 Sarai pipal thala 54478 1.83 195 Govind puri 57441 0.49 16 Jahangir puri-1 54478 0.63 196 Kalkaji 60119 1.11 17 Samaypur badli 54517 3.07 197 Tughlakabad 19229 3.64 18 Libas pur 76100 6.04 198 Pul pehlad 50725 3.08 19 Bhalswa 78176 3.41 199 Tehkhand 47535 2.37 20 Jahangir puri-ii 63284 0.57 200 Harkesh nagar 52773 2.23 21 Rohini sec 16, 17 63114 2.23 201 Jaitpur 86765 2.97 22 Rithala 59747 2.05 202 Meetheypur 67294 3.00 23 Budh vihar 73589 1.91 203 Badarpur 57479 2.38 24 Vijay vihar 63377 1.88 204 Molarband 64789 1.25 25 Puth kalan 65000 1.18 205 Zakir nagar 63006 3.00 26 Sahibabad dault pur 12362 1.83 206 Okhla 35129 2.15 27 Begumpur 50000 34.45 207 Madanpur khadar 36953 4.90 28 Bawana 175000 56.56 208 Sarita vihar 35508 3.70 29 Karala 65000 33.30 209 Mayur vihar phase-I 51898 4.53 30 Mundaka 70000 19.56 210 Dallopura 57327 1.37 31 Nangloi jat west 63541 1.70 211 Trilok puri 61681 0.95 32 Nilothi 38030 2.66 212 New ashok nagar 58810 2.70 33 Pratap vihar 64942 0.70 213 Kalyan puri 49027 1.01 34 Nithari 70000 5.76 214 Khichripur 14925 1.23 35 Kirari suleman nagar 74981 2.74 215 Kondli 45494 1.68 36 Prem nagar 72713 1.35 216 Gharoli 56706 1.47 37 Sultanpuri east 60038 0.78 217 Vinod nagar 61249 0.64 38 Mangol puri north 55483 0.61 218 Mandaoli 66351 0.82 39 Sultanpur majra 51242 0.60 219 Mayur vihar phase-ii 63132 0.80 40 Sultanpuri south 44116 0.81 220 Patparganj 56422 0.83 41 Guru harkishan nagar 57203 2.66 221 Kishan kunj 60616 1.30 42 Peragharhi 51099 1.84 222 Laxmi nagar 50135 0.91 43 Nangloi east 68586 1.96 223 Shakarpur 62562 1.16 The World Bank Group Page | 208 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report 44 Qummruddin nagar 27000 2.69 224 Pandav nagar 65906 1.54 45 Rohini south 76811 2.09 225 Anand vihar 13654 2.81 46 Mangolpuri east 69053 0.78 226 Vilshwash nagar 35372 1.47 47 Mangolpuri 60205 0.98 227 I.P. Extension 32696 0.79 48 Mangolpuri west 54592 1.34 228 Preet vihar 30841 1.21 49 Rohini north 62990 3.42 229 Krishna nagar 59921 0.86 50 Rohini central 63481 3.08 230 Geeta colony 65356 1.12 51 Rohini east 56884 2.31 231 Ghondli 64275 0.94 52 Naharpur 58248 0.80 232 Anarkali 33778 0.85 53 Pitampura south 62398 2.45 233 Dharam pura 52024 1.05 54 Pitampura north 63781 2.31 234 Gandhi nagar 52664 0.31 55 Shalimar bagh north 61228 1.98 235 Azad nagar 63973 1.01 56 Shalimar bagh south 61501 2.23 236 Raghubar pura 58973 0.97 57 Paschim vihar south 118170 3.03 237 Shahdara 65351 1.43 58 Paschim vihar north 53900 2.07 238 Jhilmil 56570 1.00 59 Rani bagh 51399 3.03 239 Vivek vihar 54605 0.45 60 Saraswati vihar 23676 0.92 240 Dilshad colony 54049 0.96 61 Tri nagar 66752 0.96 241 Dilshad garden 28790 2.06 62 Rampura 65051 2.43 242 New seema puri 56770 1.01 63 Kohat enclave 1.99 243 Nand nagri 58091 0.68 64 Shakur pur 53124 1.00 244 Sunder nagari 58072 0.74 65 Nimri colony 62140 1.22 245 Durga puri 60211 0.92 66 Sawan park 