The President’s Office Regional Administration and Local Government (PO-RALG) DAR ES SALAAM METROPOLITAN DEVELOPMENT PROJECT A APPENDIX A Methodologies Integrated Transport and Land Use Planning Strategy  Dar es Salaam Bus Rapid Transit (BRT) Phase 1 Corridor November 2018 Sponsored by Project team i Appendix A / Part A1 - Survey Methodologies Appendix A / Part A1 ii Contents A1 Survey Methodologies  4 A1.1 Income Survey Methodology  6 A1.2 Land Use Verification Survey Methodology  42 A1.3 Transport Survey Methodology  70 A2 Baseline Methodologies  164 A2.1 General Information  165 A2.2 Broadway Malyan (Urban Planning)  166 A2.3 Mobility in Chain (Transport)  173 A2.4 CoLab (Socio-Economics & Real Estate)  177 A2.5 Aurecon (Natural Environment, Social Facilities and Infrastructure)  181 A3 TOD Matrix Methodology  188 A3.1 TOD Matrix  189 A3.2 Indicators  191 3 Appendix A / Part A1 - Survey Methodologies Appendix A / Part A1 4 Appendix A Part A1 Survey Methodologies 5 Appendix A / Part A1 - Survey Methodologies Section A1.1 Appendix A / Part A1 6 Income Survey Methodology & Results The following pages are an extract from the approved methodology for the Income Survey which was carried out in Dar es Salaam during September 2017. 7 Appendix A / Part A1 - Survey Methodologies & Results A1.1 Income Survey Field Data Collection Methodology DSM Phase 1 BRT Corridor Household Survey Submitted by: Submitted to: Aurecon South Africa (Pty) Ltd Broadway Malyan Aurecon Centre Riverside House Century City Southwark Bridge Road South Africa London SE1 9HA Contact Person: Contact Person: Harry van der Berg James Rayner T: +27 84 645 8947 T: +44 (0) 207 261 4200 Appendix A / Part A1 8 A1.1 Contents Survey Content 1. Scope of the Income Survey 2. Questionnaire Fieldwork Methodology 3. Introduction 4. Approach 5. Income Survey Logistics and Technicalities 9 Appendix A / Part A1 - Survey Methodologies & Results A1.1 1 SURVEY CONTENT 1. Scope of the Income Survey To gather socio-economic data along the DSM BRT phase 1 study area that will: • Complement and verify existing data from existing sources; the 2012 census, the 2016 household survey, and government salary surveys. • Help fill gaps, at ward level from the 2012 census. • Provide data that will support the TOD assessment matrix • Provide a standard methodology that can be re-applied in future surveys of planned BRT lines so that like-for-like comparisons can be made. • Provide a standard methodology for future surveys to support a longitudinal study evaluating the impact the development of all the BRT lines in the long-term. • Provide contextual information to allow for comparison between surveyed BRT users and the wider population. The data collated will examine: • Travel to work: Mode and distance • Employment: Nature, location. • Income: Statement of income, poverty indicators • Expenditure: Statement of expenditure indicators The Income Survey will be restricted to gathering data from households within areas of identified residential land use, within 1km of the current BRT Line 1, in Dar es Salaam. This boundary, along with the survey points, is illustrated below: 2. Outline of Planned Analysis Appendix A / Part A1 10 A1.1 2 The consultants team will cross check the survey results with other relevant data sets: • 2012 Census • 2016 Household survey • Latest incomes data from National Bureau of Statistics • The project’s passenger survey The consultant team will use the data to define the characteristics of the BRT station catchments in terms of: • Household size • Housing • Employment • Travel to work • Earnings • Education • Likely consumption The consultant team will assess the ways in which the station corridor population differs from the wider population in terms of prosperity and travel patterns. We will assess the extent to which the BRT passengers are drawn from local communities. We will establish a basis for establishing how these patterns will change over time. The approach will be mindful that the survey will be repeated in the other BRT corridors. 2. Questionnaire What follows across the subsequent two pages is the proposed questionnaire, as well as the question clarifications to be used as the basis for data collection. It is our suggestion that it be accompanied by an introductory preamble, as follows: “We are undertaking a survey on behalf of the President’s Office for Regional Administration and Local Government. You have been asked to provide information on your household, your work, and how you travel to work so that the government of Tanzania can plan better transport services in Dar es Salaam. This information will be used to help plan how the BRT works, and how and where the new lines are developed. All the information we gather will be kept confidential. We thank you for your help in this important study.” 11 Appendix A / Part A1 - Survey Methodologies & Results A1.1 3 2.1 Questionnaire Capture sheet (embedded in application) Socio Economic Survey 1.0 Individual 1.1 Age 1.2 Sex 1.3 Household size 1.4 Main wage earner? 2.0 Home 2.1 Location Ward Sub‐Ward Location code Generated by sampling method 2.2 Tenure Own Rent  Other With documentation? If other … Monthly rent Visual inspection question to be completed by surveyor How satisfied are you with the  2.3 Housing  quality of your home Building Materials Quite dissatisfied Very dissatisfied Quite satisfied Very satisfied RF_SHEETME RF_CONCRET RF_ASBESTO Commercial  Bedrooms RF_TILES space? Rooms 3.0 Transport 3.1 Transport Do you use the BRT? If never/occasionally, why? Work near to where  Too much traffic on  No parking facilities Doesn't go where I  barriers to access Too far away to  Overcrowded /  Too expensive Too dificult /  Too far away Prefer other  Occasionally Unpleasant local roads Regularly transport Never Daily need walk I live 4.0 Work (main wage earner) 4.1 Age if not respondent Gender 4.2 Location Place name Ward Sub‐Ward Mode(s) 4.3 Journey to work Journey  Tuk‐tuk Motor  Bicycle Train  Daily  Dala  Walk Dala time Bike cost Taxi Bus Car What are the main challenges on your journey to work? What would be your preferred transport improvements? Easier walking and  More/better roads Distance between  transport network Poor connectivity Long travel time  A more efficient  home and work (Congestion) cycling Other Other Appendix A / Part A1 12 A1.1 3 2.1 Questionnaire Capture sheet (embedded in application) Socio Economic Survey 1.0 Individual 1.1 Age 1.2 Sex 1.3 Household size 1.4 Main wage earner? 2.0 Home 2.1 Location Ward Sub‐Ward Location code Generated by sampling method 2.2 Tenure Own Rent  Other With documentation? If other … Monthly rent Visual inspection question to be completed by surveyor How satisfied are you with the  2.3 Housing  quality of your home Building Materials Quite dissatisfied Very dissatisfied Quite satisfied Very satisfied RF_SHEETME RF_CONCRET RF_ASBESTO Commercial  Bedrooms RF_TILES space? Rooms 3.0 Transport 3.1 Transport Do you use the BRT? If never/occasionally, why? Work near to where  Too much traffic on  No parking facilities Doesn't go where I  barriers to access Too far away to  Overcrowded /  Too expensive Too dificult /  Too far away Prefer other  Occasionally Unpleasant local roads Regularly transport Never Daily need walk I live 4.0 Work (main wage earner) 4.1 Age if not respondent Gender 4.2 Location Place name Ward Sub‐Ward Mode(s) 4.3 Journey to work Journey  Tuk‐tuk Motor  Bicycle Train  Daily  Dala  Walk Dala time Bike cost Taxi Bus Car What are the main challenges on your journey to work? What would be your preferred transport improvements? Easier walking and  More/better roads Distance between  transport network Poor connectivity Long travel time  A more efficient  home and work (Congestion) cycling Other Other 13 5 6.5 6.4 6.3 A1.1 Electricity Solid waste Stormwater 0 ‐ 15,000 Yes ‐ Grid Municipality On your property Sewer / waterborne  municipal connection 15,000 ‐ 30,000 Yes ‐ Generator Private company On your neighbor's  Septic tank / conservancy  property tank 30,000 ‐ 45,000 No Not collected On your closest road Pit latrine / VIP house connection? Who collects your waste? Do you have an electrical  45,000 ‐ 60,000 Don't know Within your  Bucket system neighborhood Appendix A / Part A1 - Survey Methodologies & Results Do you ever experience flooding? 60,000 ‐ 75,000 Never River More than 75,000 0 ‐ 15,000 None of energy per month (excluding electricity)? 15,000 ‐ 30,000 30,000 ‐ 45,000 Never How much does your household spend on alternative forms  electricity per month? 45,000 ‐ 60,000 Once a month 60,000 ‐ 75,000 Closed bin Once a week More than 75,000 Open area Every day your waste? How do you store  Other More than one a day Never Sewer doesn't work because  it is continuously blocked 1‐2 times a month Landfill interruptions? 1‐2 times a week Open dumping waste disposed? How much, in shillings, does your household spend on Do you suffer from power  1‐2 times a day Burned Where/how is your  Recycled /  composted Don't know Appendix A / Part A1 14 A1.1 6 2.2 Question Clarification 1. Individual - these questions relate to the respondent 1.1 Age - what is the age of the respondent 1.2 Sex - is the respondent male or female 1.3 Household Size - how many people live in the structure with the respondent 1.4 Main wage earner - is the respondent also the main wage earner 2. Home - these questions relate to the structure and not the respondent 2.1 Location - the answer will be populated automatically when capturing answers 2.2 Tenure - is the structure owned, rented or occupied under a different agreement Rent - if the structure is rented, what is the monthly payment amount 2.3 Housing - these questions relate to the structure and the respondent Bedrooms - how many bedrooms are there in the structure Housing Quality - respondents satisfaction with dwelling quality RF_SHEETME - is the roof made of sheet metal RF_TILES - is the roof made of tiles RF_CONCRET - is the roof made of concrete RF_ASBESTO - is the roof made of asbestos 3. Transport - These questions relate to the respondents attitude towards transport Use of BRT - Does the respondent use the BRT system - if not, why does he/she not. 4. Work - these questions relate to the main wage earner 4.1 Age - what is the age of the main wage earner if it is not the respondent Gender - is the main wage earner male or female if it is not the respondent 4.2 Location - what is the place of employment called and in which ward and sub- ward is it located 4.3 Journey to work - what mode of transport does the main wage earner use, how long does it take and what does it cost to get to her/his place of employment Main Challenges - what are the primary challenges that the main wage earner faces on his/ her way to work Improvements - what are the primary transport improvements that will assist the main wage earner with his/her journey to work 4.4 Occupation - what type of work does the main wage earner do 4.5 Employment - what is the main wage earner’s present and past employment status 4.6 Earnings - what does the main wage earner earn per month 5. Prosperity - this question relates level of education and consumption patterns 5.1 Education Level - what level of education does the main wage earner have, if none, is the wage earner literate 5.2 Bank Account - does any member of the household have a bank account Smart Phone - does any member of the household have a smart-phone Air Conditioning - does the household have an air conditioner within the home Motor Cycle - does any member of the household have a motor cycle Landline - does the household have a landline telephone within the home Mobile_PH - does any member of the household have a mobile telephone COMP_LPTO - does any member of the household have a laptop or computer INTERNET - does the household have access to the internet within the home MOTOR_VEH - does any member of the household own a motor vehicle (not motor cycle) 15 Appendix A / Part A1 - Survey Methodologies & Results A1.1 7 6. Bulk Services - These questions relate to the bulk services available at the dwelling 6.1 Water - How does the respondent get water for daily use Piped Water - How often is piped water available to the property Treating Water - Is it necessary for the respondent to treat the water before use 6.2 Sanitation - What form of sanitation services does the respondent make use of Blockage - Are there any blockages in the sanitation system used and how frequent 6.3 Stormwater - Does the respondent ever experience flooding in and around dwelling 6.4 Solid Waste Waste Collection - Who collects the respondents solid waste Waste separation - Does the respondent seperate or sort different waste elements Waste Storage - Where does the respondent store accumulated waste Waste Disposal - Where does your waste go when your or someone else removes it 6.5 Electricity Power connection - Does the respondent have an electricity connection to the dwelling Power Cost - How much does the household spend on electricity per month Power Cuts - How often are their power cuts to the dwelling if ever Alt Power - How much does the household spend on alternative forms of energy Appendix A / Part A1 16 A1.1 8 FIELDWORK METHODOLOGY 3. Introduction The purpose of this section is to outline the field data collection approach that will be applied in carrying out the income survey component of DSM Phase 1 BRT Corridor Optimisation Strategy. 4. Approach The above mentioned survey data will be collected via a smart device (Samsung T116 - 7”) using the application “Collector” for ArcGIS from the ESRI software suite which enables collecting of and editing of data in the field. The devices will also be purchased in country to ensure that import restrictions do not impact on the survey timeline. Some features of the application include: • Collect and update information in the field. • Attach photos to captured features • Take maps and data offline and sync changes when connected. • Improve data quality with easy-to-use map-driven forms. • Track work and report your activities. • Track work and report activities. With this context in mind, the following section will outline the approach to be followed with regards to the income survey. 5. Income Survey Logistics and Technicalities The spatial extent of the income survey falls within the phase 1 study area where 2000 random points have been predefined (see figure 1) and dispersed across the study area to assist a sample coverage. The points were generated randomly using a Python Module which selected building footprints which intersected with the residential land use layer. Importantly, these points are guidelines and where a survey is not possible, for whatever reason, the next structure will be approached to conduct the survey. This alternative location will then be captured and hence will replace the predefined point and building footprint. The survey team will also record the reason why a certain predefined point could not be captured and relay this information to the client. 17 Appendix A / Part A1 - Survey Methodologies & Results A1.1 9 ( ! ! ( ! ( ( ! ( ! ( ! ! ( ! ( ( ! ( ! ! ( ( ! ( ! ( ! ! ( ( ! ( ! ! ( Figure 1: Randomly dispersed points matched ! ( to building ( ! footprint and residential land use ! ( ( ! ! ( ( ! Table 1: Survey duration ! ( ( ! Single Time Surveys Field Survey survey between Total Survey Survey Type per day per workers/ Count duration surveys Days device devices (min) (min) Household 2000 25 20 10 24 8 In terms of timelines, table 1 above illustrates the logic applied in calculating the total amount of days that it would take to collect 2000 surveys. Accordingly, the survey team will consist out of 3 Aurecon staff members (including on-site tech support) with the rest of the survey team consisting out of local students who have previously assisted Aurecon on surveys. Importantly, the 2000 survey locations have been divided into 24 optimal clusters (+- 84 Households each) to ensure efficiency. This was done via a Python Module through applying the space-filling curve mathematical algorithm to generate the clusters. Accordingly, each surveyor carrying a device will be assigned a cluster with only his or her allocated survey points displaying on the device to ensure duplication does not occur. These clusters are illustrated in figure 2 below. Furthermore, the optimal path between the survey points, within each of the 24 clusters, have also been calculated to ensure that the surveyors follow the most efficient path/sequence between all the survey points (see figure 3). Importantly, one of the total survey days will be used to train local surveyors on the use of the “Collector” application which will be combined with a dry run survey to iron out any potential issues or questions that the survey team might have. It is estimated that training will occur on a Tuesday with the survey set to start on a Wednesday and run through to the next week Friday (see Table 2 below). The GIS technologist will remotely assist the Tanzania Aurecon office in setting up the devices so that they are ready and working by the time the survey team arrives in Dar es Salaam. Appendix A / Part A1 18 A1.1 10 Figure 2: Clustered Survey Points Figure 3: Optimised Path between Survey Points 19 Appendix A / Part A1 - Survey Methodologies & Results A1.1 Income Survey Results This section summarises the main findings from an income survey conducted in the BRT Corridor in September 2017. Income Survey: Overview Approach In September 2017, surveyors interviewed 2,028 members filling in gaps: supplement the 2016 household survey; and of the public, at their place of residence to gather data provide information on the characteristics of the population relating to their income, economic and social status, residing in the Line One of the BRT area. housing conditions and travel to work patterns. The full A separate report has examined the data specifically in questionnaire can be found in the appendix at the end of relation to housing affordability. this report. The houses surveyed were selected in clusters of Population Age randomised points proportionate to the population in that residential area. In this way the survey was balanced across The chart below illustrates the age breakdown of the BRT corridor. respondents to the survey. The majority of those surveyed are of working age. The purpose of the survey was to; supplement data from the 2012 census, providing more up-to-date information and Figure A1.1-1  Total Income Survey, Age Distribution Appendix A / Part A1 20 A1.1 Gender The gender of those surveyed is shown in the pie chart below. Slightly more than half of the population were female. Figure A1.1-2  Total Income Survey, Gender Breakdown Household Size Household size varied considerably across the survey group. There were 11 houses of 20 or more occupants. The most commonly occurring household size is five. There was a total of 11,176 people recorded as living in the 2,028 households, an average of 5.5 people per home. The chart below illustrates the distribution. Figure A1.1-3  Total Income Survey, Household Size by Occurrence 21 Appendix A / Part A1 - Survey Methodologies & Results A1.1 The chart below shows the distribution of inhabitants by household size. When adjusting the distribution of household sizes for the number of inhabitants (as opposed to the number of households) people most, nearly 18%, are likely to live in a household of six. Figure A1.1-4  Total Income Survey, Household Size by Proportion of Population Tenure Those surveyed were asked on what basis they occupied their home. Most households, 54%, reported that they owned their home. 87% of home owners said that they had documentation of their ownership. The pie chart below shows the overall breakdown of tenure. Figure A1.1-5  Total Income Survey, Breakdown of Tenure Appendix A / Part A1 22 Rental Payments A1.1 The survey asked those who rented how much they paid each month. Most tenants (90%) pay more than 30,000 shillings per month in rent (US$132). The chart below shows the distribution or rental levels. Just over half those surveyed pay less than 50,000 shillings per month (US$22). Figure A1.1-6  Total Income Survey, Monthly Rental Payments Income We asked which income band the main wage earner fell into. Only 9% were unwilling or unable to provide and answer. The chart below illustrates the distribution of earnings. Just over half the population surveyed earn less than 300,000 shillings per month (US$132). Figure A1.1-7  Total Income Survey, Total Income Survey, Monthly Earnings 23 Appendix A / Part A1 - Survey Methodologies & Results A1.1 Income Survey: Findings by Station Area Catchment Area Analysis Station Respondents Catchment area analysis. The full set of survey data was Baruti 33 analysed to reveal the characteristics of each station Bucha 17 area, within a ten-minute catchment area. We examined City Council 0 larger catchment areas, but they did not produce distinct Corner /Kona 40 neighbourhood results because of the proximity of stations. DIT 12 The ten-minute catchment areas included 1,252 of the Fire 24 original 2,028 households surveyed, 61.74% of the whole Gerezani 15 survey group. Jangwani 5 Some of the station areas had no respondents or very Kagera 96 few respondents within the 10-minute catchment. The Morocco Hotel 39 table below shows the number of respondents associated Kibo 43 with each station. It is possible that those with very small numbers or respondents may not be representative samples Kimara 20 for the station neighbourhood. Kinondoni B 83 Kisutu 1 Kivukoni 1 Magomeni Hospital 55 Magomeni Mapipa 62 Manzese 104 Manzese Argentina 126 Manzese TipTop 70 Mkwajuni 97 Morocco 41 Msimbazi Police 35 Mwanamboka 85 Mwembe Chai 55 Posta ya Zamani 0 Resort/Korogwe 24 Shekilango 13 Ubungo Maji 12 Ubungo Terminal 22 Urafiki 41 Usalama 42 Table A1.1-1  Households Surveyed Within 10 Mins of Each Station Appendix A / Part A1 24 A1.1 Overall Picture The analysis shows a pattern of distinct neighbourhoods within the BRT corridor. These can be categorised as follows; 01 CBD: Based on the survey results this area is not a residential neighbourhood. Stations City Council and Posta ya Zamani had no respondents whilst Kisutu and Kivuloni had only one response each and should be ignored as statistically insignificant. 02 City Fringe: There is a marked concentration of positive indicators in the City Fringe area. This can be defined as; DIT, Fire, Jangwani, Msimbazi Police, Gerezani Terminal, Mangomeni Mapipa and Morocco Hotel. 03 Northern Spur: A relatively prosperous cluster of stations; Morocco Terminal, Kinondoni B, and Mwanamboka. 04 Mid Zone: All stations between Mangomeni Hospital and Ubungo Maji. This area consistently shows high levels of deprivation. 