66433 2.38 246 Ashok nagar 58384 0.91 67 Wazirpur 70293 1.70 247 Ram nagar 63444 1.60 68 Ashok vihar 70440 2.03 248 Welcome colony 58604 1.14 69 Kamla nagar 64514 2.18 249 Chauhan bangar 52328 0.54 70 Rana pratap bagh 58438 1.27 250 Zaffrabad 55833 0.51 71 Sangam park 53687 0.98 251 New usmanpur 29330 0.00 72 Model town 51920 2.81 252 Mauj pur 64370 0.67 73 Shastri nagar 70481 1.47 253 Bhajanpura 51479 1.11 74 Inder lok colony 60405 2.38 254 Brahampuri 61186 0.85 75 Kishan ganj 71983 1.28 255 Ghonda 52367 0.62 76 Deputy ganj 68098 1.00 256 Yamuna vihar 67694 1.73 77 Kashmere gate 55415 3.29 257 Subhash mohalla 59937 0.58 78 Majnu ka tilla 57142 3.53 258 Kardam puri 55848 1.42 79 Jama massjid 56086 1.05 259 Janta colony 55516 0.67 80 Chandi chowk 53701 0.99 260 Babar pur 56332 0.69 81 Minto road 51226 2.56 261 Jiwanpur 58637 1.75 82 Kuncha pandit 59922 0.34 262 Gokalpur 59311 0.99 83 Bazar sita ram 47202 0.34 263 Saboli 51163 1.65 84 Turkman gate 48370 0.26 264 Harsh vihar 56830 2.44 85 Idgah road 52179 0.81 265 Shiv vihar 63752 1.43 86 Ballimaran 55584 0.47 266 Karawal nagar east 50503 0.97 87 Ram nagar 69462 0.47 267 Nehru vihar 56968 0.28 88 Qasabpura 68349 0.76 268 Mustafabad 59949 0.99 89 Paharganj 66179 1.01 269 Khajoori khas 50291 1.14 90 Model basti 60648 1.39 270 Tukhmir pur 53843 0.45 The World Bank Group Page | 209 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report 91 Karol bhag 72524 2.15 271 Karawal nagar west 71846 2.19 92 Dev nagar 65394 0.70 272 Sonia vihar 34641 4.25 93 Baljit nagar 59479 0.38 273 C.P 10017 1.97 Barakamba modern 94 West patel nagar 66184 1.32 274 10017 1.31 school 95 East patel nagar 65751 2.34 275 Birla mandir 40069 2.64 96 New ranjit nagar 64648 0.81 276 Chanakya puri 5565 3.28 97 Kirti nagar 65291 2.48 277 Rashtrapati bawan 6678 3.06 98 Man sarover garden 62223 1.53 278 Supreme court 16696 2.10 99 Moti nagar 64630 1.74 279 India gate 12021 2.90 100 Karampur 58979 2.10 280 Prithiviraj road 8348 2.25 101 Raja garden 67029 2.19 281 Jor bagh 33391 4.08 102 Raghubir nagar 62909 0.55 282 Netaji nagar 33391 3.02 103 Punjabi bagh 56119 3.05 283 Sarojini nagar 73461 3.17 Kidwai nagar east/ 104 Madipur 53730 0.58 284 35305 1.51 AIIMS 105 Rajouri garden 60963 2.17 285 Kirbi place 21467 3.44 106 Tagore garden 56436 0.75 286 Sadar bazaar 15944 5.99 107 Vishnu garden 61790 0.83 287 Dhaula kuan enclave 10734 4.93 Baird place, khyber 108 Khyala 71808 1.25 288 16100 7.17 lines 109 Janakpuri north 65345 2.62 289 APS colony 16100 4.03 110 Nangal raya 58675 2.46 290 Nangal dairy, 20887 13.11 111 Hari nagar 61450 2.29 291 Du south campus 10734 2.26 112 Subhash nagar 55226 1.33 292 Narela (mamoor pur) 33205 7.58 113 Mahavir nagar 46775 1.29 293 Narela (hamidpur) 33205 0.62 114 Tilak nagar 56648 1.47 294 Bankner (sanoth) 43928 3.91 Major bhaupinder singh 115 53256 0.85 295 Alipur (iradat nagar) 11939 2.74 nagar 116 