05 West End: Stations from Kibo to Kimara Terminal. This is a mixed area showing both deprivation and some more positive indicators. Figure A1.1-8  City Zones, Aggregate Scores Station Neighbourhoods Based on 10 Minute Isochrone 25 Appendix A / Part A1 - Survey Methodologies & Results A1.1 Figure A1.1-9  Station Neighbourhood Prosperity Rank Scores – 10 Minute Isochrone % Earning a Station areas with Households % Households % % Higher quaity % Higher status Rank Station High rent 900k shilling household 3 or fewer Owned rented quality roofing professionals month 1 Baruti 18.18% 71.88% 21.88% 14.29% 33.33% 9.09% 2 Bucha 20.00% 76.92% 23.08% 0.00% 50.00% 0.00% 3 Corner /Kona 5.00% 70.00% 25.00% 10.00% 75.00% 20.51% 4 DIT 25.00% 36.36% 36.36% 0.00% 100.00% 9.09% 5 Fire 25.00% 34.78% 39.13% 0.00% 90.48% 17.39% 6 Gerezani 7.14% 20.00% 40.00% 0.00% 100.00% 20.00% 7 Jangwani 40.00% 40.00% 40.00% 0.00% 60.00% 20.00% 8 Kagera 20.43% 76.84% 23.16% 13.64% 8.33% 22.09% 9 Morocco Hotel 28.57% 71.43% 28.57% 20.00% 22.22% 20.59% 10 Kibo 13.95% 48.84% 48.84% 0.00% 100.00% 31.71% 11 Kimara 30.00% 47.37% 52.63% 0.00% 0.00% 55.00% 12 Kinondoni 13.58% 73.17% 25.61% 14.29% 5.00% 51.85% 13 Kisutu 0.00% 0.00% 0.00% 0.00% 100.00% 0.00% 14 Kivukoni 0.00% 0.00% 100.00% 0.00% 0.00% 0.00% 15 Magomeni Hospital 15.38% 67.92% 32.08% 35.29% 10.53% 23.08% 16 Magomeni Mapipa 24.56% 63.79% 36.21% 35.00% 9.09% 15.52% 17 Manzese 6.25% 39.18% 37.11% 0.00% 40.00% 11.88% 18 Manzese Argentina 14.88% 62.30% 33.61% 0.00% 36.36% 13.93% 19 Manzese TipTop 18.03% 53.85% 33.85% 0.00% 22.22% 18.18% 20 Mkwajuni 10.99% 50.00% 41.67% 22.50% 25.00% 7.29% 21 Morocco 15.79% 70.00% 30.00% 58.33% 36.36% 17.50% 22 Msimbazi Police 27.27% 40.63% 34.38% 0.00% 82.61% 5.88% 23 Mwanamboka 7.50% 59.04% 34.94% 13.79% 14.29% 9.64% 24 Mwembe Chai 33.33% 60.00% 40.00% 22.73% 0.00% 23.08% 25 Korogwe 20.83% 54.17% 45.83% 9.09% 0.00% 9.52% 26 Shekilango 15.38% 0.00% 0.00% 0.00% 0.00% 8.33% 27 Ubungo Maji 0.00% 0.00% 0.00% 0.00% 0.00% 9.09% 28 Ubungo Terminal 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 29 Urafiki 30.77% 36.59% 58.54% 0.00% 0.00% 17.50% 30 Usalama 21.43% 61.90% 38.10% 12.50% 5.88% 38.10% Table A1.1-2  Minute Isochrone Sorted Rank Scores Appendix A / Part A1 26 A1.1 The chat to the left shows the simple aggregate, unweighted rank for each neighbourhood against a basket of criteria. It shows a marked divergence between the very best areas and the rest. The picture is, however, not entirely even. The table on the next page shows the same rank scores by individual indicator. The inconsistent pattern points to diverse station neighbourhood characteristics. Although it is possible to generalise about wider zones each station neighbourhood should be judged on its own attributes. The numbers in this table show the ranking of each station area against different indicators. Tenure was not included in the overall ranking of the station neighbourhood. % Earning above Higher status Highest educational % Access to Mains % Access to Overall 900k shillings per Propserity Score Average ofessionals attainment Water Sewer indicator month 9.09% 9.68% 45.45% 31.21% 100.00% 3.03% 264.27% 29.36% 0.00% 21.43% 80.00% 35.29% 88.24% 5.88% 300.84% 33.43% 20.51% 8.33% 40.00% 31.25% 87.50% 12.50% 290.10% 32.23% 9.09% 0.00% 27.27% 34.17% 100.00% 83.33% 378.86% 42.10% 17.39% 18.18% 17.39% 30.83% 100.00% 83.33% 382.61% 42.51% 20.00% 26.67% 33.33% 38.00% 100.00% 93.33% 418.48% 46.50% 20.00% 25.00% 0.00% 20.00% 100.00% 80.00% 345.00% 38.33% 22.09% 3.30% 31.58% 26.15% 86.46% 16.67% 228.64% 25.40% 20.59% 0.00% 42.86% 21.28% 87.18% 61.54% 304.24% 33.80% 31.71% 4.88% 32.56% 26.05% 86.05% 6.98% 302.17% 33.57% 55.00% 0.00% 25.00% 34.00% 85.00% 10.00% 239.00% 26.56% 51.85% 3.85% 23.17% 26.87% 98.80% 50.60% 288.00% 32.00% 0.00% 0.00% 0.00% 30.00% 100.00% 0.00% 230.00% 25.56% 0.00% 0.00% 0.00% 30.00% 100.00% 100.00% 230.00% 25.56% 23.08% 0.00% 33.96% 30.18% 85.45% 34.55% 268.43% 29.83% 15.52% 3.45% 44.83% 29.52% 85.48% 46.77% 294.22% 32.69% 11.88% 5.00% 32.35% 27.79% 89.42% 4.81% 217.50% 24.17% 13.93% 5.69% 20.80% 29.13% 91.27% 15.87% 227.93% 25.33% 18.18% 0.00% 32.84% 21.43% 74.29% 20.00% 206.99% 23.00% 7.29% 4.71% 44.21% 34.85% 95.88% 23.71% 269.13% 29.90% 17.50% 21.05% 52.50% 42.20% 97.56% 51.22% 392.51% 43.61% 5.88% 17.65% 15.15% 30.86% 97.14% 74.29% 350.85% 38.98% 9.64% 8.11% 49.40% 43.88% 97.65% 36.47% 280.72% 31.19% 23.08% 0.00% 49.09% 25.45% 80.00% 3.64% 237.32% 26.37% 9.52% 15.79% 54.17% 37.50% 95.83% 4.17% 246.90% 27.43% 8.33% 0.00% 0.00% 10.00% 100.00% 92.31% 226.03% 25.11% 9.09% 0.00% 25.00% 10.00% 83.33% 91.67% 219.09% 24.34% 0.00% 0.00% 19.05% 10.00% 81.82% 81.82% 192.68% 21.41% 17.50% 0.00% 36.59% 21.95% 73.17% 19.51% 199.49% 22.17% 38.10% 0.00% 28.57% 31.67% 85.71% 4.76% 228.62% 25.40% 27 Appendix A / Part A1 - Survey Methodologies & Results A1.1 Household Size We have already seen that household sizes vary across the city. The pattern varies between neighbourhoods. The chart below illustrates the distribution of household sizes across station areas. Smaller households occur most often in the more prosperous city fringe station areas. Very large households are more likely to occur in the poorer mid zone. The Northern Spur, although it shows some signs of prosperity, is less likely to have smaller households. Figure A1.1-10  Station Neighbourhood Household Sizes – 10 Minute Isochrone The map, right, shows the distribution of households with three or fewer members. There is a noticeable concentration of smaller households in the city fringe area. Legend Medium-High (Above 25%) Medium (10-25%) Low (Below 10%) No Data Figure A1.1-11  Small Households Station Neighbourhoods - 10 Minute Isochrone Appendix A / Part A1 28 A1.1 Housing Quality The chart and the map below illustrate the distribution of higher quality (non-sheet metal) roofing across the city. We have used this as an indicator of more formal housing types. The CBD can be disregarded as a non-residential zone. Higher- quality roofing materials are found in the prosperous City Fringe and, to an extent in the West End of the corridor. This is consistent with the West End having mixed levels of prosperity. It is not surprising that the largely informal Mid Zone shows indications of having little formal housing. More unexpected is the low level of higher-quality roofing material in the more prosperous Northern Spur. This indicates that there may be more formal housing using less expensive materials in this zone. This, and evidence of larger family sizes would be consistent with this being an area favoured by aspiring families. Figure A1.1-12  High Quality Roofing – 10 Minute Isochrone Legend High (Above 50%) Medium (25-50%%) Very Low (Below 25%) No Data Figure A1.1-13  Higher Quality Roofing – 10 Minute Isochrone 29 Appendix A / Part A1 - Survey Methodologies & Results A1.1 Tenure The chart below illustrates the breakdown of tenure by station area. There is some anomalous data in the Ubungo and Shekilango areas, where a high proportion of those surveyed did not answer this question. Ownership tends to be lower in the more prosperous City Fringe and to the West End. Ownership is highest in the Northern Spur and the Eastern end of the Mid Zone. This is consistent with these areas being the more formalised, low rise residential areas. We have learned from this that tenure is not necessarily an indicator of prosperity. Figure A1.1-14  Tenure – 10 Minute Isochrone The map above shows that there are higher concentrations of home ownership in and around the Northern Spur and to the West End of the corridor. Legend High (Above 60%) Medium (30-60%) Low (Below 30%) No Data Figure A1.1-15  Home Ownership – 10 Minute Isochrone Appendix A / Part A1 30 A1.1 Rents The Chart and map below show the distribution of rents paid by station area. Janwani is an outlier, with all rents in the lowest bracket despite being in the prosperous City Fringe. This result should probably be disregarded as the sample size (5 respondents) is very low. More generally the preponderance of low rents occur in the poor Mid Zone and is also consistent with high levels of informal housing in this area. The highest rents occur in the established residential neighbourhoods in the Northern Spur, the very Eastern end of the Mid Zone and the City Fringe. Morocco Terminal and Mangomeni Hospital, which have the highest rents also have high levels of home ownership. Figure A1.1-16  Distribution of Rental Bands – 10 Minute Isochrone The map shows that the City Fringe area contains fewer tenants who pay higher rents, despite being a generally more prosperous area and having a high proportion of residents who rent rather than own. Legend High (Above 35%) Medium (15-35%) Low (Below 15%) No Data Figure A1.1-17  Distribution of Higher Rental Areas (>300,000 Shillings) – 10 Minute Isochrone 31 Appendix A / Part A1 - Survey Methodologies & Results A1.1 Incomes The chart below examines the breakdown of wages by station area. Whilst the lowest income bands feature strongly in the areas showing low levels of other prosperity indicators this is not entirely consistent. There appear to be relatively high- income levels in the Manzese and Manzese Argentina. This is hard to account for and is not consistent with the overall pattern. If this data is accurate it could indicate that this area contains a section of the population who could afford the transition to more formal housing were it made available. Figure A1.1-18  Distribution of Wage Bands – 10 Minute Isochrone The map shows unexpectedly high earnings levels in the Manzese area. Legend High (Above 30%) Medium (10-30%) Low (Below 10%) No Data Figure A1.1-19  Distribution of Higher-Earning Households – 10 Minute Isochrone Appendix A / Part A1 32 A1.1 High Status Professionals We have measured the proportion of the workforce who describe themselves as: Legislators Administrators & Managers, and Technicians & Associate Professionals, and used this as a measure of higher status professions. Baruti, Mkwajuni and Kona, towards the western end of the line, show the highest occurrence of these occupations. The chart and map below illustrate the spread. Figure A1.1-20  Distribution of Higher-Status Professional – 10 Minute Isochrone The map shows that population of the City Fringe area, though generally more prosperous than other areas, is not made up of a high proportion of higher status professions. Legend High (Above 30%) Medium (10-30%) Low (Below 10%) No Data Figure A1.1-21  Distribution of Higher-Status Professional – 10 Minute Isochrone 33 Appendix A / Part A1 - Survey Methodologies & Results A1.1 Educational Attainment The chart and map below examine educational attainment across the BRT corridor. The map illustrates the areas with college or university education. The highest scoring areas are concentrated in the Northern Spur and to the West End of the corridor. Figure A1.1-22  Distribution of Educational Attainment – 10 Minute Isochrone Legend High (Above 45%) Medium (25-45%) Low (Below 25%) No Data Figure A1.1-23  Distribution of Those With Higher Education– 10 Minute Isochrone Appendix A / Part A1 34 A1.1 Figure A1.1-24  Distribution of Those With no Formal Schooling – 10 Minute Isochrone The Chart above shows the distribution of respondents with no formal schooling. The result for Jangwani should be disregarded owing to the vey small sample size (5). Otherwise there is no strong pattern to this measure. 35 Appendix A / Part A1 - Survey Methodologies & Results A1.1 Prosperity Indicators The prosperity score is based on a basket of measures; possession of a bank account, smart-phone, air conditioning, motor cycle, telephone landline, mobile phone, computer or laptop, internet, motor vehicle, and fridge. Scores were particularly low in the Ubungo and Magomeni areas. The scores are illustrated on the chart and map below. Highest rated areas were around Gerezani, Morocco and Mwanamboka station areas. Figure A1.1-25  Distribution of Prosperity Scores – 10 Minute Isochrone The map shows that the Mid Zone and surrounding areas have low scores for prosperity indicators. Whilst this is consistent with the high levels of informal housing observed in these neighbourhoods it is not consistent with the some of the income data from the Manzese area. Legend High (Above 35%) Medium (30-35%) Low (Below 30%) No Data Figure A1.1-26  Distribution of Prosperity Scores– 10 Minute Isochrone Appendix A / Part A1 36 A1.1 Access to Utilities The chart and map illustrate where households have some access to sewers. There is a very noticeable divergence between the well served areas and the rest. The City Fringe and Northern Spur show better provision as does the Ubungo area. The Mid Zone and the West End are badly served. Other than Ubungo access to sewers is most common in the older, more established residential areas. Figure A1.1-27  Distribution of Some Access to Sewers – 10 Minute Isochrone The map shows that, apart from the Ubungo area neighbourhoods in the Mid Zone and west end are generally poorly served. Legend High (Above 60%) Medium (20-60%) Low (Below 20%) No Data Figure A1.1-28  Distribution of Some Access to Sewers – 10 Minute Isochrone 37 Appendix A / Part A1 - Survey Methodologies & Results A1.1 In contrast to levels of access to sewers the provision of piped water is generally more common. The chart and map illustrate where households have some direct access to mains water. Figure A1.1-29  Distribution of Some Access to Piped Water – 10 Minute Isochrone With some exceptions water provision tends to be less good in the Mid Zone and the West End of the corridor. Legend High (95-100%) Medium (85-95%) Low (Below 85%) No Data Figure A1.1-30  Distribution of Some Access to Piped Water – 10 Minute Isochrone Appendix A / Part A1 38 A1.1 Page left intentionally blank 39 Appendix A / Part A1 - Survey Methodologies A1.1 Summary & Conclusion 2,028 members of the public were interviewed some of the West End of the corridor. Levels of about their homes, incomes, employment and home ownership are uneven. There appears to certain prosperity indicators. The purpose was to be a concentration in and around the Northern understand the characteristics of individual station Spur and to a lesser extent at the West End of neighbourhoods in the corridor. The data gathered the corridor. Morocco, and Mangomeni Hospital has also informed analysis of housing affordability stand out as high-rental neighbourhoods. The reported in the main CDS report under the prosperous, higher quality homes in the City Fringe regeneration strategy. appear to command lower rental levels. The distribution of respondents by age and gender For most station areas income levels were appears normal. The average size of a household generally consistent with overall prosperity was 5.5 persons. Most of the survey group (54%) measures. Parts of the Mid Zone; Manzese and own their own home. Most tenants (90%) pay more Argentina, reported above average income levels. than 30,000 shillings per month in rent (US$132). This finding is hard to explain but may indicate Just over half those surveyed pay less than 50,000 that not all the population in informal housing in shillings per month (US$22). Just over half the the Mid Zone lack the ability to pay higher rents if population surveyed earn less than 300,000 the right homes became available. shillings per month (US$132) General prosperity indicators show a clear pattern The analysis of station neighbourhoods was based of deprivation in the Mid Zone. This zone of on 10 catchment areas. Four station areas have deprivation appears to be more consistent with been disregarded from the analysis as they had the distribution of informal housing than incomes, no resident population in the catchment or only education or professional status. It may indicate one person. The Central Business District (CBD) is, that there is potential to improve conditions by therefore, defined as a non-residential area. The improving housing. aggregate data pointed to four distinct zones in the Although there is still room for improvement most corridor. The stations areas within each of these neighbourhoods have high levels of access to zones were, however, not homogeneous. Each piped water. By contrast only the City Fringe and station neighbourhood has distinct characteristics. parts of the West End have adequate sewerage The most apparently prosperous areas are in provision. the City Fringe Zone and the Northern Spur. The needs of each station area are distinct The Mid Zone appears to be the most generally indicating the need for locally tailored disadvantaged. The station areas at the West End interventions. The more established City Fringe of the corridor are mixed, having some of the area is not necessarily the most expensive place to stronger and weaker indicators together. live. The extensive informal housing areas in the The City Fringe generally has a larger proportion Mid Zone are not uniform. of small households than other areas. It also has a large proportion of the higher quality housing. Better quality housing also occurs in Appendix A / Part A1 40 A1.1 Figure A1.1-31  Income Survey Aggregated Scores 41 Appendix A / Part A1 - Survey Methodologies Section A1.2 Appendix A / Part A1 42 Land Use Verification Survey Methodology The following pages are an extract from the approved methodology for the Land Use Verification survey which was carried out in Dar es Salaam during September 2017. 43 Appendix A / Part A1 - Survey Methodologies & Results A1.2 Land Use Verification Survey Methodology DSM Phase 1 BRT Corridor Land Use Verification Survey Submitted by: Submitted to: Aurecon South Africa (Pty) Ltd Broadway Malyan Aurecon Centre Riverside House Century City Southwark Bridge Road South Africa London SE1 9HA Contact Person: Contact Person: Harry van der Berg James Rayner T: +27 84 645 8947 T: +44 (0) 207 261 4200 Appendix A / Part A1 44 A1.2 CONTENTS Survey Content 1. Introduction / General 2. Land Use Verification Station Selection Method Representative “Standard” & Typical Station Selection 3. The Land Use Verification Survey Method: Transect Survey The Sample Area Overview of Transect Survey Content / Items: Transect Survey Questionnaire Fieldwork: Transect Survey Technique Transect Survey Logistics and Technicalities 4. The Land Use Verification Survey Method: Streetscape Survey The Sample Area Overview of Transect Survey Content / Items: Transect Survey Questionnaire Fieldwork: Streetscape Survey Technique 5. Timeline for Survey Execution Land Use Survey Logistics and Technicalities Timeline 45 Appendix A / Part A1 - Survey Methodologies & Results A1.2 SURVEY CONTENT 1. Introduction / General As part of the Phase 1 and Phase 2 scope of work there is a requirement to understand finer nature of existing land-uses along the BRT Line 1 corridor. This finer level of understanding can be used to support our verification and update of map data received from the client and stakeholders, as well as enhance our understanding and analysis of the existing land use condition and ability to accommodate change. In particular we want to understand land use patterns and functions that are within close proximity and under more direct sphere of influence of BRT stations. In our response to the project TOR we indicated that sample on-site land-use surveys would be used to enhance our understanding of the baseline assessment prepared in Phase 1. Below we outline a refined methodology based on our TOR response. Prior to commencing the land-use survey we would welcome comments on the proposed approach. 2. Land Use Verification Station Selection Method Representative “Standard” & Typical Station Selection Our TOR methodology response indicated: (c) Existing land uses and population density – using high-resolution digital imagery, and in conjunction with land use and socio-economic data provided by the client, BM will map land use and cadastre patterns for the project area and the immediate context. BM and partners will then carry out ‘on the ground’ surveys to validate the desktop study and based on the land uses observed, will apply and assess typical 4 ha ‘pixel’ areas of local residential and mixed use sections of the corridor to independently test population density assumptions, in association with CoLab’s growth estimates to confirm segmented density and population assessments. The aim of this approach was to identify representative “standard” and typical station that would increase our understanding across a range of land-use conditions in the Phase 1 corridor. This would help us pin-point the different land use characters and functions between informal and densely populated areas like Manzese, other older structured areas like at Fire and or City Council and emerging urban areas like Morocco. From our baseline assessment we have been able to identify a number of urban area conditions and proposed station typologies: 1 Methodology Appendix A / Part A1 46 A1.2 NAME TOD Typology Urban Area City Council Downtown CBD Kisutu Downtown CBD DIT Neighbourhood (C) City Centre Fire Neighbourhood (C) City Centre Jangwani Service Centre City Centre Magomeni Mapipa District Urban Usalama Neighbourhood (E) Urban Mwembechai Neighbourhood (E) Urban Kagera Neighbourhood (E) Urban Argentina Neighbourhood (E) Urban Manzese Neighbourhood (E) Urban Tip Top Neighbourhood (E) Urban Urafiki Neighbourhood (E) Urban Shekilango Neighbourhood (E) Urban Ubungo Maji Service Centre Urban Kibo Neighbourhood (F) Urban Kona Neighbourhood (F) Peri-urban Baruti Neighbourhood (F) Peri-urban Bucha Neighbourhood (F) Peri-urban Korogwe Neighbourhood (F) Peri-urban Magomeni Hospital District Urban Morocco Hotel District Urban Mkwajuni Service Centre Urban Mwanamboka Neighbourhood (D) Urban Kinondoni Neighbourhood (D) Urban Posta ya Zamani Downtown CBD Msimbazi Police Neighbourhood (D) City Centre Ubungo Terminal District Urban Kivukoni Terminal Gateway (A) CBD Gerezani Terminal Gateway (A) City Centre Morocco Terminal Gateway (A) Urban Kimara Terminal Gateway (B) Rural The survey method takes a selection of “representative” stations in-order assess this range of conditions and functions. The following candidate stations for the Land-use survey has been identified based on their location within the city and the typology assigned to them in the evolving corridor strategy so that each combination of urban area and TOD typology are covered. These will be surveyed under two distinct methodologies namely transect and streetscape: 1. City Council 1. City Council 2. Gerezani 2. Gerezani 3. Kimara 3. Manzese 4. Magomeni Mapipa 4. Baruti 5. Ubungo Maji 5. Kimara 6. Fire 7. Kinondoni 8. Manzese 9. Baruti 10. Kivukoni 11. Jangwani 12 Morocco Terminal 13. Kibu DSM Phase 1 BRT Corridor Land Use Survey 2 47 Appendix A / Part A1 - Survey Methodologies & Results A1.2 3. The Land Use Verification Survey Method: Transect Survey The Sample Area The Transect survey uses a sample area based on assessing 4 hectare representative transect areas for detailed land use and condition characteristics (see figure 2 - 6). The target is to survey the complete area within the sample area to capture all buildings within easy walking distance of the station. This will enable us to better assess residential and employment uses for different areas. These will help to calibrate existing mapping of population and employment densities which in turn feed in to calculations within the TOD evaluation matrix. We are particular interested in land uses within a direct sphere of influence of stations and how these may or may not change over distance. In order to capture this our survey is based on an off-set transect to one side of the trunk road. We will survey 5 stations with survey boundry of approx. 80m wide to 500m in length each. station area. Building Count: 105 Figure 2 | Kimara Transect 3 Methodology Appendix A / Part A1 48 A1.2 Building Count: 74 Figure 3 | Baruti Transect Building Count: 282 Figure 4 | Manzese Transect DSM Phase 1 BRT Corridor Land Use Survey 4 49 Appendix A / Part A1 - Survey Methodologies & Results A1.2 Building Count: 115 Figure 5 | Gerezani Transect Building Count: 39 Figure 6 | City Council Transect 5 Methodology Appendix A / Part A1 50 A1.2 Overview of Transect Survey Content / Items: See the next page for questionnaire The survey questions are designed to verify and calibrate specific elements of the baseline assessment that are subject to uncertainty. Some of these topics have a direct relationship to data that is being generated to assess stations in the TOD matrix element of the study: • Verification of residential and employment densities • Building heights and typologies • Better understand the vertical stacking of land uses to better understand the mixture of residential and commercial uses • The extent of commercial uses within areas of predominantly residential development • Condition of building stock Question Clarification 1. Location Ward, sub-ward and location code (generated from GIS data) 2. Land Use 2.1 Ground Floor Main Use (including no. of floors) 2.2 Upper Floor Main Use (including no. of floors) Based on city land use categories within documentation received by the Team from Municipal Councils 3. Physical Characteristics 3.1 Building Occupation (vacany rate) 3.2 General Condition (excellent, good, fair, poor) 3.3 Construction Type (brick, concrete, wood, metal sheet) The data collected from this survey will be combined with evidence from the team’s income survey which will collect further data on household size, tenure type, building quality and additional land use information. DSM Phase 1 BRT Corridor Land Use Survey 6 51 Appendix A / Part A1 - Survey Methodologies & Results A1.2 Transect Survey Questionnaire Indicative Sample Land Use Verification Survey: Transect Assessment 1.0 Location Location Ward Sub‐Ward 2.0 Land Use 2.1 Ground Floor Main Use: No.  No.  RESIDENTIAL Floors COMMERCIAL Floors INSTITUTIONAL Detached House Retail Government Office Semi Detached F&B Community Facility Villa Hotel Religious Apartments or Flats Offices Education Special Residential Leisure / Recreation Leisure / Recreation Duplex Workshops Health Local Market Commerical Space? Fuel Station 2.2 Upper Floor Main Use: No.  No.  RESIDENTIAL Floors COMMERCIAL Floors INSTITUTIONAL Detached House Retail Government Office Semi Detached F&B Community Facility Villa Hotel Religious Apartments or Flats Offices Education Special Residential Leisure / Recreation Leisure / Recreation Duplex Workshops Health Local Market Fuel Station 3.0 Physical Characteristics 3.1 BUILDING OCCUPIED? 3.2 GENERAL CONDITION 3.2 CONSTRUCTION TYP Yes Excellent Brick No Good Concrete Fair Wood Poor Metal Sheet 7 Methodology Appendix A / Part A1 52 A1.2 Sub‐Ward Location code Generated from GIS No.  No.  No.  INSTITUTIONAL Floors INDUSTRIAL Floors OTHER Floors Government Office Wholesale Warehouse Transport Terminal Community Facility Service Trade Public Works Religious Special Industry Agricultural Education Leisure / Recreation Health No.  No.  No.  INSTITUTIONAL Floors INDUSTRIAL Floors OTHER Floors Government Office Wholesale Warehouse Transport Terminal Community Facility Service Trade Public Works Religious Special Industry Agricultural Education Leisure / Recreation Health 3.2 CONSTRUCTION TYPE Brick Concrete Wood Metal Sheet DSM Phase 1 BRT Corridor Land Use Survey 8 53 Appendix A / Part A1 - Survey Methodologies & Results A1.2 Fieldwork: Transect Survey Technique Firstly, the trasect survey data will be collected via a smart device (Samsung T116 - 7”) using the application “Collector” for ArcGIS from the ESRI software suite (for the transect component) which enables collecting of and editing of data in the field. The devices will also be purchased in country to ensure that import restrictions do not impact on the survey timeline. Some features of the application include: • Collect and update information in the field. • Attach photos to captured features • Take maps and data offline and sync changes when connected. • Improve data quality with easy-to-use map-driven forms. • Track work and report your activities live. • Live monitoring of fieldwork progress. Transect Survey Logistics and Technicalities The spatial extent of the Land Use survey falls within the phase 1 study area where approximately 615 (final number to be confirmed) building footprints across 5 stations have been predefined. Importantly, these building footprints were determined with the data available to the consultant team and it cannot be assumed that all the building footprints are 100% correct. Accordingly, and in terms of the transect survey, the ArcGIS Collector app allows “on the fly” amendments to any building footprints which does not align to the reality on the ground. Importantly, this does not include re-projection of polygons in the case of minor misalignments (between different aerial imagery), but rather to redraw the polygon if the building footprint has changed, or a new development has occurred on a previously vacant land parcel within the survey area. Conversely, the images taken in the transect survey does not hold any potential on the ground issues as it will simply record the current state. 9 Methodology Appendix A / Part A1 54 A1.2 4. The Land Use Verification Survey Method: Streetscape Survey The Sample Area The second element of the survey will be to carry out a streetscape assessment of 13 station areas which will be focused on roads which are within the 10 minute walking distance of the station (see figure 8 to 15). The walkshed analysis was carried out by defining the walking distance that can be reached in +-10 minutes from each station assuming a walking speed of 4,5 km/h. The output is based only on the street graph analysis. The result derives from the sum of the lengths of all the paths included in the 10 minutes isochrones, which were run starting from the centre of each station. Other elements related to road characteristics, as road gradient, width or paving, have not been taken into account. Figure 7 | Streetscape Survey Location map DSM Phase 1 BRT Corridor Land Use Survey 10 55 Appendix A / Part A1 - Survey Methodologies & Results A1.2 Figure 8 | Kimara Streetscape Figure 9 | Baruti, Kibo and Ubungo Maji Streetscape 11 Methodology Appendix A / Part A1 56 A1.2 Figure 10 | Magomeni Mapipa, Jangwani and Fire Streetscape Figure 11 | Marocco and Kinondoni Streetscape DSM Phase 1 BRT Corridor Land Use Survey 12 57 Appendix A / Part A1 - Survey Methodologies & Results A1.2 Figure 12 | City Council Streetscape Figure 13 | Kivukoni Streetscape 13 Methodology Appendix A / Part A1 58 A1.2 Figure 14 | Manzese Streetscape Figure 15 | Gerezani Streetscape DSM Phase 1 BRT Corridor Land Use Survey 14 59 Appendix A / Part A1 - Survey Methodologies & Results A1.2 Overview of Streetscape Survey Content / Items: See the below for questionnaire • Better understand the vertical stacking of land uses to understand the mixture of residential and commercial uses • Patterns of informal trade in proximity to station areas • Parking provision and patterns in proximity to station areas • Building Condition • Street Condition • Parking provision • Extent of informal street trade • Pedestrian Activity • Traffic Congestion Question Clarification 1. Location Street name/code, segment code, total road length (generated from GIS data) 2. Land Use 2.1 All visible Ground Floor Uses (including indication of predominant use) 2.2 All visible Upper Floor Uses (including indication of predominant use) Based on city land use categories within documentation received by the Team from Municipal Councils 3. Physical Characteristics 3.1 Predominant Building Height 3.2 Predominant Building Condition (excellent, good, fair, poor) 3.3 Predominant Street Condition (excellent, good, fair, poor) 4. Streetscape 4.1 Street Character (evidence public realm elements) 4.2 Parking (qualitative assessment of parking conditions) 4.1 Street Trade (qualitative assessment of informal trading activity) 4.1 Activity (qualitative assessment of traffic and pedestrian activity) The data collected from this survey will be combined with evidence from the team’s income survey which will collect further data on household size, tenure type, building quality and additional land use information. 15 Methodology Appendix A / Part A1 60 A1.2 Fieldwork: Streetscape Survey Technique The streetscape survey component will be collected using 360 degree images taken at 100m intervals across the 175km of road lines across the 13 stations (1700 data points). This will be done using the Richoh Theta S Spherical Digital Camera mounted on a “Tuk Tuk” or traditional motorcycle. The output thereof will be similar to Google’s popular “Street view” web application. These 360 degree photos will then be analysed individually and used to populate the streetscape survey questionnaire after the survey has been completed. 5. Timeline for Survey Execution In terms of timeline, the two tables below illustrate the logic applied in calculating the total amount of days that it would take to collect 5 transects surveys of 615 buildings as well as covering 175 km op road at 100m intervals. Accordingly, the survey team will consist out of 4 Aurecon staff members (including on-site tech support) with the rest of the survey team consisting out of 7 local surveyors who have previously assisted Aurecon on surveys (2 for the streetscape survey and 5 for the transect survey). It is important to note that an income survey will be run concurrently to the land use survey and that the 4 Aurecon staff members will be responsible for managing both surveys at the same time. With the above context in mind, the consultant team has estimated that a minimum of 24 surveys per surveyor will be conducted per day. Each survey will take approximately 10 mins per building with another 10 mins allocated to move from the one survey point to the next. With these numbers in mind, as well as a total land use survey team of 24 surveyors, it is estimated that the survey will be completed within 6 working days as illustrated below. Furthermore, one day will be utilised to train the surveyors on the use of the smart device as well as the ArcGIS Collector application which brings the total to 7 days for completion of the Land Use Surveys. This training will also include a dry run of the survey to ensure that all potential issues are dealt with before the official start of the survey. Single Time Surveys Field (Approximate) Survey survey between Survey Type per day per workers/ Total Survey Count duration surveys device devices Days (min) (min) LU (Transect) 615 10 8 23 5 6 LU (Streetscape) 1700 1 3 105 2 8 Table 2: Survey Schedule WEEK NO. SUNDAY MONDAY TUESDAY WEDNESDAY THURSDAY FRIDAY SATURDAY 1 2 Travel to Dar es Salaam Training Fieldworkers Capturing Surveys 16 Methodology 61 Appendix A / Part A1 - Survey Methodologies & Results A1.2 Streetscape Survey Questionnaire Indicative Sample Land Use Verification Survey: Streetscape Assessment 1.0 Location & Individual Street Name / Code Section Code (100 2.0 Land Use 2.1 Ground Floor Uses Present: RESIDENTIAL COMMERCIAL INS Please select all uses that you can  Detached House Retail see within the street segment and  Semi Detached F&B then highlight the use that is the  Villa Hotel predominant use Apartments or Flats Offices Special Residential Leisure / Recreation Duplex Workshops Local Market Fuel Station 2.2 Upper Floor Uses Present: RESIDENTIAL COMMERCIAL INS Please select all uses that you can  Detached House Retail see within the street segment and  Semi Detached F&B then highlight the use that is the  Villa Hotel predominant use Apartments or Flats Offices Special Residential Leisure / Recreation Duplex Workshops Local Market Fuel Station 3.0 Physical Characteristics 3.1 PREDOMINANT BUILDING  3.2 PREDOMINANT BUILDING  3.3 PRE HEIGHT CONDITION Excellent No.  Good Floors Fair Poor 4.0 Streetscape (completed once per street section) 4.1 STREET CHARACTER 4.2 PARKING Formal Sidewalks (>2 meters) Off Street Parking Formal Sidewalks (<2 meters) On Street Parking (Regulated) Street Lighting On Street Parking (Informal) Semi Culverts / Drainage Ditches Car Park Lot R Dala Dala Stops Multi‐storey Car Park Steet Furniture Motorcycle Parking Trees Road Encroachment? 17 Methodology Appendix A / Part A1 62 A1.2 Section Code (100m segment) Total Road Length Generated by sampling method INSTITUTIONAL INDUSTRIAL OTHER Retail Government Office Wholesale Warehouse Transport Terminal F&B Community Facility Service Trade Public Works Hotel Religious Special Industry Agricultural ffices Education ation Leisure / Recreation hops Health arket ation INSTITUTIONAL INDUSTRIAL OTHER Retail Government Office Wholesale Warehouse Transport Terminal F&B Community Facility Service Trade Public Works Hotel Religious Special Industry Agricultural ffices Education ation Leisure / Recreation hops arket ation DING  3.3 PREDOMINANT STREET  TION CONDITION xcellent Excellent Good Good Fair Fair Poor Poor KING 4.3 STREET TRADE 4.4 ACTIVITY rking Roaming Traders Traffic Congestion (High, Medium, Low) ated) Stationary Traders Pedestrian Activity (High, Medium, Low) mal) Semi‐permanent Traders k Lot Road Encroachment?  Park rking ment? DSM Phase 1 BRT Corridor Land Use Survey 18 63 Appendix A / Part A1 - Survey Methodologies & Results A1.2 Land Use Survey Results 80m 10 min STATION 500m STATION Overview In September 2017 the team undertook a series of surveys their location within the city and the typology assigned to further verify and calibrate the land use data received to them in the evolving corridor strategy so that each at the project inception, helping to understand the finer- combination of urban area and TOD typology are covered. grain nature of existing land uses along the BRT Line We surveyed 6 station areas in detail with survey boundary 1 corridor. This enhanced level of understanding has been of approximately 80 m wide to 500 m in length. A second used to support our land use modelling exercise as well as photographic survey was also carried out using 360° camera enhanced our understanding of the existing land’s ability to which captured images at 100 m intervals along all roads accommodate change. In particular we want to understand radiating out from the station as far as a 10 minute nominal land use patterns and functions that are within close walking distance. This survey has helped us understand proximity and under more direct sphere of influence of BRT general land use patterns in areas further away from the stations. station as well as streetscape quality assessments. The survey method takes a selection of “representative” To further validate and make sense of the land use surveys, stations in-order assess this range of conditions and insight gained has been correlated with focus group and functions. The Team selected candidate stations based on household survey work carried out at the same time. Character Zone Total Buildings Vacancy Rate Institutional Commercial Residential Mixed Use Industrial Other City Council CBD 46 2 0% 11% 46% 35% 0% 9% Gerezani City Centre 116 42 4% 47% 41% 2% 0% 6% Manzese Urban 288 42 97% 0% 2% 0% 0% 0% Shekilango Urban 194 2 52% 0% 28% 2% 14% 4% Baruti Peri-urban 103 0 85% 0% 13% 1% 0% 0% Kimara Urban Periphery 180 1 69% 0% 26% 4% 0% 0% Average 51% 10% 26% 7% 2% 3% Figure A1.2-1  Land Use Survey Results Appendix A / Part A1 64 A1.2 Results Residential uses predominate in all areas other than the and supporting photo evidence it became clear that the CBD and city centre where there is a higher percentage of areas mapped as mixed use by the municipality contain non-residential uses such as commercial and institutional. informal street traders who are not locate in physical This is also the area where mixed use development is buildings but instead have stalls or lean-to structures. common. There was also significant small-scale employment content The CBD area around City Council features significantly less through local entrepreneurs, small scale trading and other residential density with more government and corporate commercial or self-employed activities. Morogoro Road offices, retail and hospitality facilities. The land use survey corridor shows scope for more employment uses given its results show that the split of uses for mixed use buildings enhanced mobility and would benefit from more office- in the CBD are balanced more towards commercial mixed type employment uses near its key stations outside the city use (36% Resi vs 64% Non-resi) whilst those in Gerezani are core. much more resi-led mixed use (73% vs 27%). There was less than expected mixed use development in Manzese but reviewing the comments and observations Total Buildings Mixed Use Vertical Split No. % Resi Non-Resi City Council 46 5 10.9% 36% 64% Gerezani 116 54 46.6% 73% 27% Manzese 288 0 0% N/a N/a Shekilango 194 0 0% N/a N/a Baruti 103 0 0% N/a N/a Kimara 180 0 0% N/a N/a Average 55% 45% Figure A1.2-2  Land Use Survey - Mixed Use Split 65 Appendix A / Part A1 - Survey Methodologies & Results A1.2 Streetscape Survey Results Overview Overview The streetscape survey takes a selection of 12 Street Character representative stations areas in order to evaluate Starting from the Street Character theme, the results different aspects of streetscape condition such as street show a generalized lack of essential street elements, as Character (evidence public realm elements), Parking sidewalks, formal public transport stops and especially (qualitative assessment of parking conditions), Street Trade street furniture, which is almost missing. Street lighting (qualitative assessment of informal trading activity) and and drainage infrastructure are more common features Activity (qualitative assessment of traffic and pedestrian instead. Furthermore, when the sidewalk area is activity). The gathering of this information allows the formalized, it is more common to have wide sidewalks elaboration of a qualitative assessment of mobility/ (>2 m) instead of narrow ones (<2 m). Zones 1, 2 and 3 follow accessibility issues related to functional street features on this pattern, while zone 4 differs from the general trend: local roads surrounding the BRT station areas. sidewalks, street lighting and drainage systems are included To better understand the streetscape changes within the in the street environment almost 50% of analysed cases. city, it is possible to subdivide the surveyed stations into 4 areas according to their common features: • 3 in zone 1, which covers the suburban Parking stops (Kimara; Baruti; Kibo) Regarding Parking, informal options are the most commons • 3 in zone 2, which is the central part of the corridor ones. Almost 65% of surveyed streets are characterized (Ubungo Maji; Manzese; Magomeni Mapipa) by informal on street parking, while off street parking and motorbike parking are pretty much diffuse as well. Regulated • 2 in zone 3, Morocco area (Kinondoni; Morocco) on street parking, car park lots and multi-storey car parks are • 4 in zone 4, which covers the CBD and Kariakoo almost absent instead. Due to its informal character, in some market area (Jangwani, Fire, Gerezani, City Council). cases parking causes road encroachment and carriageway obstruction. Zone 3 Zone 1 Zone 2 Zo ne 4 Figure A1.2-3  Surveyed Stations Subdivided by Zones Appendix A / Part A1 66 A1.2 Street Character Street character 1200 1000 800 600 400 200 0 No Yes No Yes No Yes No Yes No Yes No Yes Formal Formal Street Culverts / Dala dala Street sidewalks sidewalks lighting Drainage stops furniture >2m <2m ditches Figure A1.2-4  Street Character, Total Values Parking Parking 1200 1000 800 600 400 200 0 No Yes No Yes No Yes No Yes No Yes No Yes No Yes Off street On street On street Car park lot Multi-storey Motorcycle Road parking parking parking car park parking encroachment (regulated) (informal) Figure A1.2-5  Parking, Total Values 67 Appendix A / Part A1 - Survey Methodologies & Results A1.2 Looking at the different areas, in zone 1 parking is missing, Conclusion zone 2 follows the general trend, while zones 3 and 4 show a About 1250 points have been surveyed with the aim of high level of informal on street parking. Road encroachment understanding street features on local roads surrounding happens mostly in zones 2 and 4, even though also zone the BRT station areas. The data gathered show a generalized 3 presents a high level of informal parking. lack of fundamental street elements, as formal sidewalks, street lighting and drainage systems. The streetscape is Street Trade characterised by on-street informal parking and stationary street traders instead. Even though these elements may Street trade activities play an important role in shaping potentially cause a certain level of road encroachment, the Dar es Salaam street environment. Stationary traders limited number of cars limits congestion issues. Anyway, and semi-permanent traders are part of the streetscape with the forecasted car ownership increment, traffic flows almost in half of the surveyed roads, while roaming traders will increase consistently. Local streets are calling for a are generally less common. Street trades activities are streetscape reorganization of in order to avoid severe traffic pretty much formalized and therefore they rarely cause disruption and protect pedestrian activities. road encroachment. Looking at the different city zones, these characteristics constantly recur with few variations in all of them. Only zone 4 shows a higher number of Results are not always homogeneous: in each zone, distinct stationary traders, which directly derives from the strong characteristics emerge as the predominant ones. Zone 1, characterization as market area. which is suburban, is characterised by a poor streetscape populated by medium pedestrian activity. The central part of the corridor (Zone 2) and Morocco area (Zone 3) show a Street Activity prevalence of informal on-street parking and consequently Street Activity indicator evaluates the intensity of vehicular most of road encroachment cases. On the other hand, Zone and pedestrian flows on the street. On a general level, traffic 4 streetscape is highly influenced by its commercial nature, congestion is medium in 40% of cases and low in 53%, with high pedestrian activity and street trade. while pedestrian activity is almost precisely subdivided between medium and low. High values represent a smaller percentage in both cases, even if pedestrian activity shows a greater tendency of this kind. That is especially due to zone 4 values, where the commercial nature of the area raises high pedestrian activity component to 30%. In zones 1 and 2, medium levels of both vehicular and pedestrian activity are observed more frequently, covering the majority of cases. Appendix A / Part A1 68 A1.2 Street Trade Street trade 1200 1000 800 600 400 200 0 No Yes No Yes No Yes No Yes Roaming traders Stationary traders Semi-permanent Road traders encroachment Figure A1.2-6  Street Trade, Total Values Street Activity Activity 700 600 500 400 300 200 100 0 High Medium Low High Medium Low Traffic congestion Pedestrian activity Figure A1.2-7  Street Activity, Total Values 69 Appendix A / Part A1 - Survey Methodologies Section A1.3  Appendix A / Part A1 70 Transport Survey Methodology The following pages are an extract from the approved methodology for the Transport Survey which was carried out in Dar es Salaam during February /March 2017. 71 Appendix A / Part A1 - Survey Methodologies & Results A1.3 Transport Survey Technical Approach and Methodology Introduction The survey and analysis of existing traffic and Survey is divided in four parts: mobility behaviours is concerned with the existing • Manual Turn and Link Classified Counts at junctions condition and traffic flow/mobility demand, • Origin-Destination Survey which will provide the backbone of the project (catchment area of BRT Phase 1 corridor) and the capacity of • Transit Passengers Survey the facilities in demand. • Traffic Congestion Survey Surveys have be carried out in February and September 201. Item Contents Schedule Target / Remarks To capture traffic movement MON - WED - 6 intersections points (6 hours Count) Manual Turn and Link by counting number of FRI - SAT Fieldworker Requirement and staffing: 38 Classified Counts vehicles on main junctions AM 5:30 - 8:30 at Junctions PM 16:30 - 19:30 Questions on generic One WEEKDAY 11 locations, 34 Fieldworkers information of the trip (ward/ AM 05:30 - 08:30 subward you departed from, PM 16:30 - 19:30 final destination, reason of the journey, frequency of the Origin-Destination Survey journey) and questions on the (Roadside Interview driver and vehicle occupants to Motorists) (age, gender, nr. of passengers, kind of vehicle). These interviews aim to estimate the Origin-Destination matrix for the AM and PM peak period. Questions on information of ONE WEEKDAY Given the ridership of the system, the trip (ward/subward you AM 05:30 - 08:30 approx. 6000 interviews would represent departed from, final destination, a meaningful sample assuming that PM 16:30 - 19:30 reason of the journey, frequency one third of the existing demand is of the journey, chain of trip captured by the six transport nodes. Transit Passengers Survey and related travel time) These Final agreement with Aurecon was to interviews aim to estimate the undertake surveys for more man-hours catchment area of BRT Phase 1 than specified (increased manhours and related modal share. possible through resource optimization) rather than focus on 6000 interviews Table A1.3-1  Transport Survey Organization Appendix A / Part A1 72 A1.3 Survey Method and Programme Survey campaign has been carried out over one week. Below detailed programme: Activities developed: • Field Preview • Field survey: hiring, training, transport, paying, controlling outputs • Compilation of available data • Planning field surveys: locations and procedures • Data entry: cleaning, processing • Planning & developing data entry: software and procedures • Field checking: locations, team sizing and pilot surveys Possible Survey Days & Programme | February 2017 Monday Tuesday Wednesday Thursday Friday Saturday Sunday 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 Training Survey Data Capturing and Cleaning Table A1.3-2  Transport Survey Programme 73 Appendix A / Part A1 - Survey Methodologies & Results A1.3 Survey Activities The schedule for different type of surveys, survey time, number of personnel deployed is as indicated below: Data Collection and Capturing Possible Survey Days & Resources Allocation 1, Fri 2, Sat 4, Mon 5, Tue 6, Wed 7, Thu 8, Fri 9, Sat Training & 45 Dry-Run Traffic Survey 38 38 38 38 Roadside 34 Interview Spare Day BRT Station 5 5 9 5 43 5 Interviews Coordinators 2 2 2 2 2 2 Staffing 45 45 45 45 45 45 45 Saturday 2 & 9: Could be Allocated for Additional Training/Practice - thus Using Spare day for Saturday Traffic Counts Table A1.3-3  Survey Activities & Resources Allocation Survey Times: Roadside and BRT Stations Interview Periods Could be Extended with 1 hour After AM and 1 hour Before PM peak to Increase Sample Staffing: Final Number of Fieldworkers to Determine Final Resource plan Programme: Actual Dates to be Confirmed - any Possible Allocation at this Stage - Additional Training day Before Weekend Possibly Required The survey can be undertaken either manually by Enumerators were recruited and demonstration about completing the survey form, or by using an electronic the survey process was undertaken on various dates as handheld device, which is pre-programmed with the indicated on final survey schedule depending on the type questionnaire content. of survey to be undertaken. Intensive Training and field The Aurecon team proposal included the use of electronic demonstration was undertaken one day before the actual devices. Information is captured on devices and synchronise survey data to cloud-based servers via the cellular network as soon as possible when the network is available. Data captured is uploaded to a Microsoft SQL data warehouse after which real-time collation and data screening is performed. This step merges the data streams and will ultimately enable the seamless transition of data into the survey database. Due to unexpected customs restrictions at Dar Es Salaam’s airport the use of electronic devices has been excluded. Hence, all data were recorded manually on a hard copy. Appendix A / Part A1 74 A1.3 Manual Turn and Link Classified Counts at Junctions Directional Flow Counting was undertaken at intersections 1. Kimara Terminal aiming at establishing the volume of vehicles towards and 2. Morogoro Road – Nelson Mandela Road away from the intersection in each direction and every 3. Morogoro Road – Kagera Street possible combination. 4. Morogoro Road – Kawawa Road The classification of vehicles was simple: LV (including taxis), and HV (including Dala Dalas and larger buses). 5. Morogoro Road – Bibi Titi Mohammed Street The counts were conducted at critical links and intersections 6. Bagamoyo Road – Kawawa Road for a period of 6 hours including the morning and evening Actual survey for the various survey types were undertaken peak hours. The aim of these surveys is to assess the on four days (MON - WED - FRI - SAT) over two time frames magnitude of traffic volumes entering and exiting main during the day: nodes/intersections, such as • AM 05:30 - 08:30 • PM 16:30 - 19:30 6 1 2 3 4 5 Figure A1.3-1  Manual Turns and Traffic Counts Location 75 Appendix A / Part A1 - Survey Methodologies & Results A1.3 Each direction was assigned numbers to simplify the afternoon, one page per hour. On each page, they indicated Directional Flow Counting and each surveyor sketched the the time that they started counting (on that specific page), intersection and indicated the direction being counted. the hour within the peak period (1, 2 or 3), the location, the One surveyor was assigned to each direction and one movement and the quarter hour interval for the hour. supervisor was responsible for each intersection. For each The survey teams were set in such a way that one team approach there was one reserve surveyor. Therefore for a on the side of the road, taking the details of the vehicles four way intersection there were 8 surveyors whereas for a heading downtown and the other for the upcountry vehicles. “T” junction 6 surveyors were deployed. The form for each survey is shown below Each surveyor was provided with sheets having a class of vehicles to be counted. Counting was done within a controlled time of 15 minutes intervals. Origin-Destination Survey Each traffic form was personalised for each individual hour of the survey. The typical surveyor used 6 pages Origin-Destination surveys were conducted through (double sided) per day, 3 in the morning and 3 in the roadside interviews on major entry/exit points, on the road corridor along BRT Phase 1 where important activity locations are situated. Time: Hour _______ of 3 Survey point: (Mark "X") Kimara Terminal MOVEMENT COUNTS (See back of clipboard) The survey team assigned to one location comprised of police officers and 2 surveyors. The Survey period ranged Date: Morogoro Road & Nelson Mandela Road Mark "X" A B C D E F G H I J K L Morogoro Road & Kagera Street Name: Morogoro Road & Kawawa Road Morogoro Road & Bibi Titi Mohammed Street Page Number: from 05:30 to 08:30 and from 16:30 to 19:30. Surname: Bagamoyo Road & Kawawa Road LV: Cars, motorcycles, etc. HV: Trucks, buses etc. LV: Cars, motorcycles, etc. HV: Trucks, buses etc. LV: Cars, motorcycles, etc. HV: Trucks, buses etc. The police officers were responsible for stopping the first coming vehicle. Once the vehicle was stopped, the supervisor was responsible for providing more detailed information regarding the purpose of the survey, if requested 0-15min and further possible contacts to those who required that. The questions used are shown in the following page. 30-45min 15-30min . 45-60min Figure A1.3-2  Traffic Counts Sheets Sample Appendix A / Part A1 76 A1.3 ROAD-SIDE INTERVIEWS 1. SITE INFORMATION (automatically filled) 1.1 Serial number (number) 1.2 Date (date format) 1.3 Time (time format) 1.4 Location code (multiple choice: SITE1, SITE2….) 