Vikaspuri east 52065 2.13 296 Alipur (holambi kalan) 49566 8.71 117 Janak puri west 63226 1.90 297 Alipur (bankoli) 10331 2.32 118 Janak puri south 51502 2.51 298 Alipur (khera khurd) 13598 3.98 119 Milap nagar 67111 1.34 299 Alipur (bijapur) 9602 5.33 120 Sitapuri 64354 0.69 300 Alipur (khera kalan) 15206 14.37 121 Kunwar singh nagar 61528 3.48 301 Bhalswa jahangir pur 24955 0.82 122 Hastsal 137010 10.21 302 Dhir pur ( azad pur) 45349 2.20 123 Vikas puri 66008 1.76 303 Rohini sec 11 21255 2.19 124 Vikas nagar 77190 2.21 304 Puth kalan 5366 3.74 125 Mohan garden 95000 1.80 305 Puth kalan 21671 3.70 126 Nawada 48152 0.78 306 Sahibabad daulat pur 30000 4.98 127 Uttam nagar 53122 0.88 307 Sahibabad daulat pur 45673 2.42 128 Bindapur 83158 1.71 308 Sahibabad daulat pur 20690 3.97 129 Dabri 62847 1.38 309 Begumpur (barwala) 40000 4.25 130 Manglapuri 54083 2.44 310 Begumpur (sultanpuri) 38000 6.25 131 Sagarpur 65263 0.68 311 Karala 65000 2.64 132 Sagarpur west 59176 0.64 312 Mundaka 20000 7.41 133 Chhawla 106670 74.59 313 Mundaka (tikri kalan) 45000 23.18 Nangli sakravati (qutab 134 62424 22.02 314 Nilothi 39242 1.66 vihar) The World Bank Group Page | 210 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report 135 Kakraula 59947 5.44 315 Qummruddin nagar 76660 2.79 136 Matiala 35128 3.34 316 316 saraswati vihar 24390 1.91 137 Roshanpura 78089 7.92 317 Saraswati vihar 1538 1.18 138 Najafgarh 66641 1.89 318 Kunwar singh nagar 66501 2.83 139 Dichaon kalan 85379 30.80 319 Nangli sakravati 62424 2.24 140 Khera 107521 72.20 320 Kakraula (dwaraka ) 25742 3.96 141 Bijwasan 14909 6.50 321 Matiala 44930 4.12 142 Raj nagar 68380 1.16 322 Matiala 23395 2.07 143 Kapashera 23823 8.34 323 Matiala 20535 2.71 144 Mahipalpur 72776 6.67 324 Bijwasan 15087 2.16 145 Palam 62052 2.61 325 Bijwasan 15027 7.36 146 Sadh nagar 60011 0.76 326 Bijwasan 5028 5.27 Kapashera (nangal 147 Mahavir enclave 80775 2.33 327 7942 6.46 dewat) 148 Madhu vihar 56698 1.28 328 Kapashera (kapas hera) 15881 5.19 149 Rajinder nagar 74646 4.06 329 Pusa (south patel nagar) 26731 4.45 150 Pusa 26731 5.04 330 Bhogal 27415 0.91 151 Inderpuri 55025 0.93 331 Malviya nagar (iit) 19428 3.17 152 Naraina 65265 1.57 332 Hauz khas 26695 1.43 153 Daryaganj 29522 2.83 333 Lado sarai (press encl.) 20291 2.01 154 Nizamuddin 29506 4.20 334 Nizamuddin 29506 2.95 155 Lajpat nagar 52495 2.67 335 Mehrauli 28011 2.57 156 Bhogal 34534 1.38 336 Vasant kunj (sec.c) 21082 1.20 157 Kasturba nagar 53407 0.90 337 Tughlakabad 21706 0.67 158 Kotla mubarak pur 57804 1.15 338 Tughlakabad(lal kuan) 28838 2.21 159 Adnrewsganj 59878 1.92 339 Okhla (okhla ind.estate) 35129 1.90 Madanpur khadar (ali 160 Amar colony 50554 1.23 340 36953 2.47 vihar) Sarita vihar (kalidi 161 Malviya nagar 24836 1.78 341 15216 2.55 kunj) 162 Village hauz rani 44076 1.05 342 Khichripur (kondli) 34818 2.24 163 Safdarjang enclave 57817 3.28 343 Anand vihar 