1.5 Surveyor (text) 1.6 Direction (multiple choice: SITE direction 1, SITE direction 2) 2. INFORMATION FILLED BY SURVEYOR 2.1 Type of vehicle (multiple choice: motorbike, car) 2.2 N° of passengers including drivers (number) 3. TRIP INFORMATION 3.1 What is the purpose of this trip? (multiple choice: going home, going to work, going to school, going to shopping, errand, accompany, other) 3.2 How frequently do you make this trip? (multiple choice: daily, 2-3 times a week, once a week, once every fortnight, once a month, rarely/first time) 3.3 Where were you at the beginning of this trip? (multiple choice: home, work, school, shopping, errand, other) 3.4 Where did your journey start (WARD/KATA and SUB-WARD/MITAA)? (multiple choice: WARD and SUB-WARD name, ‘OTHER’ if out of Dar – fill in field) – print out of Ward’s map 3.5 What is your final destination after the BRT (WARD/KATA and SUB-WARD/MITAA)? (multiple choice: WARD and SUB-WARD name, ‘OTHER’ if out of Dar – fill in field) – print out of Ward’s map 3.6 What is the average journey time of your entire trip? (minutes) 3.7 At what time did your journey start? (hour/minute) 4. DRIVER INFORMATION 4.1 Age (number) 4.2 Gender (multiple choice: male, female) 4.3 N° of households components (number) 4.4 N° of cars owned by household (number) 4.5 Occupation (Self-Employed, Employed, Student, Not in employment or education, other) Figure A1.3-3  Origin-Destination Survey Questionnaire 77 Appendix A / Part A1 - Survey Methodologies & Results A1.3 The number and location of survey stations, is the following: The timing of the roadside interviews will coincided with 1. Morogoro Road – Kimara Terminal that of traffic counts to facilitate adjustment for sampling. The questionnaire were setting out questions on generic 2. Morogoro Road – Baruti information of the trip (ward/subward departed from, final 3. Morogoro Road – Nelson Mandela Road destination, reason of the journey, frequency of the journey) 4. Morogoro Road – Tip Top and questions on the driver and vehicle occupants (age, 5. Morogoro Road – Kagera street gender, nr. of passengers, kind of vehicle). These interviews aimed to estimate the Origin-Destination matrix for the AM 6. Morogoro Road – Kawawa Road and PM peak period. 7. Kawawa Road – Mkwajuni Road The information will be obtained by trained enumerators 8. Bagamoyo Road – Kawawa Road and experienced supervisors and include: type of vehicle, 9. Morogoro Road – Bibi Titi Mohammed Street origin and destination, trip purpose, place of residence and employment of road user and frequency of travel. 10. Julius K. Nyerere Road – Msimbasi Street The total amount of questionnaires collected is about 1,106. 11. Sokoine Drive Transit Passengers Survey 8 1 2 3 7 4 5 6 9 11 10 Figure A1.3-4  Origin-Destination Survey Location Appendix A / Part A1 78 A1.3 The survey has been conducted on the BRT trunk route Transit Passengers surveys are made for three hours over at major stations and routes at terminal areas. The basic two time frames during one weekday: purpose of the survey will be to collect information AM 05:30 - 09:30 regarding origin, destination, trip purpose, frequency of PM 16:30 - 20:30 travel and other particulars. The random survey sampling The number and location of survey stations, is the technique will be adopted to survey the passengers and will following: cover all modes. The aim of these surveys is to assess the BRT Phase 1. Kimara Terminal 1 catchment area as well as the mobility habits and related 2. Ubungo Terminal mean of transportation of passengers entering the study 3. Magomeri Kagera area. This will define the actual project boundaries of the 4. Morocco Terminal integrated master planning exercise to be carried out during the second stage of the project process. 5. Gerenzani Terminal 6. Kivukoni Terminal 1 4 2 3 6 5 Figure A1.3-5  Transit Passenger Survey Location 79 Appendix A / Part A1 - Survey Methodologies & Results A1.3 BRT INTERVIEWS 1. SITE INFORMATION - (automatically filled) 1.1 Serial Number - (Number) 1.2 Date (Date Format) 1.3 Time - (Time Format) 1.4 Location code - (Multiple choice: BRT1, BRT2, etc) 1.5 Surveyor - (Text) 2. TRIP INFORMATION 2.1 What is the purpose of this journey? (multiple choice: going home, going to work, going to school, going to shopping, errands, accompany, other) 2.2 How frequently do you make this trip? (multiple choice: daily, 2-3 times a week, once a week, once every fortnight, once a month, rarely/first time) 2.3 Where were you at the beginning of this trip? (multiple choice: home, work, school, shopping, errand, other) 2.4 Where did your journey start? (WARD/KATA and SUB-WARD/MITAA)? (multiple choice: WARD and SUB-WARD name, ‘OTHER’ if out of Dar – fill in field) – print out of Ward’s map 2.5 Did you get another transport (or more than one) before this one? (multiple choice 1° mode: walk, bicycle, car, motorbike bus, Daladala, other) (if present multiple choice 2° mode: walk, bicycle, car, motorbike, bus, Daladala, other) 2.6 How long did it take (related to first mode)? (minutes) How long did it take (related to second mode)? (if second mode is present: minutes) 2.7 2.8 Which is the BRT station of your final destination? (multiple choice: BRT stops) 2.9 What is your final destination after the BRT (WARD/KATA and SUB-WARD/MITAA)? (multiple choice: WARD and SUB-WARD name, ‘OTHER’ if out of Dar – fill in field) – print out of Ward’s map 2.10 Are you going to take another transport mode (or more than one) after the BRT? (multiple choice: walk, bicycle, car, motorbike, bus, Daladala, other) (if present multiple choice 2° mode: walked, bicycle, car, motorbike, PT-BUS, DALA DALA, other) 2.11 How long will it take (related to first mode)? (minutes) How long will it take (related to second mode)? (if second mode is present: minutes) 2.12 3. TRAVELLER INFORMATION 3.1 Age (number) 3.2 Gender (multiple choice: male, female) 3.3 N° of households components (number) 3.4 N° of cars owned by household (number) 3.5 Occupation (multiple choice: Self-Employed, Employed, Student, Not in employment or education, other) Figure A1.3-6  Transit Passengers Survey Questionnaire Appendix A / Part A1 80 A1.3 A total of 6 stations were surveyed, a sample form is shown Constraints And Observations in the previous page. The total amount of questionnaires collected is about 2225. Traffic Survey These surveys will inform the design of infrastructural AM and PM times changed before the survey commenced elements, particularly junctions and areas of immediate due to a number of reasons which include first and foremost proximity to transit stops, MIC will develop a micro- the safety of the surveyors, as some of the girls were very simulation model of the two major nodes within each action concerned about traveling to and from the survey points area and surrounding elements as required. before sunrise and after sunset. (at 6 o’clock AM it was still In fact, in MIC’s philosophy, traffic micro-simulation models dark and after 6 o’clock pm the sun had already set) Also, are used as design tools to quantitatively evaluate design due to the fact that the students were using pen & paper, solutions. trying to read and write in the dark was a possible challenge. This allows a deep comprehension of the combined effect Roadside Survey of multiple mobility-related phenomena that typically occur in urban contexts. These include impacts of adjacent The assistance of the Metro Police played a major role in the junctions, build-up of queues, delays, traffic conflicts, flows’ fact that the interviews were only conducted in the morning distribution, interaction between vehicles and pedestrians peak between 7h30 and around 11h30, as their assistance and, importantly to this project, the degree of efficiency of was crucial and their availability limited. Some of the traffic BRT at grade junctions along Morogoro Road. police officers only helped for 2 hours, indicating that they did their share or had to leave. Some of the officers were The modelling exercise will be subject to a set of not Metro police officers with proper authority to pull over assumptions on city-wide travel demand patterns that MIC vehicles, which is some cases were a challenge to pull over plans on establishing on the basis of planning framework vehicles as the citizens do not recognise the authority. documents which will be acquired throughout Phase 1 and With the officers not able to use their own transport the foreseeable interactions with the relevant stakeholders. (and refusing to use the BRT) we were responsible for all Outstanding pieces of information will be filled with their transport arrangements. In general significant physical appropriate sensitivity testing on the relevant variables. The constraints were encountered. However, the response rate, assessment shall include educated assumptions on future in particular for the road side surveys was pretty low, with an private and public transport infrastructure alternatives, average from 5 to 10 interviews per hour. integrated with the master plan land use quantities, which will be articulated in coordination with the master planner. Transit Passengers Survey A number of contributing factors played a role in the scheduled time for the BRT interviews, of which fact that a lot of surveyors needed to be moved from the traffic survey locations (which are areas they reside in) to new locations to even out the number of surveyors at each station, making it quite a logistical challenge. With the roadside interviews’ lessons learned (based on the logistical challenges) the time frame for the BRT surveys were 6 AM to around 11:30 AM. 81 Appendix A / Part A1 - Survey Methodologies & Results A1.3 Results and Analysis Traffic Surveys Introduction and Survey Details Traffic volume surveys were conducted to determine the number, movements, and classifications of roadway vehicles at a given location. This data can help in identifying critical flow time periods, determine the impact of vehicular traffic flow on the nodes, and the traffic volume daily trends. Below are the details of some logistics of the conducted surveys. Type of Traffic Counts Manual Counts Conducted on 3 weedays and 1 weekend: Monday (06.02.2017), Wednesday (08.02.2017), Friday (10.02.2017) & Saturday (11.02.2017) Date & Time Intervals 06:00 - 09:00 (AM) & 15:00 - 18:00 (PM) Counts are made at 15 minutes interval Kimara Terminal Morogoro Road & Nelson Mandela Road Morogoro Road & Kagera Street Survey Locations Morogoro Road & Kawawa Road Morogoro Road & Bibi Titi Mohammed Street Bagamoyo Road & Kawawa Road 8 people per intersection (2 people per leg) Man Power Total 6 locations - 48 people Each person 2 shifts for 4 days Table A1.3-4  Traffic Survey Details Appendix A / Part A1 82 A1.3 Methodology - Traffic Data Collection Classification of Vehicles Aurecon proposed surveys to be conducted by using Vehicles were categorized in two types during the survey: electronic handheld electronic devices. Information was Light Vehicle (LV): these are mainly cars and taxis. initially supposed to be captured on devices (tablets) able Heavy Vehicle (HV): these are composed of trucks to synchronise the data to cloud-based servers via cellular (including daladala and large buses). network. Data would then be captured and uploaded to a Microsoft SQL data warehouse after which real-time collation Conversion of Vehicles and data screening would be performed. This step would Passenger Car Unit (PCU) is a metric used in Traffic merge the data streams and ultimately enable the seamless Engineering, to assess traffic-flow rate on the network. To transition of data into the survey database. better comprehend the traffic volumes, heavy vehicles are Due to unexpected customs restrictions at Dar es Salaam’s needed to be converted into light vehicles. PCU factor of airport the use of electronic devices has been exempted. 2.5 is adopted for the current survey counts to convert heavy Hence, traffic data were recorded manually on a hard copy vehicles to light vehicles. (Heavy vehicles are composed of of survey forms for which surveyors were recruited and they trucks including Daladala and large buses) When compared were given intensive training & field demonstration one day with Transport Analysis Guidance (TAG) UNIT A5.4 (UK), PCU prior to the actual survey. 2.5 represents a good average between light goods vehicles Hence, surveys forms were provided to the team on the site and rigid goods vehicles. to conduct manual counts. At each entry leg of the junction, two surveyors counted the number of vehicles entering the junction turn by movement and classified in 15 minutes intervals to obtain a profiling of the flows within the peak. These surveys were conducted on three weekdays Vehicle Type PCU Factor and one weekend day in February 2017 at six different Car 1.0 locations along Morogoro Road in the morning Light Goods Vehicle 1.0 (06:00-09:00) and in the evening (15:00-18:00). Rigid Goods Vehicle 1.9 Articulated Goods Vehicle 2.9 Public Service Vehicle 2.5 Table A1.3-5  Transport Analysis Guidance (TAG) UNIT A5.4 (UK) 83 Appendix A / Part A1 - Survey Methodologies & Results A1.3 Results and Observations Peak Hour Analysis The traffic count survey was conducted on three weekdays As shown in the figure, the highest traffic volumes are and one weekend day, from 6:00 am until 9:00 am and from observed between 7:00 and 8:00 in the morning. The 15:00 until 18:00 pm. For all the six locations, the surveys afternoon peak is observed between 16:45 and 17:45 with were performed for each 15 minutes interval and traffic data very similar values until 18:00. Accordingly, in order to was collected for both light vehicles and heavy vehicles for simplify the following elaborations the interval from 17:00 to all different manoeuvres. Summing up the traffic counts for 18:00 pm is assumed as evening peak hour for the corridor. all manoeuvres for each hour at all six location, it is possible The following pages outline the data collected by location. to understand and establish the morning peak hour within the study area. Morning Peak Hour 120000 110000 100000 90000 80000 70000 60000 50000 40000 30000 20000 10000 0 6:00-7:00 6:15-7:15 6:30-7:30 6:45-7:45 7:00-8:00 7:15-8:15 7:30-8:30 7:45-8:45 8:00-9:00 Monday (06/02/2017) 24869 26126 26818 26955 27569 27932 27942 28182 27905 Wednesday (08/02/2017) 29978 31554 31689 31913 30769 29943 29905 29502 30323 Friday (10/02/2017) 29347 30738 31385 31821 32055 30451 29031 28161 26959 Saturday (11/02/2017) 16702 16980 17296 17892 18458 18986 19463 20290 21126 Figure A1.3-7  PCU Traffic Flow per Days, per Morning Hours Appendix A / Part A1 84 A1.3 Figure A1.3-8  Aerial View of the BRT Corridor Evening Peak Hour 120000 110000 100000 90000 80000 70000 60000 50000 40000 30000 20000 10000 0 15:00-16.00 15.15-16:15 15:30-16:30 15:45-16:45 16:00-17:00 16:15-17:15 16:30-17:30 16:45-17:45 17:00-18:00 Monday (06/02/2017) 30631 31820 32042 32674 33825 33409 33676 33205 32708 Wednesday (08/02/2017) 33792 31400 31128 31105 31068 31434 31880 32599 32464 Friday (10/02/2017) 26661 27290 28011 27977 28972 29222 29136 29335 28696 Saturday (11/02/2017) 23272 23404 23652 24063 24034 24968 25127 25106 25336 Figure A1.3-9  PCU Traffic Flow per Days, per Evening Hours 85 Appendix A / Part A1 - Survey Methodologies & Results A1.3 Peak Hour Analysis - Location 1 Kimara Terminal On an average weekday AM peak hour, high traffic flows (3,697 veh/hr) are observed between 6:00 to 7:00 am and for PM peak hour, high traffic flows (3,273 veh/hr) are between 17:00 to 18:00 pm. On a weekend AM peak hour, high traffic flows (3,006 veh/hr) are observed between 7:45 to 8:45 am and for PM peak hour, high traffic flows (3,420 veh/hr) are between 16:45 to 17:45 pm. Kimara Terminal 06/02/2017 - AM Kimara Terminal 08/02/2017 - AM 12000 12000 10500 10500 9000 9000 7500 7500 6000 6000 4500 4500 3000 3000 1500 1500 0 0 6:00- 6:15- 6:30- 6:45- 7:00- 7:15- 7:30- 7:45- 8:00- 6:00- 6:15- 6:30- 6:45- 7:00- 7:15- 7:30- 7:45- 8:00- 7:00 7:15 7:30 7:45 8:00 8:15 8:30 8:45 9:00 7:00 7:15 7:30 7:45 8:00 8:15 8:30 8:45 9:00 3469 3276 3093 3225 3213 3267 3250 3199 3254 3921 4052 4056 3877 3780 3718 3664 3546 3440 Kimara Terminal 10/02/2017 - AM Kimara Terminal 11/02/2017 - AM 12000 12000 10500 10500 9000 9000 7500 7500 6000 6000 4500 4500 3000 3000 1500 1500 0 0 6:00- 6:15- 6:30- 6:45- 7:00- 7:15- 7:30- 7:45- 8:00- 6:00- 6:15- 6:30- 6:45- 7:00- 7:15- 7:30- 7:45- 8:00- 7:00 7:15 7:30 7:45 8:00 8:15 8:30 8:45 9:00 7:00 7:15 7:30 7:45 8:00 8:15 8:30 8:45 9:00 3700 3699 3615 3677 3515 3418 2978 2871 2824 2717 2648 2499 2592 2694 2831 2943 3066 3009 Figure A1.3-10  Location 1 PCU Traffic Flow per Days, per Morning Hours Kimara Terminal 06/02/2017 - PM Kimara Terminal 08/02/2017 - PM 12000 12000 10500 10500 Appendix A / Part A1 86 Kimara Terminal 06/02/2017 - AM Kimara Terminal 08/02/2017 - AM A1.3 12000 12000 10500 10500 9000 9000 7500 7500 6000 6000 4500 4500 3000 3000 1500 1500 0 0 6:00- 6:15- 6:30- 6:45- 7:00- 7:15- 7:30- 7:45- 8:00- 6:00- 6:15- 6:30- 6:45- 7:00- 7:15- 7:30- 7:45- 8:00- 7:00 7:15 7:30 7:45 8:00 8:15 8:30 8:45 9:00 7:00 7:15 7:30 7:45 8:00 8:15 8:30 8:45 9:00 3469 3276 3093 3225 3213 3267 3250 3199 3254 3921 4052 4056 3877 3780 3718 3664 3546 3440 Kimara Terminal 10/02/2017 - AM Kimara Terminal 11/02/2017 - AM 12000 12000 10500 10500 9000 9000 7500 7500 6000 6000 4500 4500 3000 3000 1500 1500 0 0 6:00- 6:15- 6:30- 6:45- 7:00- 7:15- 7:30- 7:45- 8:00- 6:00- 6:15- 6:30- 6:45- 7:00- 7:15- 7:30- 7:45- 8:00- 7:00 Figure Aerial 7:45 7:15 7:30 A1.3-11  8:00 View of 8:15 1 8:30 8:45 9:00 Location 7:00 7:15 7:30 7:45 8:00 8:15 8:30 8:45 9:00 3700 3699 3615 3677 3515 3418 2978 2871 2824 2717 2648 2499 2592 2694 2831 2943 3066 3009 Kimara Terminal 06/02/2017 - PM Kimara Terminal 08/02/2017 - PM 12000 12000 10500 10500 9000 9000 7500 7500 6000 6000 4500 4500 3000 3000 1500 1500 0 0 15:00- 15.15- 15:30- 15:45- 16:00- 16:15- 16:30- 16:45- 17:00- 15:00- 15.15- 15:30- 15:45- 16:00- 16:15- 16:30- 16:45- 17:00- 16.00 16:15 16:30 16:45 17:00 17:15 17:30 17:45 18:00 16.00 16:15 16:30 16:45 17:00 17:15 17:30 17:45 18:00 2787 2872 2968 3077 3085 3104 3230 3168 3201 2849 2813 2757 2799 2817 2902 3046 3252 3348 Kimara Terminal 10/02/2017 - PM Kimara Terminal 11/02/2017 - PM 12000 12000 10500 10500 9000 9000 7500 7500 6000 6000 4500 4500 3000 3000 1500 1500 0 0 15:00- 15.15- 15:30- 15:45- 16:00- 16:15- 16:30- 16:45- 17:00- 15:00- 15.15- 15:30- 15:45- 16:00- 16:15- 16:30- 16:45- 17:00- 16.00 16:15 16:30 16:45 17:00 17:15 17:30 17:45 18:00 16.00 16:15 16:30 16:45 17:00 17:15 17:30 17:45 18:00 2978 2719 2881 2856 3012 3189 3286 3349 3271 3022 3140 3180 3087 3160 3297 3373 3420 3343 Figure A1.3-12  Location 1 PCU Traffic Flow per Days, per Evening Hours 87 Appendix A / Part A1 - Survey Methodologies & Results A1.3 Peak Hour Analysis - Location 2 Morogoro Road & Nelson Mandela Road On an average weekday AM peak hour, high traffic flows (19,408 veh/hr) are observed between 7:00 to 8:00 am and for PM peak hour, high traffic flows (19,209 veh/hr) are between 17:00 to 18:00 pm. On a weekend AM peak hour, high traffic flows (4,985 veh/hr) are observed between 8:00 to 9:00 am and for PM peak hour, high traffic flows (5,435 veh/hr) are between 17:00 to 18:00 pm. Morogoro & Nelson Mandela Road Morogoro & Nelson Mandela Road 06/02/2017 - AM 08/02/2017 - AM 12000 12000 10500 10500 9000 9000 7500 7500 6000 6000 4500 4500 3000 3000 1500 1500 0 0 6:00- 6:15- 6:30- 6:45- 7:00- 7:15- 7:30- 7:45- 8:00- 6:00- 6:15- 6:30- 6:45- 7:00- 7:15- 7:30- 7:45- 8:00- 7:00 7:15 7:30 7:45 8:00 8:15 8:30 8:45 9:00 7:00 7:15 7:30 7:45 8:00 8:15 8:30 8:45 9:00 5672 5496 5259 5154 5462 5674 5734 5797 5372 6481 6932 7001 6861 7270 6876 6387 6491 6548 Morogoro & Nelson Mandela Road Morogoro & Nelson Mandela Road 10/02/2017 - AM 11/02/2017 - AM 12000 12000 10500 10500 9000 9000 7500 7500 6000 6000 4500 4500 3000 3000 1500 1500 0 0 6:00- 6:15- 6:30- 6:45- 7:00- 7:15- 7:30- 7:45- 8:00- 6:00- 6:15- 6:30- 6:45- 7:00- 7:15- 7:30- 7:45- 8:00- 7:00 7:15 7:30 7:45 8:00 8:15 8:30 8:45 9:00 7:00 7:15 7:30 7:45 8:00 8:15 8:30 8:45 9:00 6622 6566 6630 6450 6317 6059 5976 5962 5726 4605 4320 4340 4247 4158 4294 4291 4555 4985 Figure A1.3-13  Location 2 PCU Traffic Flow per Days, per Morning Hours Morogoro & Nelson Mandela Road Morogoro & Nelson Mandela Road 06/02/2017 - PM 08/02/2017 - PM 12000 12000 Appendix A / Part A1 88 Morogoro & Nelson Mandela Road Morogoro & Nelson Mandela Road 06/02/2017 - AM 08/02/2017 - AM 12000 10500 12000 10500 A1.3 9000 9000 7500 7500 6000 6000 4500 4500 3000 3000 1500 1500 0 0 6:00- 6:15- 6:30- 6:45- 7:00- 7:15- 7:30- 7:45- 8:00- 6:00- 6:15- 6:30- 6:45- 7:00- 7:15- 7:30- 7:45- 8:00- 7:00 7:15 7:30 7:45 8:00 8:15 8:30 8:45 9:00 7:00 7:15 7:30 7:45 8:00 8:15 8:30 8:45 9:00 5672 5496 5259 5154 5462 5674 5734 5797 5372 6481 6932 7001 6861 7270 6876 6387 6491 6548 Morogoro & Nelson Mandela Road Morogoro & Nelson Mandela Road 10/02/2017 - AM 11/02/2017 - AM 12000 12000 10500 10500 9000 9000 7500 7500 6000 6000 4500 4500 3000 3000 1500 1500 0 0 6:00- 6:15- 6:30- 6:45- 7:00- 7:15- 7:30- 7:45- 8:00- 6:00- 6:15- 6:30- 6:45- 7:00- 7:15- 7:30- 7:45- 8:00- 7:00 7:15 7:30 7:45 8:00 8:15 8:30 8:45 9:00 7:00 7:15 7:30 7:45 8:00 8:15 8:30 8:45 9:00 Figure A1.3-14  Aerial View of Location 2 6622 6566 6630 6450 6317 6059 5976 5962 5726 4605 4320 4340 4247 4158 4294 4291 4555 4985 Morogoro & Nelson Mandela Road Morogoro & Nelson Mandela Road 06/02/2017 - PM 08/02/2017 - PM 12000 12000 10500 10500 9000 9000 7500 7500 6000 6000 4500 4500 3000 3000 1500 1500 0 0 15:00- 15.15- 15:30- 15:45- 16:00- 16:15- 16:30- 16:45- 17:00- 15:00- 15.15- 15:30- 15:45- 16:00- 16:15- 16:30- 16:45- 17:00- 16.00 16:15 16:30 16:45 17:00 17:15 17:30 17:45 18:00 16.00 16:15 16:30 16:45 17:00 17:15 17:30 17:45 18:00 6803 6594 6105 6285 6156 6716 6740 6509 6613 5839 5620 5565 5293 5744 5808 6123 6393 6399 Morogoro & Nelson Mandela Road Morogoro & Nelson Mandela Road 10/02/2017 - PM 11/02/2017 - PM 12000 12000 10500 10500 9000 9000 7500 7500 6000 6000 4500 4500 3000 3000 1500 1500 0 0 15:00- 15.15- 15:30- 15:45- 16:00- 16:15- 16:30- 16:45- 17:00- 15:00- 15.15- 15:30- 15:45- 16:00- 16:15- 16:30- 16:45- 17:00- 16.00 16:15 16:30 16:45 17:00 17:15 17:30 17:45 18:00 16.00 16:15 16:30 16:45 17:00 17:15 17:30 17:45 18:00 5090 5332 5460 5557 5697 5953 6044 6307 6120 4921 4919 4945 5372 5316 5364 5423 5248 5435 Figure A1.3-15  Location 2 PCU Traffic Flow per Days, per Evening Hours 89 Appendix A / Part A1 - Survey Methodologies & Results A1.3 Peak Hour Analysis - Location 3 Morogoro Road & Kagera Street On an average weekday AM peak hour, high traffic flows (11,027 veh/hr) are observed between 6:45 to 7:45 am and for PM peak hour, high traffic flows (12,007 veh/hr) are between 17:00 to 18:00 pm. On a weekend AM peak hour, high traffic flows (2,309 veh/hr) are observed between 6:00 to 7:00 am and for PM peak hour, high traffic flows (3,224 veh/hr) are between 15:00 to 16:00 pm. Morogoro Road & Kagera Street Morogoro Road & Kagera Street 06/02/2017 - AM 08/02/2017 - AM 12000 12000 10500 10500 9000 9000 7500 7500 6000 6000 4500 4500 3000 3000 1500 1500 0 0 6:00- 6:15- 6:30- 6:45- 7:00- 7:15- 7:30- 7:45- 8:00- 6:00- 6:15- 6:30- 6:45- 7:00- 7:15- 7:30- 7:45- 8:00- 7:00 7:15 7:30 7:45 8:00 8:15 8:30 8:45 9:00 7:00 7:15 7:30 7:45 8:00 8:15 8:30 8:45 9:00 2853 2715 3127 3450 3696 3658 3741 3650 3514 3380 3675 3485 3581 3474 3431 3468 3377 3576 Morogoro Road & Kagera Street Morogoro Road & Kagera Street 10/02/2017 - AM 11/02/2017 - AM 12000 12000 10500 10500 9000 9000 7500 7500 6000 6000 4500 4500 3000 3000 1500 1500 0 0 6:00- 6:15- 6:30- 6:45- 7:00- 7:15- 7:30- 7:45- 8:00- 6:00- 6:15- 6:30- 6:45- 7:00- 7:15- 7:30- 7:45- 8:00- 7:00 7:15 7:30 7:45 8:00 8:15 8:30 8:45 9:00 7:00 7:15 7:30 7:45 8:00 8:15 8:30 8:45 9:00 4068 4041 3950 3996 3699 3588 3578 3279 3160 2309 2234 2108 2187 2197 2154 2091 2016 2068 Figure A1.3-16  Location 3 PCU Traffic Flow per Days, per Morning Hours Morogoro Road & Kagera Street Morogoro Road & Kagera Street 06/02/2017 - PM 08/02/2017 - PM 12000 12000 Appendix A / Part A1 90 Morogoro Road & Kagera Street Morogoro Road & Kagera Street 06/02/2017 - AM 08/02/2017 - AM 12000 10500 12000 10500 A1.3 9000 9000 7500 7500 6000 6000 4500 4500 3000 3000 1500 1500 0 0 6:00- 6:15- 6:30- 6:45- 7:00- 7:15- 7:30- 7:45- 8:00- 6:00- 6:15- 6:30- 6:45- 7:00- 7:15- 7:30- 7:45- 8:00- 7:00 7:15 7:30 7:45 8:00 8:15 8:30 8:45 9:00 7:00 7:15 7:30 7:45 8:00 8:15 8:30 8:45 9:00 2853 2715 3127 3450 3696 3658 3741 3650 3514 3380 3675 3485 3581 3474 3431 3468 3377 3576 Morogoro Road & Kagera Street Morogoro Road & Kagera Street 10/02/2017 - AM 11/02/2017 - AM 12000 12000 10500 10500 9000 9000 7500 7500 6000 6000 4500 4500 3000 3000 1500 1500 0 0 6:00- 6:15- 6:30- 6:45- 7:00- 7:15- 7:30- 7:45- 8:00- 6:00- 6:15- 6:30- 6:45- 7:00- 7:15- 7:30- 7:45- 8:00- 7:00 7:15 7:30 7:45 8:00 8:15 8:30 8:45 9:00 7:00 7:15 7:30 7:45 8:00 8:15 8:30 8:45 9:00 Figure A1.3-17  Aerial View of Location 3 4068 4041 3950 3996 3699 3588 3578 3279 3160 2309 2234 2108 2187 2197 2154 2091 2016 2068 Morogoro Road & Kagera Street Morogoro Road & Kagera Street 06/02/2017 - PM 08/02/2017 - PM 12000 12000 10500 10500 9000 9000 7500 7500 6000 6000 4500 4500 3000 3000 1500 1500 0 0 15:00- 15.15- 15:30- 15:45- 16:00- 16:15- 16:30- 16:45- 17:00- 15:00- 15.15- 15:30- 15:45- 16:00- 16:15- 16:30- 16:45- 17:00- 16.00 16:15 16:30 16:45 17:00 17:15 17:30 17:45 18:00 16.00 16:15 16:30 16:45 17:00 17:15 17:30 17:45 18:00 3453 3767 4190 4369 4660 4426 4213 3999 3970 3423 3581 3764 3871 3845 3981 4080 4452 4615 Morogoro Road & Kagera Street Morogoro Road & Kagera Street 10/02/2017 - PM 11/02/2017 - PM 12000 12000 10500 10500 9000 9000 7500 7500 6000 6000 4500 4500 3000 3000 1500 1500 0 0 15:00- 15.15- 15:30- 15:45- 16:00- 16:15- 16:30- 16:45- 17:00- 15:00- 15.15- 15:30- 15:45- 16:00- 16:15- 16:30- 16:45- 17:00- 16.00 16:15 16:30 16:45 17:00 17:15 17:30 17:45 18:00 16.00 16:15 16:30 16:45 17:00 17:15 17:30 17:45 18:00 3070 3189 3159 3247 3418 3508 3584 3432 3423 3224 3099 3066 3097 2992 3051 3011 2936 2971 Figure A1.3-18  Location 3 PCU Traffic Flow per Days, per Evening Hours 91 Appendix A / Part A1 - Survey Methodologies & Results A1.3 Peak Hour Analysis - Location 4 Morogoro Road & Kawawa Road On an average weekday AM peak hour, high traffic flows (19,987 veh/hr) are observed between 6:45 to 7:45 am and for PM peak hour, high traffic flows (20,972 veh/hr) are between 16:00 to 17:00 pm. On a weekend AM peak hour, high traffic flows (4,714 veh/hr) are observed between 8:00 to 9:00 am and for PM peak hour, high traffic flows (5,677 veh/hr) are between 17:00 to 18:00 pm. Morogoro Road & Kawawa Road Morogoro Road & Kawawa Road 06/02/2017 - AM 08/02/2017 - AM 12000 12000 10500 10500 9000 9000 7500 7500 6000 6000 4500 4500 3000 3000 1500 1500 0 0 6:00- 6:15- 6:30- 6:45- 7:00- 7:15- 7:30- 7:45- 8:00- 6:00- 6:15- 6:30- 6:45- 7:00- 7:15- 7:30- 7:45- 8:00- 7:00 7:15 7:30 7:45 8:00 8:15 8:30 8:45 9:00 7:00 7:15 7:30 7:45 8:00 8:15 8:30 8:45 9:00 5202 6019 6538 6386 6624 6625 6469 6494 6346 5684 6042 6212 6574 6229 6199 6548 6431 6711 Morogoro Road & Kawawa Road Morogoro Road & Kawawa Road 10/02/2017 - AM 11/02/2017 - AM 12000 12000 10500 10500 9000 9000 7500 7500 6000 6000 4500 4500 3000 3000 1500 1500 0 0 6:00- 6:15- 6:30- 6:45- 7:00- 7:15- 7:30- 7:45- 8:00- 6:00- 6:15- 6:30- 6:45- 7:00- 7:15- 7:30- 7:45- 8:00- 7:00 7:15 7:30 7:45 8:00 8:15 8:30 8:45 9:00 7:00 7:15 7:30 7:45 8:00 8:15 8:30 8:45 9:00 5540 6236 6768 7027 7106 6457 6314 6045 5638 3247 3622 3813 3928 3952 4001 4326 4617 4714 Figure A1.3-19  Location 4 PCU Traffic Flow per Days, per Morning Hours Morogoro Road & Kawawa Road Morogoro Road & Kawawa Road 06/02/2017 - PM 08/02/2017 - PM 12000 12000 Appendix A / Part A1 92 Morogoro Road & Kawawa Road Morogoro Road & Kawawa Road 06/02/2017 - AM 08/02/2017 - AM 12000 10500 12000 10500 A1.3 9000 9000 7500 7500 6000 6000 4500 4500 3000 3000 1500 1500 0 0 6:00- 6:15- 6:30- 6:45- 7:00- 7:15- 7:30- 7:45- 8:00- 6:00- 6:15- 6:30- 6:45- 7:00- 7:15- 7:30- 7:45- 8:00- 7:00 7:15 7:30 7:45 8:00 8:15 8:30 8:45 9:00 7:00 7:15 7:30 7:45 8:00 8:15 8:30 8:45 9:00 5202 6019 6538 6386 6624 6625 6469 6494 6346 5684 6042 6212 6574 6229 6199 6548 6431 6711 Morogoro Road & Kawawa Road Morogoro Road & Kawawa Road 10/02/2017 - AM 11/02/2017 - AM 12000 12000 10500 10500 9000 9000 7500 7500 6000 6000 4500 4500 3000 3000 1500 1500 0 0 6:00- 6:15- 6:30- 6:45- 7:00- 7:15- 7:30- 7:45- 8:00- 6:00- 6:15- 6:30- 6:45- 7:00- 7:15- 7:30- 7:45- 8:00- 7:00 7:15 7:30 7:45 8:00 8:15 8:30 8:45 9:00 7:00 7:15 7:30 7:45 8:00 8:15 8:30 8:45 9:00 Figure A1.3-20  Aerial View of Location 4 5540 6236 6768 7027 7106 6457 6314 6045 5638 3247 3622 3813 3928 3952 4001 4326 4617 4714 Morogoro Road & Kawawa Road Morogoro Road & Kawawa Road 06/02/2017 - PM 08/02/2017 - PM 12000 12000 10500 10500 9000 9000 7500 7500 6000 6000 4500 4500 3000 3000 1500 1500 0 0 15:00- 15.15- 15:30- 15:45- 16:00- 16:15- 16:30- 16:45- 17:00- 15:00- 15.15- 15:30- 15:45- 16:00- 16:15- 16:30- 16:45- 17:00- 16.00 16:15 16:30 16:45 17:00 17:15 17:30 17:45 18:00 16.00 16:15 16:30 16:45 17:00 17:15 17:30 17:45 18:00 6156 6067 5877 6138 6315 6106 6266 6145 5903 6823 7077 7259 7490 7743 7643 7513 7238 7059 Morogoro Road & Kawawa Road Morogoro Road & Kawawa Road 10/02/2017 - PM 11/02/2017 - PM 12000 12000 10500 10500 9000 9000 7500 7500 6000 6000 4500 4500 3000 3000 1500 1500 0 0 15:00- 15.15- 15:30- 15:45- 16:00- 16:15- 16:30- 16:45- 17:00- 15:00- 15.15- 15:30- 15:45- 16:00- 16:15- 16:30- 16:45- 17:00- 16.00 16:15 16:30 16:45 17:00 17:15 17:30 17:45 18:00 16.00 16:15 16:30 16:45 17:00 17:15 17:30 17:45 18:00 6557 6606 6694 6648 6914 6998 6809 6757 6276 5197 5063 5152 5248 5268 5566 5526 5560 5677 Figure A1.3-21  Location 4 PCU Traffic Flow per Days, per Evening Hours 93 Appendix A / Part A1 - Survey Methodologies & Results A1.3 Peak Hour Analysis - Location 5 Morogoro Road & Bibi Titi Mohammed Street On an average weekday AM peak hour, high traffic flows (10,053 veh/hr) are observed between 7:00 to 8:00 am and for PM peak hour, high traffic flows (8,651 veh/hr) are between 16:30 to 17:30 pm. On a weekend AM peak hour, high traffic flows (2,221 veh/hr) are observed between 8:00 to 9:00 am and for PM peak hour, high traffic flows (2,590 veh/hr) are between 15:00 to 16:00 pm. Morogoro Road & Bibi Titi Morogoro Road & Bibi Titi Mohammed Street 06/02/2017 - AM Mohammed Street 08/02/2017 - AM 12000 12000 10500 10500 9000 9000 7500 7500 6000 6000 4500 4500 3000 3000 1500 1500 0 0 6:00- 6:15- 6:30- 6:45- 7:00- 7:15- 7:30- 7:45- 8:00- 6:00- 6:15- 6:30- 6:45- 7:00- 7:15- 7:30- 7:45- 8:00- 7:00 7:15 7:30 7:45 8:00 8:15 8:30 8:45 9:00 7:00 7:15 7:30 7:45 8:00 8:15 8:30 8:45 9:00 2888 3202 3289 3272 3102 3004 2802 2694 2754 2585 2794 3149 3383 3421 3220 3160 2976 2836 Morogoro Road & Bibi Titi Morogoro Road & Bibi Titi Mohammed Street 10/02/2017 - AM Mohammed Street 11/02/2017 - AM 12000 12000 10500 10500 9000 9000 7500 7500 6000 6000 4500 4500 3000 3000 1500 1500 0 0 6:00- 6:15- 6:30- 6:45- 7:00- 7:15- 7:30- 7:45- 8:00- 6:00- 6:15- 6:30- 6:45- 7:00- 7:15- 7:30- 7:45- 8:00- 7:00 7:15 7:30 7:45 8:00 8:15 8:30 8:45 9:00 7:00 7:15 7:30 7:45 8:00 8:15 8:30 8:45 9:00 2674 2999 3139 3306 3530 3202 3088 2887 2837 1017 1043 1216 1377 1524 1666 1780 1947 2221 Figure A1.3-22  Location 5 PCU Traffic Flow per Days, per Morning Hours Morogoro Road & Bibi Titi Morogoro Road & Bibi Titi Mohammed Street 06/02/2017 - PM Mohammed Street 08/02/2017 - PM 12000 12000 Appendix A / Part A1 94 Morogoro Road & Bibi Titi Morogoro Road & Bibi Titi Mohammed Street 06/02/2017 - AM Mohammed Street 08/02/2017 - AM 12000 10500 12000 10500 A1.3 9000 9000 7500 7500 6000 6000 4500 4500 3000 3000 1500 1500 0 0 6:00- 6:15- 6:30- 6:45- 7:00- 7:15- 7:30- 7:45- 8:00- 6:00- 6:15- 6:30- 6:45- 7:00- 7:15- 7:30- 7:45- 8:00- 7:00 7:15 7:30 7:45 8:00 8:15 8:30 8:45 9:00 7:00 7:15 7:30 7:45 8:00 8:15 8:30 8:45 9:00 2888 3202 3289 3272 3102 3004 2802 2694 2754 2585 2794 3149 3383 3421 3220 3160 2976 2836 Morogoro Road & Bibi Titi Morogoro Road & Bibi Titi Mohammed Street 10/02/2017 - AM Mohammed Street 11/02/2017 - AM 12000 12000 10500 10500 9000 9000 7500 7500 6000 6000 4500 4500 3000 3000 1500 1500 0 0 6:00- 6:15- 6:30- 6:45- 7:00- 7:15- 7:30- 7:45- 8:00- 6:00- 6:15- 6:30- 6:45- 7:00- 7:15- 7:30- 7:45- 8:00- 7:00 7:15 7:30 7:45 8:00 8:15 8:30 8:45 9:00 7:00 7:15 7:30 7:45 8:00 8:15 8:30 8:45 9:00 Figure A1.3-23  Aerial View of Location 5 2674 2999 3139 3306 3530 3202 3088 2887 2837 1017 1043 1216 1377 1524 1666 1780 1947 2221 Morogoro Road & Bibi Titi Morogoro Road & Bibi Titi Mohammed Street 06/02/2017 - PM Mohammed Street 08/02/2017 - PM 12000 12000 10500 10500 9000 9000 7500 7500 6000 6000 4500 4500 3000 3000 1500 1500 0 0 15:00- 15.15- 15:30- 15:45- 16:00- 16:15- 16:30- 16:45- 17:00- 15:00- 15.15- 15:30- 15:45- 16:00- 16:15- 16:30- 16:45- 17:00- 16.00 16:15 16:30 16:45 17:00 17:15 17:30 17:45 18:00 16.00 16:15 16:30 16:45 17:00 17:15 17:30 17:45 18:00 2722 2802 2831 2776 2636 2571 2723 2560 2548 2659 2766 2792 2818 2836 2902 2937 2773 2613 Morogoro Road & Bibi Titi Morogoro Road & Bibi Titi Mohammed Street 10/02/2017 - PM Mohammed Street 11/02/2017 - PM 12000 12000 10500 10500 9000 9000 7500 7500 6000 6000 4500 4500 3000 3000 1500 1500 0 0 15:00- 15.15- 15:30- 15:45- 16:00- 16:15- 16:30- 16:45- 17:00- 15:00- 15.15- 15:30- 15:45- 16:00- 16:15- 16:30- 16:45- 17:00- 16.00 16:15 16:30 16:45 17:00 17:15 17:30 17:45 18:00 16.00 16:15 16:30 16:45 17:00 17:15 17:30 17:45 18:00 2677 2798 2841 2875 2979 2962 2992 3081 3034 2590 2427 2365 2295 2236 2227 2140 2079 2160 Figure A1.3-24  Location 5 PCU Traffic Flow per Days, per Evening Hours 95 Appendix A / Part A1 - Survey Methodologies & Results A1.3 Peak Hour Analysis - Location 6 Bagamoyo Road & Kawawa Road On an average weekday AM peak hour, high traffic flows (20,677 veh/hr) are observed between 6:15 to 7:15 am and for PM peak hour, high traffic flows (27,201 veh/hr) are between 15:00 to 16:00 pm. On a weekend AM peak hour, high traffic flows (4,131 veh/hr) are observed between 8:00 to 9:00 am and for PM peak hour, high traffic flows (5,865 veh/hr) are between 16:45 to 17:45 pm. Bagamoyo Road & Kawawa Road Bagamoyo Road & Kawawa Road 06/02/2017 - AM 08/02/2017 - AM 12000 12000 10500 10500 9000 9000 7500 7500 6000 6000 4500 4500 3000 3000 1500 1500 0 0 6:00- 6:15- 6:30- 6:45- 7:00- 7:15- 7:30- 7:45- 8:00- 6:00- 6:15- 6:30- 6:45- 7:00- 7:15- 7:30- 7:45- 8:00- 7:00 7:15 7:30 7:45 8:00 8:15 8:30 8:45 9:00 7:00 7:15 7:30 7:45 8:00 8:15 8:30 8:45 9:00 4786 5420 5513 5469 5474 5706 5946 6349 6666 7928 8060 7786 7638 6597 6501 6678 6681 7213 Bagamoyo Road & Kawawa Road Bagamoyo Road & Kawawa Road 10/02/2017 - AM 11/02/2017 - AM 12000 12000 10500 10500 9000 9000 7500 7500 6000 6000 4500 4500 3000 3000 1500 1500 0 0 6:00- 6:15- 6:30- 6:45- 7:00- 7:15- 7:30- 7:45- 8:00- 6:00- 6:15- 6:30- 6:45- 7:00- 7:15- 7:30- 7:45- 8:00- 7:00 7:15 7:30 7:45 8:00 8:15 8:30 8:45 9:00 7:00 7:15 7:30 7:45 8:00 8:15 8:30 8:45 9:00 6743 7198 7284 7365 7889 7727 7098 7119 6775 2808 3114 3321 3563 3934 4041 4033 4091 4131 Figure A1.3-25  Location 6 PCU Traffic Flow per Days, per Morning Hours Bagamoyo Road & Kawawa Road Bagamoyo Road & Kawawa Road 06/02/2017 - PM 08/02/2017 - PM 12000 12000 Appendix A / Part A1 96 Bagamoyo Road & Kawawa Road Bagamoyo Road & Kawawa Road 06/02/2017 - AM 08/02/2017 - AM 12000 10500 12000 10500 A1.3 9000 9000 7500 7500 6000 6000 4500 4500 3000 3000 1500 1500 0 0 6:00- 6:15- 6:30- 6:45- 7:00- 7:15- 7:30- 7:45- 8:00- 6:00- 6:15- 6:30- 6:45- 7:00- 7:15- 7:30- 7:45- 8:00- 7:00 7:15 7:30 7:45 8:00 8:15 8:30 8:45 9:00 7:00 7:15 7:30 7:45 8:00 8:15 8:30 8:45 9:00 4786 5420 5513 5469 5474 5706 5946 6349 6666 7928 8060 7786 7638 6597 6501 6678 6681 7213 Bagamoyo Road & Kawawa Road Bagamoyo Road & Kawawa Road 10/02/2017 - AM 11/02/2017 - AM 12000 12000 10500 10500 9000 9000 7500 7500 6000 6000 4500 4500 3000 3000 1500 1500 0 0 6:00- 6:15- 6:30- 6:45- 7:00- 7:15- 7:30- 7:45- 8:00- 6:00- 6:15- 6:30- 6:45- 7:00- 7:15- 7:30- 7:45- 8:00- 7:00 7:15 7:30 7:45 8:00 8:15 8:30 8:45 9:00 7:00 7:15 7:30 7:45 8:00 8:15 8:30 8:45 9:00 Figure A1.3-26  Aerial View of Location 6 6743 7198 7284 7365 7889 7727 7098 7119 6775 2808 3114 3321 3563 3934 4041 4033 4091 4131 Bagamoyo Road & Kawawa Road Bagamoyo Road & Kawawa Road 06/02/2017 - PM 08/02/2017 - PM 12000 12000 10500 10500 9000 9000 7500 7500 6000 6000 4500 4500 3000 3000 1500 1500 0 0 15:00- 15.15- 15:30- 15:45- 16:00- 16:15- 16:30- 16:45- 17:00- 15:00- 15.15- 15:30- 15:45- 16:00- 16:15- 16:30- 16:45- 17:00- 16.00 16:15 16:30 16:45 17:00 17:15 17:30 17:45 18:00 16.00 16:15 16:30 16:45 17:00 17:15 17:30 17:45 18:00 8710 9720 10072 10031 10973 10488 10505 10826 10474 12200 9544 8992 8835 8084 8199 8182 8492 8431 Bagamoyo Road & Kawawa Road Bagamoyo Road & Kawawa Road 10/02/2017 - PM 11/02/2017 - PM 12000 12000 10500 10500 9000 9000 7500 7500 6000 6000 4500 4500 3000 3000 1500 1500 0 0 15:00- 15.15- 15:30- 15:45- 16:00- 16:15- 16:30- 16:45- 17:00- 15:00- 15.15- 15:30- 15:45- 16:00- 16:15- 16:30- 16:45- 17:00- 16.00 16:15 16:30 16:45 17:00 17:15 17:30 17:45 18:00 16.00 16:15 16:30 16:45 17:00 17:15 17:30 17:45 18:00 6291 6647 6977 6795 6953 6613 6423 6410 6573 4319 4758 4945 4965 5063 5464 5656 5865 5751 Figure A1.3-27  Location 6 PCU Traffic Flow per Days, per Evening Hours 97 Appendix A / Part A1 - Survey Methodologies & Results A1.3 Traffic Flows Morogoro Road - Weekday Traffic counts are represented in a schematic way showing During AM peak hour, traffic flows entering the all the manoeuvres at all six surveyed locations. Concerning BRT Phase-1 Corridor from west to east show high volumes the weekday AM & PM peak hour, the average traffic and less traffic volume leaving from east to west. This volume for three weekdays (Monday/Wednesday/Friday) indicates that in the morning the prevailing traffic runs is considered. The diagrams shown below represent the eastbound toward the City Center. West-East and North-South bound traffic flows at the Traffic entering the corridor at the beginning of the section at surveyed junctions on Morogoro road. In particular, these Kimara are 2,216 vehicles/hour, in the middle of the section observations show the traffic flow trends during AM and PM at Magomeni are 2,752 vehicles/hour and in between these peak hours in an average weekday. two sections at Ubungo are 1,160 vehicles/hour. Figure A1.3-28  Traffic Flows Weekday AM Appendix A / Part A1 98 A1.3 Whereas, traffic leaving the section from west at the Traffic leaving the section from west at the beginning of the beginning of the section Bibi Titi are 718 vehicles/hour, at section Bibi Titi are 531 vehicles/hour, at the middle of the the middle of the section Ubungo are 1,170 vehicle/hour and section Ubungo are 1,853 vehicle/hour and leaving section leaving section Kimara are 1,287 vehicles/hour. Kimara are 1,543 vehicles/hour. Whereas, traffic entering During PM peak hour, westbound traffic the corridor, at the beginning of the section Kimara are (leaving the City Center) is prevailing on the entering traffic 1,186 vehicles/hour, in the middle of the section Magomeni flows. This indicates that during the evening peak hour, most are 737 vehicles/hour and in between these two sections at of the traffic is leaving from east to west out of the corridor Ubungo are 646 vehicles/hour. towards Kimara. Figure A1.3-29  Traffic Flows Weekday PM 99 Appendix A / Part A1 - Survey Methodologies & Results A1.3 Traffic Flows Morogoro Road - Weekend Looking at the weekend day, the surveyed flows are Whereas, traffic leaving the section from west at the comparatively lower than the ones observed during the beginning of the section Bibi Titi are 673 vehicles/hour, at weekday. In any case, also over the weekend during AM peak the middle of the section Ubungo are 1,258 vehicles/hour hour, traffic flows are mostly entering the Morogoro road and leaving section Kimara are 1,262 vehicles/hour. from West to East. During the evening peak hour, most of the traffic is leaving Traffic entering the corridor at the beginning of the section at from east (City Center) to west out of the corridor towards Kimara are 1,748 vehicles/hour, at the middle of the section Kimara. Traffic leaving the section from west at the Magomeni are 1,352 vehicles/hour and in between these two beginning of the section Bibi Titi are 403 vehicles/hour, at sections at Ubungo are 1,036 vehicles/hour. the middle of the section Ubungo are 1,035 vehicles/hour and leaving section Kimara are 1,329 vehicles/hour. Figure A1.3-30  Traffic Flows Weekend AM Appendix A / Part A1 100 A1.3 Whereas, traffic entering the corridor at the beginning of the section Kimara are 1,110 vehicles/hour are entering the corridor, at the middle of the section Magomeni are 1,234 vehicles/hour and in between these two sections at Ubungo are 911 vehicles/hour. Figure A1.3-31  Traffic Flows Weekend PM 101 Appendix A / Part A1 - Survey Methodologies & Results A1.3 Traffic Flows Composition Light & Heavy Vehicles - Morogoro Road The figures represent in a schematic way a comparison vehicle traffic flows are higher during the evening peak when between light and heavy vehicles during weekday and compared with the morning peak. weekend day for AM & PM peak hours, expressed in Similarly, on a weekend day AM peak hour, the average share percentages. During the weekday AM peak hour, the average of light and heavy vehicles at the surveyed six locations are share of light and heavy vehicles at the surveyed locations 80% and 20% simultaneously and during PM peak hour, the are namely 82% and 18%. During PM peak hour, the heavy share for heavy vehicles increases to 25%. vehicles share rises up to 25%. It is evident that heavy AM PM Weekdays Vehicles 19,373 24,488 PM Total 44% AM Total 56% Figure A1.3-32  Light and Heavy Vehicles Traffic Flows Weekday Average Appendix A / Part A1 102 A1.3 Traffic Flows Individual Junctions In the following pages traffic counts are represented in a schematic way showing all the manoeuvres at the six surveyed locations individually. For each surveyed location, traffic volumes (in PCU units) are shown for morning (7:00 to 8:00 am) and evening (17:00 to 18:00 pm) peak hour for average weekdays and weekend day. This schematic representation helps in understanding the pattern of traffic flows during different peak hours of different days at each individual location for each specific manoeuvre. AM PM Weekdays Vehicles 14,183 16,306 PM Total 47% AM Total 53% Figure A1.3-33  Light and Heavy Vehicles Traffic Flows Weekend 103 Appendix A / Part A1 - Survey Methodologies & Results A1.3 Location 1 Kimara Terminal No. of movements: 2 On weekday AM peak, entering traffic flows are higher (K-2,216 veh/hr) compared with traffic leaving (E-1,287 veh/hr). On contrary for PM peak, entering traffic (K-1,186 veh/hr) are less compared with traffic leaving (E-1,543 veh/hr). On weekend day, traffic flows are comparatively lower than the weekday. For AM peak, entering traffic flows are higher (K-1,748 veh/hr) compared with traffic leaving (E-1,262 veh/hr). On contrary for PM peak, entering traffic (K-1,110 veh/hr) are less compared with traffic leaving (E-1,329 veh/hr). Figure A1.3-34  Satellite View of Location 1 Weekday 7am - 8am 5pm - 6pm K 2216 1186 Weekend 8am - 9am 5pm - 6pm K 1748 1110 Weekday 7am - 8am 5pm - 6pm E 1287 1543 Weekend 8am - 9am 5pm - 6pm E 1262 1329 Figure A1.3-35  Schematic Representation of Traffic Flows at Location 1 Appendix A / Part A1 104 A1.3 Location 2 Morogoro Road & Nelson Mandela Road No. of movements: 12 On an average weekday, AM peak inbound flows at the junction are 3,502 veh/hr and PM peak inbound flows are 3,273 veh/hr. On a weekend day, AM peak inbound flows at the junction are 2,694 veh/hr and PM peak inbound flows are 3,343 veh/hr. On weekday AM peak, entering traffic flows are higher compared with traffic leaving. On the contrary, in the PM peak, the situation is the opposite. This would mean that part of the inbound flows are diverted on to Nelson Mandela road and Sam Nujoma road representing currently one of the main arterial roads of the city. On weekend day, traffic flows are comparatively lower than Figure A1.3-36  Satellite View of Location 2 the weekday. For AM peak, entering traffic flows are less compared with traffic leaving. Also for Weekday 7am - 8am 5pm - 6pm PM peak, entering traffic are less compared A 354 511 with traffic leaving. B 719 679 C 174 108 Weekend 8am - 9am 5pm - 6pm A 335 271 Weekday 7am - 8am 5pm - 6pm B 634 520 L 730 285 C 118 137 K 788 338 J 867 750 Weekend 8am - 9am 5pm - 6pm L 448 300 K 707 599 J 622 438 Weekday 7am - 8am 5pm - 6pm D 434 367 Weekday 7am - 8am 5pm - 6pm E 493 954 I 323 388 F 188 201 H 1081 571 Weekend 8am - 9am 5pm - 6pm G 198 200 D 220 479 Weekend 8am - 9am 5pm - 6pm E 536 493 I 387 271 F 259 157 H 510 342 G 211 175 Figure A1.3-37  Schematic Representation of Traffic Flows at Location 2 105 Appendix A / Part A1 - Survey Methodologies & Results A1.3 Location 3 Morogoro Road & Kagera Street No. of movements: 6 On an average weekday, AM peak inbound flows at the junction are 19,048 veh/hr and PM peak inbound flows are 19,131 veh/hr. On a weekend day, AM peak inbound flows at the junction are 4,158 veh/hr and PM peak inbound flows are 5,435 veh/hr. On weekday AM peak, entering traffic flows are higher compared with traffic leaving. Also for PM peak, entering traffic are higher compared with the outbound traffic. On weekend day, traffic flows are comparatively lower than on a weekday. For AM peak, inbound traffic flows are higher compared to Figure A1.3-38  Satellite View of Location 3 the outbound traffic. On the contrary, for PM peak, entering traffic is less compared to the traffic exiting the junction. Weekday 7am - 8am 5pm - 6 pm C 288 514 Weekend 8am - 9am 5pm - 6 pm C 317 339 Weekday 7am - 8am 5pm - 6pm Weekday 7am - 8am 5pm - 6pm L 474 346 E 649 1230 K 1989 898 F 95 220 Weekend 8am - 9am 5pm - 6pm Weekend 8am - 9am 5pm - 6pm L 214 202 E 632 1055 K 750 476 F 91 125 Weekday 7am - 8am 5pm - 6pm I 129 128 Weekend 8am - 9am 5pm - 6pm I 66 45 Figure A1.3-39  Schematic Representation of Traffic Flows at Location 3 Appendix A / Part A1 106 A1.3 Location 4 Morogoro Road & Kawawa Road No. of movements: 8 On an average weekday, AM peak inbound flows at the junction are 10,869 veh/hr and PM peak inbound flows are 12,007 veh/hr. On a weekend day, AM peak inbound flows at the junction are 2,197 veh/hr and PM peak inbound flows are 2,971 veh/hr. On weekday AM peak, entering traffic flows are higher compared with traffic leaving. On contrary, for PM peak, entering traffic are less compared with traffic leaving. On weekend day, traffic flows are comparatively lower than the weekday. For AM peak, entering traffic flows are higher compared with traffic leaving. On contrary, for PM peak, entering traffic are less compared with traffic leaving. Figure A1.3-41  Satellite View of Location 4 Weekday 7am - 8am 5pm - 6pm B 1597 1079 C 467 202 Weekend 8am - 9am 5pm - 6pm B 1604 1062 C 265 249 Weekday 7am - 8am 5pm - 6pm L 93 196 K 2285 535 Weekend 8am - 9am 5pm - 6pm L 125 203 K 1087 377 Weekday 7am - 8am 5pm - 6pm E 649 1230 Weekday 7am - 8am 5pm - 6pm F 53 76 I 97 120 Weekend 8am - 9am 5pm - 6pm H 1304 1104 E 512 1234 Weekend 8am - 9am 5pm - 6pm F 59 82 I 49 70 H 1014 885 Figure A1.3-40  Schematic Representation of Traffic Flows at Location 4 107 Appendix A / Part A1 - Survey Methodologies & Results A1.3 Location 5 Morogoro Road & Bibi Titi Mohammed Street No. of movements: 4 On an average weekday, AM peak inbound flows at the junction are 19,958 veh/hr and PM peak inbound flows are 19,238 veh/hr. On a weekend day, AM peak inbound flows at the junction are 3,952 veh/hr and PM peak inbound flows are 5,677 veh/hr. On weekday AM peak, entering traffic flows are higher compared with traffic leaving. Also for PM peak, entering traffic are higher compared with traffic leaving. On weekend day, traffic flows are comparatively lower than the weekday. For AM peak, entering traffic flows are higher compared Figure A1.3-42  Satellite View of Location 5 with traffic leaving. Also for PM peak, entering traffic are less compared with traffic leaving. Weekday 7am - 8am 5pm - 6pm B 718 531 Weekend 8am - 9am 5pm - 6pm B 673 403 Weekday 7am - 8am 5pm - 6pm L 985 416 Weekend 8am - 9am 5pm - 6pm L 470 260 Weekday 7am - 8am 5pm - 6pm I 159 308 H 1489 951 Weekend 8am - 9am 5pm - 6pm I 98 148 H 981 780 Figure A1.3-43  Schematic Representation of Traffic Flows at Location 5 Appendix A / Part A1 108 A1.3 Location 6 Bagamoyo Road & Kawawa Road No. of movements: 12 On an average weekday, AM peak inbound flows at the junction are 19,959 veh/hr and PM peak inbound flows are 25,477 veh/hr. On a weekend day, AM peak inbound flows at the junction are 3,934 veh/hr and PM peak inbound flows are 5,751 veh/hr. On weekday AM peak, inbound traffic flows are less compared to the outbound traffic. The same situation is reflected also during the PM peak hour. On the weekend day, traffic flows are comparatively lower than the weekday. For the AM peak hour, entering traffic flows are higher Figure A1.3-45  Satellite View of Location 6 compared to the traffic flows exiting the junction. The same situation happens also during the PM peak hour. Weekday 7am - 8am 5pm - 6pm A 39 58 B 600 1483 C 52 31 Weekend 8am - 9am 5pm - 6pm Weekday 7am - 8am 5pm - 6pm A 102 22 L 246 74 B 515 595 K 960 817 C 58 37 J 802 272 Weekend 8am - 9am 5pm - 6pm L 104 75 K 886 1132 J 258 217 Weekday 7am - 8am 5pm - 6pm Weekday 7am - 8am 5pm - 6pm I 628 560 D 52 76 H 1541 1388 E 1447 2284 G 151 92 F 137 208 Weekend 8am - 9am 5pm - 6pm Weekend 8am - 9am 5pm - 6pm I 299 263 D 99 122 H 926 566 E 606 793 G 213 129 F 67 98 Figure A1.3-44  Schematic Representation of Traffic Flows at Location 6 109 Appendix A / Part A1 - Survey Methodologies & Results A1.3 Origin-Destination Surveys Introduction and Survey Details Origin-destination (O-D) surveys provide a detailed picture of the trip patterns and travel choices of the study area’s residents. These surveys collected valuable data related to households, individuals and trips. This information allows to understand travel patterns and characteristics, measure trends, forecasting, planning for area-wide transportation infrastructure needs and services, and, eventually, provide inputs to travel demand model development. Type of Traffic Counts Road User’s Interviews Conducted on a weekday, Tuesday (07.02.2017) Date & Time Intervals Proposed 06:00 - 12:00 (AM) & 16:00 - 19:00 (PM) Morogoro Road - Kimara Terminal Morogoro Road - Baruti Morogoro Road - Nelson Mandela Road Morogoro Road - Tip Top Morogoro Road - Kagera Street Survey Locations Morogoro Road - Kawawa Road Kawawa Road - Mkwajuni Road Bagamoyo Road - Kawawa Road Morogoro Road - Bibi Titi Mohammed Street Julius K. Nyerere Road - Msimbasi Street Sokoine Drive Proposed for Tuesday - 34 people, two shifts during AM and PM Man Power period, but performed only one shift during morning. Table A1.3-6  Origin-Destination Survey Details Appendix A / Part A1 110 A1.3 Methodology – Roadside Interviews Origin-destination surveys were conducted through As for the BRT transit passenger survey, the study area has roadside interviews on major entry/exit points on the trunk been divided in wards and sub-wards to better interpret route along BRT Phase 1 corridor in the proximity of relevant drivers responses on their journey beginning and intended activity nodes. Survey teams composed of police officers end. and 2 surveyors were assigned every location. The former During the days of surveys, the assistance of the Metro Police were responsible for stopping the vehicles while the latter played a major role in conducting interviews in the morning would carry out the survey. peak between 07:00 until 12:00, as their assistance was Survey periods range from 07:00 to 12:00. Upon the stop crucial and their availability was limited. of any vehicle, the supervisor was responsible in providing Some of the traffic police officers only helped for 2 hours, more detailed information regarding the purpose of the indicating that they did their share or had to leave the site survey, and further contacts information if requested by the due to some reasons. Some of the officers were not Metro road users. police officers with proper authority to pull over vehicles, as Upon the acceptance to cooperate of the road users, then the citizens did not recognise the authority. surveyors followed with asking the questions mentioned in Logistics was a major issue with the officers who were not the questionnaire. Similarly to the BRT interviews, the forms independent in managing their own mobility and at the contained a pre-defined set of questions as outlined in the same time refused to use the BRT. It was certainly critical methodology part. The interviewer filled up site information, to organise their transport arrangements and this was date & time of the survey conducted and manually unexpected. inputted the responses of each road user in a single form. In general, significant physical constraints were Questions were focused on the passenger’s trip origin encountered. These issues influenced the ability to operate (ward/sub-ward) and destination (ward/sub-ward), purpose for the entirety of the intended period and resulted in: of the trip, frequency of the trip, driver’s personal details (age/occupation/household/car ownership etc). • Not surveying one location (Bagamoyo Road - Kawawa Road), • Not surveying all directions (North/South/ West/East) at a given location, and • Obtaining a generalized limited response rate (for the road side surveys an average from 5 to 10 interviews per hour was obtained). 