21846 1.70 164 Hauz khas 26695 1.38 344 Anand vihar 19115 1.71 165 Vasant vihar 52257 9.33 345 Vilshwash nagar 29444 0.56 I.P. Extension 166 Munirka 54256 1.13 346 32696 2.39 (ghazipur) 167 R.K. Puram 54683 2.17 347 Preet vihar 30841 1.15 Dilshad garden (j & k 168 Nanak pura 59737 3.20 348 28790 1.49 block) 169 Lado sarai 32632 2.70 349 New usmanpur 29330 0.00 170 Mehrauli 28011 1.91 350 Sonia vihar (shubhpur) 34641 2.68 India gate (CGO 171 Vasant kunj 34331 5.65 351 28047 2.60 complex) 172 Kishangarh 81833 21.78 352 Daryaganj (Rajghat) 28377 2.17 Chanakya puri 173 Sai-ul-ajaib 65774 9.76 353 4455 1.89 (diplomatic enclave) Chanakya puri (malcha 174 Chhatarpur 80000 13.39 354 1113 4.08 mahal) Prithiviraj road 175 Aya nagar 82000 31.15 355 8348 1.88 (Gyaramurti) The World Bank Group Page | 211 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report 176 Bhati 85172 45.03 356 Chandi chowk (red fort) 5967 1.51 Kapashera (dwarka 177 Sangam vihar 72370 6.33 357 32142 1.75 sector21) Anarkali (krishna 178 Deoli 73668 2.35 358 33778 0.97 nagar) 179 Tigri 69366 1.90 359 Akshardham 11046 13.20 180 Dakshin puri ext. 63818 1.17 360 IT park shastri park 19621 15.12 Sub-Total 944.72 Sub-Total 479.07 Grand Total 1423.79 The World Bank Group Page | 212 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Appendix 11 1st Workshop on MEGACITY LOGISTICS: METRICS, TOOLS AND MEASURES FOR SUSTAINABILITY (MEGALOG) Research Project Funded by May 9th, 2017 Venue: Council Hall, CSIR-CRRI, New Delhi – 110025 List of Participants S. Name Designation Organisation No. 1 Prof. Satish Chandra Director CSIR-CRRI, New Delhi 2 Dr. Errampalli Madhu Principal Scientist and HOD (TP) CSIR-CRRI, New Delhi 3 Prof. Lorant A. Tavasszy Professor TU Delft, The Netherlands 4 Mr. Jeroen Borst Manager TNO, The Netherlands 5 Prof. P. K Sikdar Vice President ICT Pvt. Ltd., New Delhi 6 Dr. Neelam J. Gupta Principal Scientist and Head (ILT) CSIR-CRRI, New Delhi 7 Mr. D. P. Gupta Retd. Director General MoRTH, New Delhi 8 Dr.CH. Ravi Sekhar Principal Scientist CSIR-CRRI, New Delhi 9 Mr. Subhash Chand Principal Scientist CSIR-CRRI, New Delhi 10 Dr. S. Padma Senior Scientist CSIR-CRRI, New Delhi 11 Dr. Sippy Kalra Senior Scientist CSIR-CRRI, New Delhi 12 Dr. Neelima Chakrabarty Principal Scientist and HOD (TES) CSIR-CRRI, New Delhi 13 Ms. Farhat Azad Scientist CSIR-CRRI, New Delhi 14 Dr. Pratikana Das Scientist CSIR-CRRI, New Delhi 15 Ms. Kamini Gupta STO CSIR-CRRI, New Delhi Shakti Sustainable Energy 16 Mr. Ravi Gadepalli Transport Foundation, New Delhi 17 Dr. B. Kanagadurai Chief Scientist CSIR-CRRI, New Delhi 18 Mr. D. Ravinder Senior Technical Officer CSIR-CRRI, New Delhi 19 Dr. Anuradha Shukla Chief Scientist CSIR-CRRI, New Delhi 20 Dr. Kayitha Ravinder Principal Scientist CSIR-CRRI, New Delhi The World Bank Group Page | 213 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report 