111 Appendix A / Part A1 - Survey Methodologies & Results A1.3 Sampling Rate The sampling rate were estimated for the total interviews conducted on morning survey period. Survey data obtained from the team registered from 7:00-12:00 (938 no. of records) and 15:00 to 16:30 (60 no. of records) for 10 locations. Due to very small number of interview during afternoon period, this information was excluded from the analysis. In addition, due to small no. of record during 07:00 to 12:00 (5 hours), it was considered to use the total number of records during this period, in this respect, if traffic flows are considered for the same period to estimate the sampling rate then the percentage will be negligible. So it was assumed that in order to obtain a value for sampling rate, all the records during morning period (983 no.) will be divided by traffic flows of peak period (one hour - 27,560) which is 3% (*This is not the right way to represent sampling rate and it is deemed unsatisfactory. However, it seems the only possible way to obtain information in the circumstance). The information was therefore elaborated, but treated with extreme caution for planning purposes. Results & Observations The analysis was carried out for the survey data obtained for the time period 7:00 – 12:00 AM. The main aim of the analysis Morning Peak Hour 120000 110000 100000 90000 80000 70000 60000 50000 40000 30000 20000 10000 0 6:00-7:00 6:15-7:15 6:30-7:30 6:45-7:45 7:00-8:00 7:15-8:15 7:30-8:30 7:45-8:45 8:00-9:00 Monday (06/02/2017) 24869 26126 26818 26955 27569 27932 27942 28182 27905 Wednesday (08/02/2017) 29978 31554 31689 31913 30769 29943 29905 29502 30323 Friday (10/02/2017) 29347 30738 31385 31821 32055 30451 29031 28161 26959 Saturday (11/02/2017) 16702 16980 17296 17892 18458 18986 19463 20290 21126 Figure A1.3-46  Morning Peak Hour Flows Traffic was to understand the traffic composition, occupation, purpose of the trips, frequency of the trips and Composition average journey times 10 surveyed location. These results help in supporting the reasoning over the behaviours & patterns of road user’s over the Phase-1 corridor (with limitations). Traffic Composition 16% Traffic composition is defined as the percentage of the different types of vehicles moving on the road network. During morning period, traffic is composed of 16% of motorbikes and 84% of cars. Most of the traffic during the morning is mainly composed of cars. Occupation 84% Occupation of road users represents the attempt to reconcile traffic flows with Car Motorbike socio economic profiling. Occupation of road user’s is estimated and represented Figure A1.3-47  Traffic Composition in the figure as shown below. During morning period for all the 10 locations, Appendix A / Part A1 112 A1.3 major portion traffic road users are workers (self-employed & employed – 96%), a small portion 2% of students, 1% of not- employed and 1% of others. Morning period represents major traffic road users are workers. Purpose of Trips Purpose of trips aims to represent the purpose for which the journey is undertaken. Purpose of the trips are estimated and Occupation represented in the figure as shown below. During morning period for all the 10 surveyed locations, work based purposes (73%) trips are the vast majority followed by a considerable portion (12%) of home-bound trips with the remaining as Occupation SOKOINE STREET DRIVE 9 10 11 1% MOROGORO ROAD & TIP TOP 2% 1% MOROGORO ROAD & NELSON MANDELA ROAD MOROGORO ROAD & KIMARA TERMINAL Se 7 MOROGORO ROAD & KAWAWA ROAD 6 MOROGORO ROAD & KAGERA STREET Em 5 MOROGORO ROAD & BIBI TITI STREET Stu 4 MOROGORO ROAD & BARUTI ROAD 46% 50% 3 No KAWAWA ROAD & MUKAJUNI ROAD 2 JK NYERERE ROAD & MSIMBAZI ROAD Oth 1 0% 10% 20% 30% 40% 50% 60% 70% 80% 90%100% Self-employed Employed Student Not employed Other Figure A1.3-48  Road Users Occupation school/shopping/other based trips. Purpose of trip Purpose of trips SOKOINE STREET DRIVE 1% 9 10 11 MOROGORO ROAD & TIP TOP 4% MOROGORO ROAD & NELSON MANDELA ROAD 5% 3% Hom MOROGORO ROAD & KIMARA TERMINAL 2% 12% 7 MOROGORO ROAD & KAWAWA ROAD Wor 6 MOROGORO ROAD & KAGERA STREET Sch 5 MOROGORO ROAD & BIBI TITI STREET Sho 4 MOROGORO ROAD & BARUTI ROAD 3 Erra KAWAWA ROAD & MUKAJUNI ROAD 2 Acc JK NYERERE ROAD & MSIMBAZI ROAD 73% 1 Oth 0% 10% 20% 30% 40% 50% 60% 70% 80% 90%100% Home Work School Shopping Errand Accompany Other Figure A1.3-49  Purpose of Trips 113 Appendix A / Part A1 - Survey Methodologies & Results A1.3 Beginning of the Trip (Starting Point of the Journey) Beginning of the trips represents the starting point of each trip made by the road user. Beginning of the trips are estimated and represented in the figure as shown below. During the morning period in all the 10 surveyed locations, the majority of the trips starts unsurprisingly from home (69%), followed by work places (24%) representing potentially workers leaving the night shift. Beginning of trip Beginning of trip SOKOINE STREET DRIVE 9 10 11 2% 2% 2% MOROGORO ROAD & TIP TOP 1% MOROGORO ROAD & NELSON MANDELA ROAD MOROGORO ROAD & KIMARA TERMINAL Home 7 MOROGORO ROAD & KAWAWA ROAD Work 6 MOROGORO ROAD & KAGERA STREET 24% 5 School MOROGORO ROAD & BIBI TITI STREET 4 MOROGORO ROAD & BARUTI ROAD Shoppin 3 KAWAWA ROAD & MUKAJUNI ROAD 69% Errand 2 JK NYERERE ROAD & MSIMBAZI ROAD Other 1 0% 10% 20% 30% 40% 50% 60% 70% 80% 90%100% Home Work School Shopping Errand Other Figure A1.3-50  Beginning of Trip Frequency of Trips Frequency of trips represents how frequently users make their trip. Frequency of the trips are estimated and represented in the figure as shown below. During the morning period in all the surveyed locations, 83% of the trips are made daily and 17% of the trips are made 2-3 times per week. Frequency of trips are majorly done daily by the road users and observed phenomena can be treated as a systematic component of Dar es Salaam mobility. Frequency of the trip 17% 83% Daily 2/3 times a week Figure A1.3-51  Frequency of Trips Appendix A / Part A1 114 A1.3 Average Journey Times Average journey travel times represents journey time of each road user on road the network. Average journey times are estimated and represented in the figure as shown below. During morning period for 11 surveyed locations, 50 minutes of average journey time has been registered. At Morogoro - Baruti Road higher journey times were recorded (64 minutes). Also at Morogoro - Bibi Titi Road, JK Nyerere - Msimbazi Road and Sokoine Street Drive, drivers stated an average journey times of 55 minutes. The figure blow represents the average journey travel times by purpose at all the surveyed locations. It can be observed that Average journey time SOKOINE STREET DRIVE 11 JK NYERERE ROAD & MSIMBAZI ROAD 10 MOROGORO ROAD & BIBI TITI STREET 9 KAWAWA ROAD & MUKAJUNI ROAD 7 MOROGORO ROAD & KAWAWA ROAD 100,0 6 MOROGORO ROAD & KAGERA STREET 90,0 5 80,0 MOROGORO ROAD & TIP TOP 4 70,0 MOROGORO ROAD & NELSON MANDELA ROAD 3 60,0 MOROGORO ROAD & BARUTI ROAD 2 50,0 MOROGORO ROAD & KIMARA TERMINAL 1 40,0 0,0 10,0 20,0 30,0 40,0 50,0 60,0 70,0 30,0 Figure A1.3-52  Average Journey Time 20,0 10,0 journey times are comparably less for the homebound trips at all locations (average – 40 minutes). Shopping based trips 0,0 last on average 56 JK NYERERE minutes and KAWAWA are the longest among other purposes. The rest of the purposes feature an average travel MOROGORO MOROGORO MOROGORO MOROGORO MOROGORO MOROGORO MOROGORO SOKOINE time & 35 minutes. below ROAD ROAD & ROAD & ROAD & BIBI ROAD & ROAD & ROAD & ROAD & ROAD & TIP STREET DRIVE MSIMBAZI MUKAJUNI BARUTI ROAD TITI STREET KAGERA KAWAWA KIMARA NELSON TOP ROAD ROAD STREET ROAD TERMINAL MANDELA ROAD 100,0 Home Work School Shopping Errand Accompany Other 90,0 80,0 70,0 60,0 50,0 40,0 30,0 20,0 10,0 0,0 JK NYERERE KAWAWA MOROGORO MOROGORO MOROGORO MOROGORO MOROGORO MOROGORO MOROGORO SOKOINE ROAD & ROAD & ROAD & ROAD & BIBI ROAD & ROAD & ROAD & ROAD & ROAD & TIP STREET DRIVE MSIMBAZI MUKAJUNI BARUTI ROAD TITI STREET KAGERA KAWAWA KIMARA NELSON TOP ROAD ROAD STREET ROAD TERMINAL MANDELA ROAD Figure A1.3-53  Average Journey Time by Purpose Home Work School Shopping Errand Accompany Other 115 Appendix A / Part A1 - Survey Methodologies & Results A1.3 Interviews at BRT Stations Introduction and Survey Details BRT interview survey, also called as a face-to-face survey, is a Personal interview surveys are used to evaluate the answers survey method conducted when a specific target population of the passengers and at the same time, to observe the (BRT Passengers) are involved. The purpose of conducting behaviour of the passengers, either individually or as a personal BRT interview surveys is to explore the responses group. Interview questionnaire aim was primarily to assess of the passengers to gather more and deeper information. the stations catchment investigating mobility habits in reaching the BRT access points. The information obtained at the six locations is intended to be used as a specimen for others with similar characteristics. Type of BRT Survey Passengers Interviews Conducted on 5 weekdays and 1 weekend: Monday (06.02.2017), Tuesday (07.02.2017), Wednesday (08.02.2017), Thursday Date & Time Intervals (09.02.2017), Friday (10.02.2017) & Saturday (11.02.2017) 06:00 - 12:00 (AM) & 16:00 - 19:00 (PM) Gerenzani Terminal Kimara Terminal Kivukoni Terminal Survey Locations Kagera Morocco Terminal Ubungo Terminal Monday/Wednesday/Saturday - 5 people Tuesday - 9 people Man Power Thursday - 43 people Each person 2 shifts for 6 days Table A1.3-7  BRT Passengers Survey Details Appendix A / Part A1 116 A1.3 Methodology - Transit Passenger Survey The interview have been conducted on the BRT Phase Passengers were asked a pre-defined set of questions 1 corridor at major stations and terminal locations. These as outlined in a specifically designed form shown in the interviews were conducted over 5 weekdays (from 6th methodology chapter. Interviewers were required to fill up February 2017 until 10th February 2017) and 1 weekend site information, date & time of the interview and manually day (11th February 2017). Transit Passengers surveys fill up the passengers’ responses in the form: one form per were conducted for three hours over two periods during interview. Questions were focused on the passenger’s trip 06:00 - 12:00 AM and 16:00 - 19:00 pm. Six locations along origin (ward/sub-ward) and destination (ward/sub-ward), the corridor were chosen for the surveys. These cover the purpose of the trip, frequency of the trip, multiple mode following typologies: terminal stations and surrounding trips and related travel time. areas, minor stations locate in the middle of the corridor The study area was divided in wards to better interpret (such as Kagera, which is surrounded by densely populated passengers responses their journey origin begins and final area). destination. The ward subdivision is represented in the figure below. Figure A1.3-54  Central Ward Subdivision map Figure A1.3-55  Citywide Ward Subdivision map 117 Appendix A / Part A1 - Survey Methodologies & Results A1.3 Sampling Rate The sampling rate represents the ability of a statistical The chosen factors express the proportion of the surveyed population (which is observed, monitored, studied) to hours in relation to the whole day: 0.4 for AM and represent the characteristics of the statistical “universe”. In 0.3 for PM. This process allows to compare the number of its simplest form, it is obtained after dividing the population, interviews and the correspondent ridership values at the in our case the total number of interviews conducted during same scale to derive the sampling rate. the survey period, by the universe, in our case the number of A high sampling rate is observed for Kagera when compared passengers at each station (i.e. the ridership). with other locations. An average of 9% sampling rate has The total number of BRT interviews at all the six locations been obtained for the morning AM survey period. are 3,588. Out of which 1,824 were collected during the During the afternoon period, survey sampling rates are morning survey hours (06:00-12:00 AM) and 1,764 during the higher than in the morning period. An average of 12% afternoon survey hours (16:00-19:00). sampling rate has been obtained for the evening PM survey Daily ridership at each location was obtained from DART period. A mean overall sampling rate for morning and total monthly ridership values divided by 30. There upon, evening period is 10%, which is adequate to give a good the daily ridership value has been multiplied by a reduction representation of BRT user behaviors. factor to obtain the morning and the evening ridership These sampling rates are deemed acceptable for the estimated figures. intended purposes (i.e. help in supporting the reasoning over the behaviours & patterns of people reaching BRT station) hence the information collected is deemed significant. Appendix A / Part A1 118 A1.3 AM Ridership (06:00-12:00) PM Ridership (16:00-19:00) Ridership Interviews Sampling Rate Location AM PM AM PM AM PM Gerezani Terminal 3,897 2,923 249 285 6% 10% Kimara Terminal 7,860 5,895 318 318 4% 5% Kivukoni Terminal 3,434 2,576 216 354 6% 14% Kagera 721 541 563 273 78% 50% Morocco Terminal 1,878 1,409 54 328 3% 23% Ubungo Terminal 2,028 1,521 424 206 21% 14% AM Average 19,818 14,864 1,824 1,764 9% 12% Table A1.3-8  Sampling Rates for AM and PM Survey 119 Appendix A / Part A1 - Survey Methodologies & Results A1.3 Results & Observations Modal Share The analysis was carried out for the survey data obtained This analysis expresses the prevailing choices that people for the morning period 6:00 – 12:00 AM and afternoon make about the mode of travel when undertaking a journey. period 16:00 – 19:00 PM. The main aim of the analysis was It varies according to the purpose of the trip and the time of to understand the mode through which people access the the day. BRT station, travel times, desire lines and catchment area for The responses to the interview questionnaires returned a each surveyed BRT station. detailed information on the mode split for motorized and non-motorized transport modes and helped in gaining a full understanding of the mobility patterns and modes of access to the BRT stations during AM and PM peak hour. It should be clear that the analysis is aimed at defining the mode of access to the station of people within the catchment, not the mode share. Figure A1.3-56  Modal Share AM Appendix A / Part A1 120 A1.3 According to the responses given to the During the afternoon period (16:00/19.00 PM), walking interview surveys during the morning hours emerges out as the main mean to reach the BRT (6:00 – 12:00 AM), people’s main preferred means of stations except at Kimara terminal, where major portion transport to reach BRT stations are walking and public of responses indicates the use of public transport transport (daladala & bus). Gerezani (76%), Kimara (54%) (daladala and bus – 49%) as the main mode to reach station. and Morocco Terminals (49%) stand out having high shares Also Morocco (34%), Gerezani (24%) and Kivukoni Terminals of public transport compared with others. On the other (15%) have a considerable share of public transport hand, Ubungo Terminal (54%) and Kagera station (58%) (daladala and bus) as the mean to access the station. show walking as the prevailing access mode. Figure A1.3-57  Modal Share PM 121 Appendix A / Part A1 - Survey Methodologies & Results A1.3 Travel Time The travel times and related modes of transport prior & This is mostly due to the fact that these interviews were subsequent from BRT station locations for each passenger based on verbal description of their travel pattern, which were also investigated with the interviews. may be true or distorted in some cases, also these verbal Response by a typical user of BRT represents the following estimates are not as accurate as real travel times. travel pattern: At Gerezani Terminal, longest travel 1. A segment from the origin (wards/sub-wards) using times are observed during AM period one or multiple modes to reach BRT station (48 mins) compared with PM period (18 mins) and also at Gerezani, AM period travel times are longest 2. The segment on board of the BRT among all other locations. At Ubungo Terminal, 3. After reaching destined BRT station, a segment longest travel times are observed during PM period from thereon using one or multiple modes to (29 mins) compared with AM period (27 mins) and also reach the final destination (wards/sub-wards). PM period travel times are the longest among all other Average travel times are based only on passengers declared locations. travel time in reaching the BRT stations from their origin At Morocco Terminal, longest travel wards/sub-wards by multi means of transport, as illustrated times are observed during AM period in the graph below. (23 mins) compared with PM period (16 mins). At Kagera, This is because interviews are only conducted on boarding longest travel times are observed during AM period passengers, it explains the origin travel time estimates (16 mins) compared with PM period (12 mins). At Kivukoni roughly, but this doesn’t explain precisely alighting patterns. Terminal, longest travel times are observed during AM period (23 mins) compared with PM period (14 mins). Similarly, at Kimara Terminal, longest travel times are observed during AM period (22 mins) compared with PM period (21 mins). Appendix A / Part A1 122 A1.3 Gerezani Terminal Ubungo Terminal Morocco Terminal Magomeni Kagera Kivukoni Terminal Kimara Terminal 0 5 10 15 20 25 30 35 40 45 50 Kimara Kivukoni Magomeni Morocco Ubungo Gerezani Terminal Terminal Kagera Terminal Terminal Terminal PM Period 21 14 12 16 29 18 AM Period 22 23 16 23 27 48 Figure A1.3-58  Average Travel Time, AM and PM Period 123 Appendix A / Part A1 - Survey Methodologies & Results A1.3 Travel Time by Modes Average travel times by different modes are based on the Whereas, during PM period public transport (daladala) also time taken by passenger to reach the BRT stations from has longest travel times for all the survey locations except their origin wards/sub-wards by different modes. The graph for Gerezani and Kivukoni Terminals which mark the longest below represents the average travel times for both morning travel times for private mode (motor bikes & cars). AM and afternoon PM periods by mode of transport utilized. Overall, Gerezani shows longest travel times per each mode During the AM period, public transport (daladala) has for both AM & PM periods. longest travel times for all the survey locations except Kagera which has longest travel times for private mode (cars). 60 50 40 TIME (min) 30 20 10 0 Walk Bike M-Bike Car Bus Daladala Other Gerenzani Terminal 17 55 21 40 52 59 44 Kimara Terminal 14 12 24 28 30 23 Kivukoni Terminal 8 9 25 20 38 31 Magomeni Kagera 10 8 13 45 28 29 23 Morocco Terminal 12 16 30 20 31 23 Ubungo Terminal 11 11 29 32 40 18 Figure A1.3-59  Travel Time by Mode, AM Period Appendix A / Part A1 124 A1.3 60 50 40 TIME (min) 30 20 10 0 Walk Bike M-Bike Car Bus Daladala Other Gerenzani Terminal 14 27 35 28 30 Kimara Terminal 15 24 13 21 30 22 Kivukoni Terminal 11 1 37 27 20 24 12 Magomeni Kagera 11 8 25 Morocco Terminal 12 20 5 11 23 22 15 Ubungo Terminal 25 18 19 30 45 30 Figure A1.3-60  Travel Time by Mode, PM Period 125 Appendix A / Part A1 - Survey Methodologies & Results A1.3 Trip Distribution – Generation and Attraction From BRT Stations Desire lines represent the trips generated (by people) from Note that the desire line maps show the distribution of trip different origins (wards/sub-wards) to reach a specific BRT origins in relation to wards. It was decided to use wards as station. The line thickness represents the density of the the reference system because sub-wards were not indicated trips and it is obtained from the number of interviewed clearly in the interviews forms and it could have led to poor passengers originating from the same wards/sub-ward. representation. Instead, the wide territorial extension of In order to obtain a good representation and avoid wards offers a rough and partial view of the movements. disproportions caused by the interviews, all data have been During Morning AM period, Gerezani Terminal is adjusted by multiplying them to stations sampling rate. characterised by longest trips (desire lines) coming from several wards located south of the city. Whereas, during PM period trips are shorter and with less in number originating from different small ward (origins), in particular from city centre (CBD area). Kivukoni Terminal for both AM & PM periods shows similar results representing several trips originating from the other Figure A1.3-61  Desire Lines, AM Period Appendix A / Part A1 126 A1.3 side of the creek (Kigamboni). Also Kimara Terminal shows similar results for both the periods, short trips have high density compared to longer trips with lower density. The remaining locations show similar trends for both the time periods having shorter trips with high density. Figure A1.3-62  Desire Lines, PM Period 127 Appendix A / Part A1 - Survey Methodologies & Results A1.3 Catchment Area The BRT interview surveys enabled to understand BRT locations and then multiplying the average walking time for stations catchment area and, by generalization, the whole all locations divided by 4.5 km/h (average walking speed) corridor’s catchment that is, in other words, the area in gives the value of an average distance walk-able from station which the BRT Phase-1 corridor can have an influence. locations within the estimated average time (see below). This was held as one of the parameters (among others) During the AM period, the average travel time is of 12 to define the project boundary for the integrated master minutes, which leads to an average walking distance to of planning exercise to be carried out during the second stage 900 m. of the project process. During the PM period, the average walking distance is In order to understand the extension of the catchment area, slightly longer (1,009 m) because the declared access average travel times were estimated for walking mode at travel time was of 15 minutes. Accordingly, the assumed all the surveyed six locations for morning and afternoon catchment area is 1,000 m periods. An average walking time is estimated for all AM Peak PM Peak Location Minutes Meters Minutes Meters Gerezani Terminal 17 1.28 14 1.06 Kimara Terminal 14 1.05 15 1.10 Kivukoni Terminal 8 0.60 11 0.81 Kagera 10 0.75 11 0.84 Morocco Terminal 12 0.90 12 0.90 Ubungo Terminal 11 0.83 25 1.86 Average 12 0.90 15 1.09 Table A1.3-9  Average Walking Time for AM and PM Appendix A / Part A1 128 A1.3 1500 m 1000 m 500 m Figure A1.3-63  Corridor Catchment Area Average walking distance 2,00 1,80 1,60 1,40 Distance (Km) 1,20 1,00 0,80 0,60 0,40 0,20 0,00 GEREZANI KIVUKONI MOROCCO UBUNGO KIMARA KAGERA TERMINAL TERMINAL TERMINAL TERMINAL AM PEAK 1,28 1,05 0,60 0,75 0,90 0,83 PM PEAK 1,06 1,10 0,81 0,84 0,90 1,86 Figure A1.3-64  Average Walking Distance for AM and PM 129 Appendix A / Part A1 - Survey Methodologies & Results A1.3 Video Survey Methodology In order to assess the actual capacity of the BRT corridor, a Furthermore, this kind of analysis allows to estimate the video survey was conducted by positioning a video camera existing capacity of the BRT Phase 1 Corridor as well as on the pedestrian bridge at Tip Top station. This survey residual capacity of the BRT Phase 1 Corridor in light of the was meant to understand the different kind of services and future land use development along the transit corridor. number of buses passing through the section as well as BRT buses going to and coming from the City centre and computing the headways of the system during the peak passing by Tip Top station are recorded clearly by the video hour. camera. The video shooting was carried out in the morning peak hour (7:00 – 8:00 AM) for a duration of approximately 40 minutes. Results & Observations Figure A1.3-66  BRT Services LEFT (going RIGHT (Coming towards CBD) from CBD) Figure A1.3-65  Video Screen-Shot Appendix A / Part A1 130 A1.3 Number of BRT Buses & Services General Observations Video survey was conducted for 40 minutes, in order to The following observations derive from the analysis of the assess the total BRT buses during the peak hour, and the video. results of the 40 mins were proportionally converted to 60 During certain time periods within the peak hour, min. This helps in understanding the peak one hour results. BRT buses are queuing up. This is due to the fact that It was observed that Tip Top station has three bays on each no BRT priorization and efficient signalling system is side. Total BRT buses leaving from Kimara towards the city implemented on Morogoro road. centre (left side) and passing by Tiptop Station are 77, out of Tiptop station on either side has pedestrian crossing. which 11 are express buses and 66 are local buses. There are few services which are passing by the station Total BRT buses leaving from CBD towards Kimara without stopping at the station. (right side) and passing by Tiptop Station are 78, out of Tiptop station has overtaking lane. which 30 are express buses and 48 are local buses. When pedestrians are crossing, the buses passing by via Headways overtaking lane has to wait until they see clearance on the The headway of BRT buses leaving from Kimara towards the road which causes queuing up by the BRT buses leaving the city center (left side) is 47 seconds whereas in the opposite station bays. direction the headway is 46 seconds. It was observed, pedestrians are not only crossing in the designated area, but also all along the corridor. All the above mentioned phenomena will inevitably affect the potential of the line to achieve its full theoretical throughput CBD to KIMARA (Right Side) KIMARA to CBD (Left Side) No. of Station bays 3 No. of Station bays 3 No. of Buses arrived at station 78 No. of Buses arrived at station 77 No. of Express Buses 30 No. of Express Buses 11 No. of Local Buses 48 No. of Local Buses 66 Headway (Seconds) (60*(60/78)) 46 Headway (Seconds) (60*(60/77)) 47 Table A1.3-10  Video Survey Results 131 Appendix A / Part A1 - Survey Methodologies & Results A1.3 GPS Survey Methodology A GPS survey was conducted on BRT corridor, a team The analysis was carried out for the survey data obtained has used a handheld CPS unit while travelling from one between Gerenzani Terminal and Kimara Terminal. The main station location to the other. This allowed the retrieval aim of the analysis is to understand average BRT speeds and of information such as speed, delay, and acceleration travel time along the corridor. without the need for costly instrumentation and constant recalibration. GPS survey has been carried out to calibrate the isochrones analysis on the current and planned BRT public transport network. This kind of survey allows in having precise information, which is a very good representation of the existing average speeds along the BRT corridor. Results & Observations Figure A1.3-67  GPS Tracking Gerezani-Kimara Appendix A / Part A1 132 A1.3 Speed Profiles Survey data is plotted for elapsed time vs travelled distance. It can be observed, how the BRT vehicle stopped along This helps in viewing a number of attributes of travel on the its way in a queue of traffic upstream of the intersection survey route. (at Ubungo Maji) and then accelerated away from the Firstly, variability in travel times along the route can be intersection, albeit