21 Mr. Pradeep Kumar Senior Principal Scientist CSIR-CRRI, New Delhi 22 Dr. Ravinder Kumar Principal Scientist CSIR-CRRI, New Delhi 23 Dr. Purnima Parida Senior Principal Scientist CSIR-CRRI, New Delhi 24 Dr. J. Nataraju Principal Scientist CSIR-CRRI, New Delhi 25 Dr. A. Mohan Rao Principal Scientist CSIR-CRRI, New Delhi 26 Mr. Ashutosh Arun Scientist CSIR-CRRI, New Delhi 27 Mr. S. B. S. Tyagi Superintending of Police (SP) Delhi Police South Delhi Municipal 28 Mr. S. L. Meena Executive Engineer (EE) Corporation (SDMC) Associate Executive Engineer South Delhi Municipal 29 Mr. Mohd. Galib (AEE) Corporation (SDMC) Associate Executive Engineer South Delhi Municipal 30 Mr. S. A. Khan (AEE) Corporation (SDMC) School of Planning and 31 Prof. Sanjay Gupta Professor Architecture (SPA), New Delhi North Delhi Municipal 32 Mr. Tejvir Singh Executive Engineer (EE) Corporation (NDMC) 33 Dr. S. Velmurugan Senior Principal Scientist CSIR-CRRI, New Delhi 34 Dr. Mukti Advani Senior Scientist CSIR-CRRI, New Delhi The World Bank Group Page | 214 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report MEGACITY LOGISTICS: METRICS, TOOLS AND MEASURES FOR SUSTAINABILITY (MEGALOG) Research Project Funded by December 12th, 2017 Venue: Council Hall, CSIR-CRRI, New Delhi – 110025 Short Course on "Sustainable City Logistics for Policy Making and Freight Operations" List of Participants S. Name Designation Organisation No. 1 Prof. Satish Chandra Director CSIR-CRRI, New Delhi School of Planning and 2 Prof .Sanjay Gupta Professor Architecture (SPA), New Delhi 3 Prof. K. Ramachandra Rao Professor IIT Delhi, New Delhi School of Planning and 4 Prof. S. Bhaskar Gowd Assistant Professor Architecture (SPA), New Delhi 5 Mr. Anand Pal Singh Regional Executive Admin Associated Road Carriers Ltd. School of Planning and 6 Mr. Pankaj Kant PhD Student Architecture (SPA), New Delhi 7 Dr. Rina Singh Senior Scientist CSIR-CRRI, New Delhi 8 Dr. Kirti Bhandari Principal Scientist CSIR-CRRI, New Delhi 9 Dr. Nasim Akhtar Principal Scientist CSIR-CRRI, New Delhi 10 Ms. Farhat Azad Senior Scientist CSIR-CRRI, New Delhi 11 Dr. Pritikana Das Scientist CSIR-CRRI, New Delhi 12 Ms. Kamini Gupta Senior Technical Officer CSIR-CRRI, New Delhi 13 Mr. Ramesh Chand Majhi Scientist CSIR-CRRI, New Delhi 14 Mr. Satish Kumar Senior Technical Officer CSIR-CRRI, New Delhi 15 Dr. H. Lokeshwor Singh Senior Technical Officer CSIR-CRRI, New Delhi 16 Mr. Sanjay Kumar Senior Technician CSIR-CRRI, New Delhi 17 Dr. Ravindra Kumar Principal Scientist CSIR-CRRI, New Delhi The World Bank Group Page | 215 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Delhi Metro Rail Corporation 18 Dr. D. Mukhopadhyay Advisor (DMRC), New Delhi 19 Prof. Lorant A. Tavasszy Professor TU Delft, The Netherlands 20 Dr. Hans J. Quak Transport Planner (Logistics) TNO, The Netherlands 21 Dr. Nilesh Anand Former PhD Student TU Delft, The Netherlands 22 Ms. Leeza Malik PhD Student IIT Delhi, New Delhi 23 Ms. Nilanjana De Bakshi PhD Student IIT Delhi, New Delhi Delhi Metro Rail Corporation 24 Mr. Prashant Mishra Engineer (DMRC), New Delhi 25 Dr. CH. Ravi Sekhar Principal Scientist CSIR-CRRI, New Delhi 26 Dr. Errampalli Madhu Principal Scientist and HOD (TP) CSIR-CRRI, New Delhi 27 Mr. Vikash Kumar Thakur Project Assistant(TP) CSIR-CRRI, New Delhi 28 Mr. Amit Kumar Dubey Project Assistant (TP) CSIR-CRRI, New Delhi 29 Mr. Subramaniya Kannan Senior Technical Officer CSIR-CRRI, New Delhi 30 Dr. B. Kanagadurai Chief Scientist CSIR-CRRI, New Delhi 31 Dr. Mukti Advani Senior Scientist CSIR-CRRI, New Delhi 32 Dr. Neelima Chakrabarty Principal Scientist and HOD (TES) CSIR-CRRI, New Delhi 33 Dr. S. Padma Senior Scientist CSIR-CRRI, New Delhi 34 Dr. Kayitha Ravinder Principal Scientist CSIR-CRRI, New Delhi 35 Dr. J. Nataraju Principal Scientist CSIR-CRRI, New Delhi 36 Dr. A. Mohan Rao Principal Scientist CSIR-CRRI, New Delhi 37 Dr. S. Velmurugan Senior Principal Scientist CSIR-CRRI, New Delhi The World Bank Group Page | 216 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report National Dissemination Workshop on MEGACITY LOGISTICS: METRICS, TOOLS AND MEASURES FOR SUSTAINABILITY (MEGALOG) Research Project Funded by December 13th, 2017 Venue: C. V. Raman Hall, CSIR-CRRI, New Delhi – 110025 List of Participants S. Name Designation Organisation No. 1 Prof. Satish Chandra Director CSIR-CRRI, New Delhi The University of Melbourne, 2 Prof. Russel G. Thompson Professor Australia Senior Principal Scientist and 3 Dr. Neelima Chakrabarty CSIR-CRRI, New Delhi HOD (TES) 4 Ms. Farhat Azad Senior Scientist CSIR-CRRI, New Delhi 5 Dr. Pritikana Das Scientist CSIR-CRRI, New Delhi 6 Dr. CH. Ravi Sekhar Principal Scientist CSIR-CRRI, New Delhi 7 Dr. Sippy Kalra Senior Scientist CSIR-CRRI, New Delhi 8 Mr. Rajan Verma Technical Officer CSIR-CRRI, New Delhi 9 Mr. S. K. Biswas Senior Technician CSIR-CRRI, New Delhi 10 Mr. Jagdish S. Jangpangi Senior Technician CSIR-CRRI, New Delhi 11 Ms. Kamini Gupta Senior Technical Officer CSIR-CRRI, New Delhi 12 Dr. Mukti Advani Senior Scientist CSIR-CRRI, New Delhi 13 Dr. Errampalli Madhu Principal Scientist HOD (TP) CSIR-CRRI, New Delhi 14 Dr. Ravinder Kumar Principal Scientist CSIR-CRRI, New Delhi 15 Mr. S. Kannan Senior Technical Officer CSIR-CRRI, New Delhi 16 Dr. Nasim Akhtar Principal Scientist CSIR-CRRI, New Delhi 17 Dr. J. Nataraju Principal Scientist CSIR-CRRI, New Delhi 18 Mr. K. Sitaramanjaneyulu Senior Principal Scientist CSIR-CRRI, New Delhi 19 Mr. Pradeep Kumar Principal Scientist CSIR-CRRI, New Delhi 20 Mr. Manoj Kumar Shukla Principal Scientist and HOD (FP) CSIR-CRRI, New Delhi The World Bank Group Page | 217 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Senior Principal Scientist and 21 Mr. S. S. Geharwar CSIR-CRRI, New Delhi HOD (BES) 22 