rather slowly. observed, at the same time being able to see which sections Another delay was caused by the necessity of getting off at of the route are contributing to the variations in travel time. Fire station in order to catch the service headed to Kimara Secondly, locations where vehicles stop can be observed; Terminal. stopping-starting nature of the flow is evident at all stations. The lack of seamless interchanges between the different The image below outlines the variations in speed along services along the trunk route represents a major issue the route for a single survey run and few delays were for the optimization of the travel times. In a nut shell, the encountered at two stations performed analysis on travel time to reach Kimara Terminal from Gerezani Terminal is about 45 minutes with an average speed ranging from 18 km/h to 22 km/h. Figure A1.3-68  Speed Profile Gerezani-Kimara 133 Appendix A / Part A1 - Survey Methodologies & Results A1.3 Survey Constraints and Limitations There are certainly some limitations for the surveys carried Roadside Survey out, and in particular: The main limitation in the road side interview is the Traffic Survey sampling rate (3%) which is too low to consider the information obtained statistically reliable. The limited The main limitation of Traffic surveys is that they sample size was affected by unexpected occurrences on the have been carried out only for 6 locations and not filed such as, in this case, a limited cooperation from Police on all secondary intersections along the corridor partols. (mainly for budget reasons and available time) Additional Although originally intended to build select link matrices, it to this, the surveys are carried out only along Morogoro was decided to use the collected data to only understand road and not on the local roads feeding the main corridor. the behaviour of road users and other qualitative aspects. Fine grained information on the local roads will be retrieved A more detailed analysis on mobility behaviours will be through the land use verification survey. performed through the reports for the development of These 6 locations allow a partial observation of overall origin-destination matrices using mobile phone data for the dynamics of the traffic flow, but do not represent a World Bank. This will allow us to build origin-destination comprehensive picture of traffic volumes and turn profiles of the corridor by station area. manoeuvres at the remaining junctions inside the study area. Turn movements are site specific information and can’t Video Survey be generalized. No particular limitations were identified. Transit Passengers Interview Survey GPS Survey The only limitation in the Transit Passengers Interview GPS survey has a major limitation, it has only obtained Survey lie in the limited number of stations observed vs average speeds and travel time of BRT only from Gerenzani the overall number of stations. This doesn’t allow the to Kimara Terminal at a specific period (off peak) of the construction of a corridor matrix but only allows parametric entire day. Expecting the results to represent the real pattern behavioural information of BRT users. of BRT throughout the day is not appropriate. Increasing the number of sample information on different routes of the study area would allow us to have a greater detail of information that could permit to obtain a more realistic set of data. Appendix A / Part A1 134 A1.3 135 Appendix A / Part A1 - Survey Methodologies & Results A1.3 Qualitative Traffic Congestion Study Results and key Findings Introduction The Traffic Congestion Study Method The main aim of the survey is to understand the causes The Sample Areas of congestion and the conflicts on the local roads feeding The survey method takes a selection of 13 “representative” into Morogoro road. As part of Phase 1 this finer level of stations areas in order to evaluate the location and causes understanding will be used to support our verification and of traffic bottlenecks on the local road network within a finalization of the baseline studies, as well as enhance our raster-based Euclidean distance of 500m from the centre of understanding and analysis of the existing traffic condition each station. The following candidate stations have been on the local road network and ability to accommodate the identified based on their location within the city and the future change. typology assigned to them in the evolving corridor strategy In particular, the analysis results will establish the causes, so that each combination of urban area and TOD typology types and locations of congestion on key sections within are covered. close proximity of the BRT stations. The output will be As per land use verification survey (streetscape component) represented by a set of qualitative parameters in order to key sections of the local road network within the following inform the planning process during the next project phase station areas were surveyed: by determining possible improvements. The findings will also be comparable with the land use survey verification and 1. Gerezani Terminal station area related results in terms of street activity (traffic congestion 2. City Council station area level and pedestrian activity) performed within the same 3. Kivukoni station area station areas. 4. Fire Station area 5. Jangwani station area 6. Magomeni Mapipa station area Morocco Kimara Baruti Ubungo Maji Manzese Kinondoni Kibo Magomeni Mapipa Jangwani City Council Kivukoni Fire Gerezani Figure A1.3-69  Survey Location map Appendix A / Part A1 136 A1.3 1. Manzese Station area 1. Loading/unloading activities - Trucks and delivery vans occupy the carriageway for 2. Ubungo Maji Station area loading and unloading activities 3. Kibo station area 2. Heavy vehicle transit - Heavy vehicle circulation occurs 4. Baruti station area on local roads within residential neighborhoods 5. Kimara station area 3. Road encroachment - Mainly when pedestrians invade the street due to the lack of dedicated 6. Kinondoni station area pedestrian paths or due the presence of obstructed 7. Morocco station area sidewalks by informal trading or illegal parking 4. Other Fieldwork: Survey Technique The survey was conducted throughout two days, Monday The survey was carried out by three trained persons 9th October, from 8 am to 3 pm, and Thursday 12th October, (MIC traffic consultants/engineers) that annotated in loco the from 8 am to 12 am. Part of the first day was utilised also presence and the causes of the bottlenecks (traffic jam, road for a short dry run of the survey to ensure that all potential accidents, illegal parking, vehicle breakdowns, random dala issues are dealt with before starting officially the survey. dala stops, random pedestrian crossings etc …). Surveyed routes inside the already defined 13 station areas were retrieved through the use of a GPS unit while travelling. The survey components were collected using videos and images taken from a traditional three-wheeler (bajaji). The surveyors were provided with sheets and electronic handheld devices to annotate the location of the bottlenecks as well as all qualitative observations on traffic influencing events categorized as follows: 1. Random Dala dala Stops - The road section doesn’t allow dala dalas to stop without obstructing the carriageway 2. Random Pedestrian Crossings - Pedestrians tend to randomly cross the street 3. Road accidents/Vehicle breakdowns - The road is not safe/the road section doesn’t provide emergency space for vehicle breakdowns 4. Illegal parking/double parking/parking action - Illegal or double parking reduces road capacity / parking maneuvers cause friction with traffic flows 5. Non signalized traffic intersection - Random crossing forces vehicles to slow down and prevent regular traffic flows 6. Informal trading activity - Traders occupy sidewalks or the carriageway, reducing the road space or preventing smooth traffic flows 7. Poor road condition - Unpaved road and potholes prevent seamless circulation enhancing road safety issues 137 Appendix A / Part A1 - Survey Methodologies & Results A1.3 General Findings Within the surveyed station areas, traffic flows are mostly Besides the main four issues described above, other causes influenced the poor road conditions. Unpaved roads, of congestion have been noted, in particular: potholes and no drainage system make the circulation very –– Random pedestrian crossings and footpaths difficult and forces very often vehicles to stop and divert encroachment issues are concentrated in areas their route. Other important traffic influencing events are with a high concentration of commercial activities represented by non signalized traffic intersections, informal and working places, as Gerezani Terminal and Fire (Kariakoo market), City Council (CBD), Manzese, trading activities and illegal parking. They all affect the Kimara and Kinondoni (informal traders). overall circulation by creating bottlenecks, hence, increasing travel times, safety issues and low carbon emissions. In –– Loading/unloading activities and heavy vehicles transit affect mostly the central part of the corridor general, throughout the survey campaign, no road accidents (Fire, Magomeni Mapipa, Manzese) and the Morocco or vehicle breakdowns were recorded. area (Kinondoni and Morocco) due to the presence of local workshops and informal commercial activities. Looking at the traffic influencing events distribution within the different stations areas, some overarching observations can be made: –– Poor road conditions, which is the most common observation, equally affect different station typologies. –– Non signalized traffic intersections represent the main cause of congestion in formally planned areas, as the City Centre and Morocco area. –– Informal trading activities affect mostly Gerezani and Manzese areas, where a high concentration of markets occur. –– Illegal parking problems are mainly concentrated in areas where attractors such as commercial activities or educational facilities (universities) are located. 40 35 30 25 20 15 10 5 0 Random Dala dala Random Pedestrian Road Illegal Non signalized Informal trading Poor road condition Loading/unloading Heavy vehicles High pedestrian Stops Crossings accidents/Vehicle parking/parking traffic intersection activity activities transit activity breakdowns action Figure A1.3-70  Traffic Influencing Events Appendix A / Part A1 138 A1.3 12 10 8 6 4 2 0 Gerezani City Kivukoni Fire Jangwani Magomeni Manzese Ubungo Kibo Baruti Kimara Kinondoni Morocco Terminal Council Terminal Mapipa Maji Terminal Terminal Illegal parking/parking action Non signalized traffic intersection Informal trading activity Poor road condition 12 10 8 6 4 2 0 Gerezani City Kivukoni Fire Jangwani Magomeni Manzese Ubungo Kibo Baruti Kimara Kinondoni Morocco Terminal Council Terminal Mapipa Maji Terminal Terminal Random Dala dala Stops Random Pedestrian Crossings Road accidents/Vehicle breakdowns Loading/unloading activities Heavy vehicles transit High pedestrian activity Figure A1.3-71  Traffic Influencing Events Distribution 139 Appendix A / Part A1 - Survey Methodologies & Results A1.3 Traffic Congestion (Bottlenecks) Analysis –– Random pedestrian crossings often stop traffic flow on Msimbazi Street, especially in close proximity to the BRT stations when passengers alight from the buses. Gerezani Terminal area –– Many roads located between Msimbazi Police station Gerezani Terminal area is located in the most urbanized part and Gerezani Terminal are blocked by informal trading of the city and is characterised by a high number of trading activities by obstructing the carriageway. Namely, that happens in Narung’ombe Street, Muhonda street, activities, generating a tremendous pedestrian footfall. The Michikichi Street, Aggrey Street and Masasi Street. terminal is a key interchange hub, with formal and informal public transport means collecting passengers from disparate –– Uhuru Street is heavily affected by congestion due to high pedestrian activity, road encroachment and parts of the metropolitan area. random paratransit stops (dala dala, bajaji and boda On many local roads inside this area the paratransit boda). In addition to this, poor road conditions (Dala dala buses) cannot circulate due to the presence of and non signalized traffic intersections make traffic street markets that prevent any vehicle to access those conditions even worst. The effects of congestion go beyond Uhuru Street: along Kipata Street random roads. As a consequence, their transit is limited only to pedestrian crossings, parking actions and loading/ major local streets, such as Msimbazi Street and Uhuru unloading activities obstruct the carriageway, while in Street. Lindi Street bad road conditions and non signalized traffic intersections prevent a smooth circulation. 1. Random Dala Dala Stops 2. Random Pedestrian Crossings 3. Road Accidents/ Vehicle Breakdowns P 4. Illegal Parking/Double Parking/Parking Action 5. Not Signalized Traffic Intersection 6. Informal Trading Activity 7. Poor Road Condition 8. Loading/Unloading Activities 9. Heavy Vehicles Transit 10. High Pedestrian Activity Route 1 Figure A1.3-72  Gerezani Station Traffic Influencing Events Route 2 Appendix A / Part A1 140 A1.3 Road encroachment due to informal trading activities High pedestrian activity on the street Random dala dala and bajaji stops. 141 Appendix A / Part A1 - Survey Methodologies & Results A1.3 City Council station City Council station area covers a large portion of the –– Illegal parking and non regulated on street CBD. Offices and shops and street markets attract many parking (overspill parking) occupy sidewalks people and both pedestrian footfall and traffic congestion and the carriageway, reducing the street width and forcing pedestrian to walk on the are high. While bajaji’s are not allowed to enter this area, street. This happens in particular on Jamhuri dala dala buses congest the roads as the majority of the Street, Samora Avenue and Sewa Street. routes start/terminate here. Further to this, the circulation –– Very frequent random pedestrian crossings, generating of the BRT generates frictions with the local traffic mainly road encroachments, interrupt traffic flows and when intersecting other roads since the junctions are not represent a safety issue on many streets, as Samora controlled by traffic lights. Avenue, India Street and Makunganya Street. –– Non signalized traffic intersections represent the biggest issue in this area. Traffic flows are not regulated and there are conflicts with the BRT between Morogoro Road and Samora Avenue. 1. Random Dala Dala Stops 2. Random Pedestrian Crossings 3. Road Accidents/ Vehicle Breakdowns P 4. Illegal Parking/Double Parking/Parking Action 5. Not Signalized Traffic Intersection 6. Informal Trading Activity 7. Poor Road Condition 8. Loading/Unloading Activities 9. Heavy Vehicles Transit 10. High Pedestrian Activity Route 3 Figure A1.3-73  City Council Station Traffic Influencing Events Route 4 Appendix A / Part A1 142 A1.3 Not regulated on street parking on the sidewalk forces pedestrian to walk on the street Poor road conditions make circulation difficult and increase road safety issues 143 Appendix A / Part A1 - Survey Methodologies & Results A1.3 Kivukoni Terminal Kivukoni Terminal area relies on a well-defined street network, accommodating dedicated parking spaces as well as wide sidewalks. This station area represents one of the main interchanges of the city, serving BRT, ferry and paratransit passengers as well as acting as main attractor due to the presence of the fish market. In general there are no major issues in terms of road congestion; major traffic flows disruptions occur only when pedestrian cross randomly the road while interchanging from the BRT to the ferry and vice versa. The street section is in good condition and provides dedicated space to accommodate vehicular and pedestrian flows 1. Random Dala Dala Stops 2. Random Pedestrian Crossings 3. Road Accidents/ Vehicle Breakdowns P 4. Illegal Parking/Double Parking/Parking Action 5. Not Signalized Traffic Intersection 6. Informal Trading Activity 7. Poor Road Condition 8. Loading/Unloading Activities 9. Heavy Vehicles Transit 10. High Pedestrian Activity Route 5 Figure A1.3-74  Kivukoni Station Traffic Influencing Events Appendix A / Part A1 144 A1.3 The street section provides adequate space for vehicular and pedestrian transit Jangwani Station Jangwani station area is mostly occupied by the river basin and is subject to flooding. The intersection between Twiga Street and the connection to Morogoro Road, which needs to be improved and regulated, represents the only cause of bottlenecks in this area. 1. Random Dala Dala Stops 2. Random Pedestrian Crossings 3. Road Accidents/ Vehicle Breakdowns P 4. Illegal Parking/Double Parking/Parking Action 5. Not Signalized Traffic Intersection 6. Informal Trading Activity 7. Poor Road Condition 8. Loading/Unloading Activities 9. Heavy Vehicles Transit 10. High Pedestrian Activity Route 8 Figure A1.3-75  Jangwani Station Traffic Influencing Events 145 Appendix A / Part A1 - Survey Methodologies & Results A1.3 Fire station Fire station area is divided into two different parts. The –– Some streets, such as Twiga Street and Congo Street, northern one is mostly characterized by residential are characterized by informal trading activities, which functions, while the southern one is very close to Kariakoo cause several encroachment issues on the carriageway. market and is mainly occupied by commercial activities. –– Non signalized traffic intersections on Nyamwezi Both BRT and paratransit are allowed to access this area and Street and Mazengo Road prevent traffic from often generate friction between private and public transport. running smoothly by creating major bottlenecks. –– Parking issues affect the entire area. Illegal or non- –– Given the close proximity to Kariakoo market regulated on street parking occupy the sidewalks area, Msimbazi Street is affected by the and the carriageway, reducing the street width circulation of heavy vehicles as well as loading/ and creating conflicts with pedestrian. This takes unloading activities throughout the day creating place in particular on Nyati Street, Twiga Street, major discontinuities of flows and decreasing Rufiji Street, Congo Street and Nyamwezi Street. the quality of the urban environment –– Random pedestrian crossings represent a –– The lack of dedicated paratransit stops and drop off/ common issue that slows dramatically the traffic pick up areas (for dala dala, bajaji and boda boda) down. This happens frequently in Nyamwezi forces the vehicles to stop randomly along the way Street, Sikukuu Street and Swahili Street. on the roadside occupying the pedestrian realm and forcing pedestrians to walk on the roads. 1. Random Dala Dala Stops 2. Random Pedestrian Crossings 3. Road Accidents/ Vehicle Breakdowns P 4. Illegal Parking/Double Parking/Parking Action 5. Not Signalized Traffic Intersection 6. Informal Trading Activity 7. Poor Road Condition 8. Loading/Unloading Activities 9. Heavy Vehicles Transit 10. High Pedestrian Activity Route 6 Figure A1.3-76  Fire Station Traffic Influencing Events Route 7 Appendix A / Part A1 146 A1.3 Poor road conditions make transit very difficult by increasing road safety issues and decreasing average travelling speed on the network Spontaneous dala dala stops obstruct the sidewalks, forcing Informal trading activities and on street parking occupy pedestrian to walk on the street sidewalks, forcing pedestrian to walk on the street 147 Appendix A / Part A1 - Survey Methodologies & Results A1.3 Magomeni Mapipa Station Magomeni Mapipa area is characterised by a regular road grid that distributes a fine-grained residential fabric. Generally, roads are not congested, although very poor road conditions represent the major issue in for traffic circulation. Random dala dala on the roadside occupy part of the carriageway limiting the road capacity –– Fair and poor road conditions affect the whole area, preventing regular traffic flows along the local streets. –– Heavy vehicle transit on Mlandizi Street and Kiyungi Street represent a safety issue and an obstacle on local roads. –– Non signalized intersection between Kiyungi Street and Kawawa Road creates a bottleneck, slowing down the traffic flow. –– Random dala dala stops along Dosi Street obstruct the carriageway and block the traffic flows. Very poor road conditions reduce the accessibility to the local neighborhoods 1. Random Dala Dala Stops 2. Random Pedestrian Crossings 3. Road Accidents/ Vehicle Breakdowns P 4. Illegal Parking/Double Parking/Parking Action 5. Not Signalized Traffic Intersection 6. Informal Trading Activity 7. Poor Road Condition 8. Loading/Unloading Activities 9. Heavy Vehicles Transit 10. High Pedestrian Activity Route 9 Figure A1.3-77  Magomeni Mapipa Station Traffic Influencing Events Route 10 Appendix A / Part A1 148 A1.3 The street section provides adequate space for vehicular and pedestrian transit Ubungo Maji Station Ubungo Maji station is characterized by big factories and green areas. The street network is mainly composed by dead-end roads that provide access to industrial plots. The intersection between Morogoro Road, Sam Nujoma Road and Nelson Mandela Road acts as an important bottleneck for the city traffic in need of a revision of the traffic light phasing that currently produces long traffic queues on the main arterial roads. 1. Random Dala Dala Stops 2. Random Pedestrian Crossings 3. Road Accidents/ Vehicle Breakdowns P 4. Illegal Parking/Double Parking/Parking Action 5. Not Signalized Traffic Intersection 6. Informal Trading Activity 7. Poor Road Condition 8. Loading/Unloading Activities 9. Heavy Vehicles Transit 10. High Pedestrian Activity Route 8 Figure A1.3-78  Jangwani Station Traffic Influencing Events 149 Appendix A / Part A1 - Survey Methodologies & Results A1.3 Kinondoni Station –– Uncontrolled on street parking often reduces Kinondoni station area is mainly residential, although available street width and causes traffic slowdowns. This happens in particular along Ikungi Street the streets intersecting the corridor are characterised at the intersection with Kawawa Road. by a moderate number of commercial activities. Most of –– The lack of dedicated loading and unloading the roads, located beyond an approximate distance of bays causes bottlenecks, especially when 50-60m from the corridor are unpaved and in urgent need heavy good vehicles are involved. This takes of rehabilitation. This condition prevent traffic flows from place in Karago Street and Kawawa Road. entering the area Therefore, poor road conditions are a –– High pedestrian activity and random serious issue that prevent a normal traffic flow in the local pedestrian crossings in Togo Street, Ikungi streets. Street and Karafuu Street forces traffic to slow down generating long traffic queues. –– Poor road conditions heavily affect the whole –– Non signalized traffic intersections between Kasaba area, hindering the accessibility on local roads. Street and Kawawa Road and between Kinondoni The lack of an efficient drainage system forces Road and Togo Street cause high level of congestion. vehicles to slow down to avoid potholes and –– Random dala dala stop in Kasaba Street obstruct puddles or even prevent their access to the area. the street forcing the traffic flow to slow down. 1. Random Dala Dala Stops 2. Random Pedestrian Crossings 3. Road Accidents/ Vehicle Breakdowns P 4. Illegal Parking/Double Parking/Parking Action 5. Not Signalized Traffic Intersection 6. Informal Trading Activity 7. Poor Road Condition 8. Loading/Unloading Activities 9. Heavy Vehicles Transit 10. High Pedestrian Activity Route 20 Figure A1.3-79  Kinondoni Station Traffic Influencing Events Route 21 Appendix A / Part A1 150 A1.3 Non regulated on street parking encroaches the street side and reduces the space for a fluid circulation Heavy vehicle transit on a local road Poor road conditions within local neighbourhoods 151 Appendix A / Part A1 - Survey Methodologies & Results A1.3 Morocco Terminal Morocco Terminal, located at the crossing point between –– Poor road conditions affect many residential streets, Kawawa Road, Bagamoyo Road, Mwai Kibaki Road and Ali in particular Lenitatu Street, Komostavu Street, Hassan Mwinyi Road, is an area that is currently undergoing Magangamwanza Street and Kananga Street. a process of urban renewal with a predominant land use –– Unregulated traffic intersections are the main cause function of employment centres. Along with that, the area is of congestion between the following roads: also characterized by the presence of the Open University of • Mwai Kibaki Road and Migombani Street Tanzania and the US Embassy. The fine grained residential • Bagamoyo Road and Chato Street fabric is concentrated mainly in the southern end of this area, marked by low density urban blocks while the northern • Bagamoyo Road and Ursino Street end shows higher densities and larger lots. The main causes • Donga Street and Isere Street of congestion in this area are represented by poor road • Kawawa Road and Bolingonanga Street conditions (in the residential area) and the presence of non- –– Heavy vehicle traffic affects mainly the intersections signalized traffic intersections at the main intersections. between local roads (Bolingonanga Street and Lenitatu Street) and the main corridor (Kawawa Road). 1. Random Dala Dala Stops 2. Random Pedestrian Crossings 3. Road Accidents/ Vehicle Breakdowns P 4. Illegal Parking/Double Parking/Parking Action 5. Not Signalized Traffic Intersection 6. Informal Trading Activity 7. Poor Road Condition 8. Loading/Unloading Activities 9. Heavy Vehicles Transit 10. High Pedestrian Activity Route 20 Figure A1.3-80  Morocco Station Traffic Influencing Events Route 21 Route 22 Appendix A / Part A1 152 A1.3 The lack of sidewalks forces pedestrians to walk on the street. The presence of open culverts raises critical road safety issues Poor road conditions make transit difficult 153 Appendix A / Part A1 - Survey Methodologies & Results A1.3 Manzese Station Manzese area is strongly characterised by an informal, –– Heavy vehicles transit generates friction with fine grained residential fabric as well as a high amount of pedestrian, paratransit and local traffic. They randomly informal commercial activities, workshops deployed along stop where needed for loading and unloading activities narrow and unpaved streets. Hence, loading and unloading along Morogoro Road and on the main streets that intersect the corridor. The narrow road section activities become very difficult and create congestion, makes these operations very difficult and represent especially when heavy vehicles are involved. Informal a safety issue for pedestrian and local inhabitants. traders occupy the sides of Morogoro Road, often preventing –– Random dala dala stops on Morogoro Road slow the access to secondary/local roads. the traffic down, especially in close proximity of the BRT stops where a high pedestrian footfall occurs. –– Informal trading activities represent the predominant traffic influencing events. Located along the main road corridor as well as on the parallel side streets, informal traders occupy the entire public realm facing the buildings along the road preventing pedestrians and vehicles from accessing the streets. 