Mr. A. K. Jain Senior Scientist and HOD (Civil) CSIR-CRRI, New Delhi 23 Prof. Lorant A. Tavasszy Professor TU Delft, The Netherlands 24 Dr. Nilesh Anand Former PhD Student TU Delft, The Netherlands 25 Prof. Sanjay Gupta Professor SPA New Delhi 26 Dr. Hans J. Quak Transport Planner (Logistics) TNO, The Netherlands 27 Mr. Jeroen Borst Transport Modeller TNO, The Netherlands 28 Dr. Devesh Tiwari Senior Principal Scientist CSIR-CRRI, New Delhi 29 Dr. Kirti Bhandari Principal Scientist CSIR-CRRI, New Delhi 30 Mr. M. N. Nagabhushana Senior Principal Scientist CSIR-CRRI, New Delhi 31 Mr. R. S. Bharadwaj Chief Scientist CSIR-CRRI, New Delhi 32 Dr. Rina Singh Senior Scientist CSIR-CRRI, New Delhi 33 Dr. Siksha Swaroopa Kar Senior Scientist CSIR-CRRI, New Delhi 34 Dr. Ambika Behl Senior Scientist CSIR-CRRI, New Delhi 35 Mr. Dinesh Ganvir Senior Scientist CSIR-CRRI, New Delhi 36 Mr. Abhishek Mittal Senior Scientist CSIR-CRRI, New Delhi 37 Dr. Sangita Senior Principal Scientist CSIR-CRRI, New Delhi 38 Ms. Parvathi G. S. Senior Scientist CSIR-CRRI, New Delhi 39 Mr. Sudesh Kumar Principal Scientist CSIR-CRRI, New Delhi 40 Mr. Vikash Kumar Thakur Project Assistant (TP) CSIR-CRRI, New Delhi 41 Mr. G. Durgaprasad Scientist CSIR-CRRI, New Delhi 42 Mr. Sampath Kumar Scientist CSIR-CRRI, New Delhi 43 Ms. Risha Chatterjee Journalist Times of India 44 Mr. Subhash Chand Principal Scientist CSIR-CRRI, New Delhi 45 Mr. Ramesh Chand Manjhi Scientist CSIR-CRRI, New Delhi Town and Country Planning 46 Mr. Udit Ratna Town and Country Planner Organisation (TCPO), New Delhi 47 Mr. Prashant Mishra Engineer Delhi Metro Rail Corporation (DMRC), New Delhi 48 Dr. R. N. Dutta Senior Principal Scientist CSIR-CRRI, New Delhi 49 Mr. K. Sasidhar Chief General Manager ICT Pvt. Ltd., New Delhi 50 Dr. A. K. Sinha Principal Scientist CSIR-CRRI, New Delhi The World Bank Group Page | 218 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report 51 Mr. Ravi Shankar Scientist CSIR-CRRI, New Delhi 52 Mr. Romeil Sagwal Scientist CSIR-CRRI, New Delhi 53 Mr. Amit Kumar Dubey Project Assistant (TP) CSIR-CRRI, New Delhi The World Bank Group Page | 219 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report Appendix 12 The World Bank Group Page | 220 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report The World Bank Group Page | 221 CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report The World Bank Group CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands Megacity Logistics: Metrics, Tools and Measures for Sustainability (MEGALOG) Final Report The World Bank Group CSIR-Central Road Research Institute (CRRI), New Delhi, India, TNO Netherlands and TU Delft Netherlands CSIR-Central Road Research Institute (An ISO 9001:2008 Institution) Delhi-Mathura Road New Delhi - 110025, India http://www.crridom.gov.in/ TNO, The Netherlands Delft University of Technology, The Netherlands