1. Random Dala Dala Stops 2. Random Pedestrian Crossings 3. Road Accidents/ Vehicle Breakdowns P 4. Illegal Parking/Double Parking/Parking Action 5. Not Signalized Traffic Intersection 6. Informal Trading Activity 7. Poor Road Condition 8. Loading/Unloading Activities 9. Heavy Vehicles Transit 10. High Pedestrian Activity Route 11 Figure A1.3-81  Manzese Station Traffic Influencing Events Route 12 Appendix A / Part A1 154 A1.3 Unpaved streets, lack of sidewalks and drainage system Informal traders occupy the sidewalks, while on street within local settlements parking generates frictions with pedestrians The lack of sidewalks and spontaneous loading/unloading High pedestrian activity on the street due to the presence of activities force pedestrians to walk on the roads informal trading activities 155 Appendix A / Part A1 - Survey Methodologies & Results A1.3 Top: No separation between public realm and vehicular domain. In some cases this condition has to be preserved. Middle: The lack of sidewalks forces pedestrians to walk on the street. Kibo Station Kibo station is located in a residential suburb area. Roads are not paved and most of them are not accessible from the main corridor (Morogoro Road) –– Poor road conditions affect the entire local street network, even preventing the access in some cases. –– The lack of sidewalks due to narrow street conditions forces pedestrians to walk on the road. Accordingly, the vehicles need to slow down creating queues and decreasing the level of service of the road. –– The corridor side street is affected by illegal parking that occupies the carriageway. 1. Random Dala Dala Stops 2. Random Pedestrian Crossings 3. Road Accidents/ Vehicle Breakdowns P 4. Illegal Parking/Double Parking/Parking Action 5. Not Signalized Traffic Intersection 6. Informal Trading Activity 7. Poor Road Condition 8. Loading/Unloading Activities 9. Heavy Vehicles Transit 10. High Pedestrian Activity Route 14 Figure A1.3-82  Kibo Station Traffic Influencing Events Route 15 Appendix A / Part A1 156 A1.3 Baruti Station Baruti station is very similar to Kibo station area. The low- density residential fabric spreads along narrow local streets and paths that are often not accessible due to the poor road conditions. In these cases the access is possible only on foot. –– Informal trading activities are located on Morodoro’s side road and mainly around the BRT station areas. Here, dala dalas stop randomly to pick up and drop off passengers invading the public realm. –– Only small vehicles, as bajaji or motorbikes, can access the local roads due to their bad conditions. The lack of sidewalk space forces pedestrian to walk in the street 1. Random Dala Dala Stops 2. Random Pedestrian Crossings 3. Road Accidents/ Vehicle Breakdowns P 4. Illegal Parking/Double Parking/Parking Action 5. Not Signalized Traffic Intersection 6. Informal Trading Activity 7. Poor Road Condition 8. Loading/Unloading Activities 9. Heavy Vehicles Transit 10. High Pedestrian Activity Route 16 Figure A1.3-83  Baruti Station Traffic Influencing Events Route 17 157 Appendix A / Part A1 - Survey Methodologies & Results A1.3 Kimara Terminal Kimara Terminal is an important gateway for the city of Dar es Salaam, as the feeder line connects it to Mbezi and to other suburbs. Hence, the pedestrian activity is generally very high given also the vast amount of street traders settled in the station area. Due to the lack of designated park and ride facilities, informal parking lots were created in the areas next to the corridor. –– Two side roads connect local developments –– Loading and unloading activities are managed with Morogoro Road. In general those side with difficulty on narrow and badly connected roads are in very poor condition and make streets. Furthermore, the lack of sidewalks access to the local settlements very difficult. forces pedestrian to walk on the carriageways creating several road encroachments. –– Access to informal parking facilities is poor and badly connected to Morogoro Road, causing major traffic slowdowns. 1. Random Dala Dala Stops 2. Random Pedestrian Crossings 3. Road Accidents/ Vehicle Breakdowns P 4. Illegal Parking/Double Parking/Parking Action 5. Not Signalized Traffic Intersection 6. Informal Trading Activity 7. Poor Road Condition 8. Loading/Unloading Activities 9. Heavy Vehicles Transit 10. High Pedestrian Activity Route 18 Figure A1.3-84  Kimara Station Traffic Influencing Events Route 19 Appendix A / Part A1 158 A1.3 Loading and unloading activities on the street create friction with pedestrians. Informal trading activities and random paratransit pick up Dala dala drop off and pick up points happen along the side points occupy the sidewalks disrupting pedestrian flows road causing difficult access to/from the main road corridor Unregulated surface parking areas often create traffic congestion due to parking manoeuvres along the side of the Mixed circulation of vehicles and pedestrians on through corridor traffic roads, increasing the friction between transport means. 159 Appendix A / Part A1 - Survey Methodologies & Results A1.3 5. Recommended Priority Links Looking at the surveyed station areas, some links emerge Within the road network of Magomeni Mapipa Station area, for their primary importance in the road network, carrying some roads appear more relevant to be improved and a higher traffic flow or connecting strategic locations. For upgraded: this reason, is it possible to identify them as “recommended –– Mikumi Street, Outbound Road, Chemchem Street, priority links”, namely as road portions whose upgrade will Mwinyimkuu Street to facilitate the circulation generally have an important impact on the traffic conditions around the existing residential areas. of the considered area. –– Kondoa Street to provide a structural connection parallel to the corridor. In Gerezani Terminal area, these links are: –– Mkauini Street and Jaribuo Street to –– Uhuru Street, Shauri Moyo Street and Lindi improve local connectivity. Street, whose enhancement will facilitate through traffic and improve road safety. As for Magomeni Mapipa, Morocco Terminal and Kinondoni –– Muheza Street, Tandmuti Street, Swahili Street Station areas the following links should be prioritized: to allow a smoother destination traffic. –– Lenitatu Street, Isere Street, Togo Street, to improve the connectivity within the local residential areas. In the case of City Council Station, the improvement of –– Donga Street, Mount Atlas Road, Ikungi Street, particular roads links will mitigate road safety and traffic Karafuu Street, Kinondoni Road as main links management issues, in particular in: crossing the Kawawa road corridor. –– Nkrumah Street, Samora Avenue, Maktaba Street, Azikiwe Street as main collectors. Regarding Manzese Station area, the main links connecting –– Zanaki Street, Jamhuri Street, India Street to the corridor with the surrounding areas can be considered as improve the circulation inside the city centre area. the ones that should be prioritized given their strategic role. From Ubungo Maji to Kimara the current local road network Fire Station area has to be analysed in relation to Kariakoo does not offer a consistent base to make a comprehensive market and will benefit from the enhancement of the assessment on recommended priority links critical to an following links: overall redesign process of the station area. –– Twiga Street, which will improve cross connectivity parallel to the corridor (influencing also Jangwani area). –– Swahili Street, to ensure a better connection with Kariakoo market area. –– United Nation Road and Mazengo Road as collectors for the traffic towards Upanga. Appendix A / Part A1 160 A1.3 Magomeni Mapipa Jangwani Fire City Council Gerezani Morocco Manzese Kinondoni Figure A1.3-85  Recommended Priority Links Within the Surveyed Station Areas 161 Appendix A / Part A1 - Survey Methodologies A1.3 Conclusion These conclusions comprise some of the highlights and key Roadside interview surveys enables the obtaining of the findings of the mobility and transport surveys. All lessons following results: learned from the surveys will feed and integrate the current • Traffic composition during morning period are baseline analysis as well as informing the next planning composed of 16% cars and 84% motorbikes. phase on the two station areas. • Road users occupation mainly composed of Traffic volume surveys enables the obtaining of the following workers (self-employed & employed) results: • Main purpose based trips during morning period • Morning peak hour is 07:00 -08:00 AM and are work based (73%) trips, a considerable evening peak hour as 05:00 – 07:00 pm. portion 12% are home based and the rest are school/shopping/other based trips. • Definition of traffic flow patterns during AM and PM peak hours. It was observed that major flows are • Main starting point of the trip during morning going from West (Kimara) to East (city center) during period starts from home (69%), a small portion morning AM peak hour. Whereas, during evening at work places (24%) and rest for other. PM peak hour, high flows are observed coming from • The frequency of the trips during morning East (city center) to West (Kimara). Similar traffic period are as follows: 83% of daily trips flow patterns are observed for both weekday and and 17% of 2/3 times trips per week. weekend day, but the volume of traffic flows are less during weekend day compared with weekday. • Average journey times during morning period is 50 minutes. • Traffic flow compositions on a weekday AM peak hour, the average share of light and heavy vehicles at the surveyed locations are namely 82% and 18% simultaneously and during PM peak hour, the share has improved for heavy vehicles up to 25%. Similarly, on a weekend day PM peak hour, the average share of light and heavy vehicles at surveyed six locations are 80% and 20% simultaneously and during PM peak hour, the share has improved for heavy vehicles up to 25%. Appendix A / Part A1 162 A1.3 Transit passenger interview surveys enables the obtaining Video survey enables the obtaining of the following results: the following results: • BRT buses leaving from Kimara towards CBD passing • Modal share during morning AM period are mainly by Tiptop Station are 77 numbers, out of which walking and public transport (daladala & bus) to reach 11 are express buses and 66 are local buses. Total BRT station. Whereas, for afternoon PM period are BRT buses leaving from CBD towards Kimara are mainly public transport (daladala) to reach the BRT. passing by Tiptop Station are 78 numbers, out of which 30 are express buses and 48 are local buses. • During morning AM period, longest travels are registered to reach Gerezani terminal when • Headway of BRT buses leaving from Kimara towards CBD compared to others. Whereas, during afternoon are passing by Tiptop Station is 47 seconds, for total of PM period, longest travels are happening to reach 77 buses per hour. The headway of BRT buses leaving Ubungo terminal when compared with others. from CBD towards Kimara are passing by Tiptop Station is 46 seconds, for total of 78 number bus per hour. • Desire lines during morning AM period, Gerezani Terminal is characterised by longest trips (desire lines) coming • During certain time periods within the peak from several wards located south of the city. Whereas, hour, BRT buses are queuing up. during PM period trips are shorter and less in terms of • It was observed, pedestrians are not only crossing in number of trips originating from different small ward the designated area, but also all along the corridor. (origins), in particular from city the centre (CBD area). • Estimated average catchment area distance from GPS surveys enables the obtaining of the following results: station location as 1000m with a travel time of 15mins. • Travel time to reach Kimara Terminal from Gerezani Terminal is about 45 minutes with an average speed ranging from 18 km/h to 22 km/h. Traffic Congestion Study As a general conclusion, it is possible to state that the –– Furthermore, many local roads in the suburban traffic flows on the local roads feeding into Morogoro areas are disconnected from the corridor and the access or vehicular circulation is very difficult Road is not subject to serious congestion issues, but the and in some cases, completely obstructed. overall accessibility and local circulation is rather difficult throughout all thirteen surveyed station areas. –– To grant an immediate alleviation of the traffic conditions and related causes the local road network is in urgent need of rehabilitation in terms of road Main causes are described as follows: paving, road section enhancements, together with a proper traffic management (intersection signalization –– The poor condition of the local network is a permanent and access regulation strategies for freight traffic). and structural limit that prevents traffic to run smoothly. In fact, bottlenecks often occur because This would also support the benefits of urban vehicles have to stop to avoid potholes while driving agglomeration, such as jobs creation and connectivity to or slow down to approach an unpaved road. On the social services, while reducing negative outcomes, such other hand, the friction with pedestrian activities as sprawl, informal settlements, traffic, and environmental represent a challenge for drivers and a frequent cause degradation. These efforts will improve city efficiency and traffic influencing event. Both, the lack of designated sidewalks and the cultural behaviour of using the global competitiveness of Dar es Salaam. roads as an integrated part of public realm makes road encroachment a recurring problem and a safety issue. 163 Appendix A / Part A2 - Baseline Methodologies  Appendix A / Part A2 164  Appendix A Part A2 Baseline Methodologies 165 Appendix A / Part A2 - Baseline Methodologies A2.1 General Information Introduction Information Sources Appendix A Part A2 covers the methodology used to Data was provided by the client at the commencement produce our baseline understanding and assessment of the works and was complemented by specific data of the current and planned urban condition in provided by local authorities and service providers Dar es Salaam. The resulting maps and evaluations where needed. Although the team has identified are part of Chapter 3.0 Corridor Baseline Assessment data gaps throughout Stage 1, they have not been and support the findings in Chapter 5.0 Diagnosis of able to mitigate all of these and are still awaiting the Existing Conditions and Development Pressures. further complete data packages. Each item details the The project team including all consultants, Broadway information source used each individual processes. Malyan, Mobility in Chain, Aurecon and CoLab At the beginning of Stage 1, Dar es Salaam had Consulting, developed the applied methods. been recently reorganized into five municipalities. Therefore, all works explained in this section are relative to the original three municipalities (Kinondoni, Ilala and Temeke) but will be updated for Stage 2 works. Kinondoni Kinondoni Ubungo Ilala Temeke Temeke Kigamboni Ilala Figure A.2.1 Old Municipal Boundaries (up to 2015) Figure A.2.2 New Municipal Boundaries (since 2015) Appendix A / Part A2 166 Broadway Malyan (Urban Planning) A2.2 Methodology Character Zone Plan Land Use Plan Drawing Numbers: DMDP-BM-01-003 to004 Drawing Numbers: DMDP-BM-01-005 to 008 Methodology: Through the analysis of the Methodology: The project team adapted the land photographic documentation produced during a use shapefile to the municipalities’ boundaries, low-level flight over the city to survey BRT corridors combined the municipalities’ shapefiles and identified along with satellite imagery and land uses shapefile, three common levels of land use. The land use areas the team classified all wards into six character areas: were further cut to match their ward boundaries, had Central Business District (CBD), city centre, and urban, irrelevant attributes deleted, duplications and area peri-urban and rural areas. The classification took overlaps eliminated. into consideration the predominant land uses, The land use classifications was found to range from level of urban consolidation and building typology. general to more detailed activity classifications; Character areas attributes were added to the ward however, many of the original codes were incomplete boundary and land use shapefiles. or were non-standard entries. The coding present Sources:: Photographic documentation of an aerial in each level was analysed and converted in order citywide survey from January 2017 by Broadway to standardize the information across the three Malyan; (2) Satellite imagery with 1m resolution municipalities, as shows the following tables. provided from ArcGIS World Imagery combined from The team ran a check across the three levels Esri, DigitalGlobe, GeoEye, Earthstar Geographics, of land use to ensure classification coherence. CNES/Airbus DS, USDA, USGS, AEX, Getmapping, A visual check was done based on satellite imagery, Aerogrid, IGN, IGP, swisstopo, and the GIS User especially for misclassified residential areas. Community updated in May 2017; (3) Dar es Salaam The team used the Dodi Moss Dar es Salaam Master land use classification shapefile by Broadway Malyan; Plan (2012-2032) provided by the client to check and (4) ward boundaries shapefile provided by the mixed use areas in the Central Business District client. (CBD). This resulted in a unified shapefile with land Issues: The classification into six character areas uses for the Dar es Salaam area. Each land use parcel represent a general characterization in order to aid our has an individual coding that specifies its municipality, understanding of Dar es Salaam’s present structure. ward and its three levels of land use. As a generalization, it excludes territorial specificities. Land Use Level 1 Code Description 1 Planned 2 Unplanned 3 Un-built Up 4 Water Bodies Table A.2.1 Land Use Level 1 167 Appendix A / Part A2 - Baseline Methodologies A2.2 Land Use Level 2 Sources: (1) Kinondoni, Ilala and Temeke Original From municipality land use and boundary shapefiles Conversion Municipalities provided by the client; (2) Satellite imagery with Code Code Description 1m resolution provided from ArcGIS World Imagery 1, 1U 1 Residential combined from Esri, DigitalGlobe, GeoEye, Earthstar 2 2 Commercial Geographics, CNES/Airbus DS, USDA, USGS, AEX, 4, 4M 3 Institutional Getmapping, Aerogrid, IGN, IGP, swisstopo, and 7, 7H 4 Industries the GIS User Community updated in May 2017; 7.5, 8 5 Transport and Utilities (3) ward boundaries shapefile provided by the client; 9, 9A, 9F 6 Open Space (4) Dar es Salaam City Master Plan 2012-2032 from the 10, 12 7 Water Bodies Ministry of Lands, Housing and Human Settlements 11 8 Mining Development in consortium with Dodi Moss, Buro Happold, Afri-Arch Associates and QConsult. Table A.2.2 Land Use Level 2 Code Conversions Issues: The provided land use shapefile does not correctly match with the satellite image and road Land Use Level 3 overlay. Whilst the team has attempted to calibrate Original Conversion the land-use layer, including making adjustments in some wards where we have identified significant Code Code Description errors, the team has not been able to correct the A A Residential Building overall errors. Land use classification may contain B, U B Special Residential outdated classifications that were not fully updated by C C Retail and F&B the checks ran by the team. At a local level, there are C1 C1 Hotel significant mismatches between the land-use zones, CC CC Offices areas and boundaries, the right of ways, land areas and CCC CCC Commercial Leisure edges, which can observed on the more accurate GIS D D Workshops satellite imagery. E E Local Market The following figures samples are extracts from the E1, EEE EE Fuel and Energy Services GIS output to help illustrate the issue. The team has F F Mixed Use confidence in the information and our assumptions G, 3 G Public Building at a city wide level, however the identified errors will H H Community Facilities become problematic at the more detailed BRT line I, 1 I Religious 1 corridor scale at Stage 02. J J1 Indoor Leisure J J2 Outdoor Leisure K K Education Building Heights L L Wholesale Warehouse M M Service Trade Drawing Numbers: DMDP-BM-01-015 to 016 N N Special Industries Building inventory also impacts P P Transport Terminal Facilities drawings DMDP-BM-01-011 to 012 Q1, Q2 Q3, Q4 Q Communications & Public Works Methodology: The bases for this item was a R1, R2, R3 R Agriculture building footprint shapefile for Dar es Salaam S1 S Leave ways for Public Utilities retrieved from Open Street Map – OSM on February 2nd, 2017. The team analysed the shapefile’s T1, T2, T Ecological Fragile Lands attributes and eliminated information and entities T3, T4, 10 that misrepresented building footprints through U2 U Shooting Ranges automated queries and visual analysis using satellite W W Cultural and Historical Sites imagery. Duplications and overlays were eliminated, U2 Z Military and missing building footprints inside a 1km buffer Table A.2.3 Land Use Level 3 Code Conversions for the BRT phase 1 network were drafted and added. Appendix A / Part A2 168 A2.2 Land Use Level 1 Land Use Level 2 Land Use Level 3 General Land Use Grouping Code Description Code Description Code Description Description 1 Planned A Residential Building Planned Residential 2 Unplanned A Residential Building Unplanned Residential 1 Residential B Special Residential Special Residential F Mixed Use Mixed Use C1 Hotel Hotel C Retail and F&B Retail, F&B and Local Market E Local Market 2 Commercial EE Fuel and Energy Services Fuel and Energy Services CC Offices Offices D Workshops Workshops Z Military Military G Public Building Public Building K Education Education 3 Institutional H Community Facilities Community Facilities 1 I Religious Religious Planned / W Cultural and Historical Sites Cultural and Historical Sites 2 Unplanned / L Wholesale Warehouse Wholesale Warehouse Un-Built up 4 Industries M Service Trade Service Trade 3 N Special Industries Special Industries Communications Q Communications & Public Works & Public Works Transportation 5 P Transport Terminal Facilities Transport Terminal Facilities and Utilities Leave ways for S Public Utilities Public Utilities 6 Open Space R Agriculture Agriculture U Shooting Ranges 6 Open Space Leisure, Sport and Recreation J Outdoor Leisure Open Space/ Water 6/7/8 T Ecological Fragile Lands Ecological Fragile Lands Bodies/Mining 4 Water Bodies 7 Water Bodies T Ecological Fragile Lands Water Table A.2.4 Land Use Grouping 169 Appendix A / Part A2 - Baseline Methodologies A2.2 Figure A.2.5 Sample from the City Centre Figure A.2.6 Sample from Tip Top Figure A.2.3 Sample from the City Centre Figure A.2.4 Sample from Tip Top Appendix A / Part A2 170 A2.2 The team then imported character area and land use Residential Density classifications into the building footprint. As there were gaps between land use polygons Drawing Numbers: DMDP-BM-01-009 to 010 due to misaligned road buffers present in the Methodology: Net residential densities were based original file provided by the client, land uses on the 2017 Population Projection estimated by CoLab, were assigned according to: (a) geometry with its which was based on the 2012 Census population by centroid inside of a land use polygon, (b) geometry ward. The team calculated each land use parcel’s total intersected by a land use, and (c) land use imported residential GFA by selecting those with land use level from adjacent buildings within a 50m radius. 2 code “1 – Residential.” For land use level 3 code F – Roughly 40% of the building footprints from OSM had Mixed Use, we assumed a ratio of 50:50 residential and information regarding its number of floors, which commercial activity, which will be subject to further the team checked using satellite imagery and aerial refinement. Therefore, half of the F – Mixed Use GIA survey photographs. For the remaining 60%, the team was used in the residential calculation. The team then divided the ward population by the land use parcel calculated and assigned an average of the number of area, resulting in a net population density. floors per character area. Each of the building’s Gross Floor Area (GFA) were calculated though the building Sources:: (1) Population Projection for 2017 from footprint area and number of floors, except for those CoLab; (2) Dar es Salaam land use classification smaller than 10 square meters. These smaller polygons shapefile by Broadway Malyan; and (3) Dar es Salaam were excluded from the GFA calculation due to the building inventory by Broadway Malyan. probability that these do not shelter the assigned use. Issues: Net residential densities are difficult to calculate with precision in a citywide scale without Sources:: (1) Building footprints and heights from cadastral documentation or official population Open Street Map (openstreetmap.org); (2) Satellite statistics. The shown density are estimations using the imagery with 1m resolution provided from ArcGIS data available at this stage’s time of study. World Imagery combined from Esri, DigitalGlobe, GeoEye, Earthstar Geographics, CNES/Airbus DS, USDA, Employment Density USGS, AEX, Getmapping, Aerogrid, IGN, IGP, swisstopo, Drawing Numbers: DMDP-BM-01-011 to 012 and the GIS User Community updated in May 2017; (3) Photographic documentation of an aerial citywide Methodology: Employment densities are based on survey from January 2017 by Broadway Malyan; the estimation and distribution of formal and informal (4) Dar es Salaam land use classification shapefile by employment in land use parcels. The team calculated Broadway Malyan; and (5) ward boundaries shapefile Gross Internal Area (GIA) as 80% of the GFA for land provided by the client. use level 3 codes except A – Residential Building, B – Special Residential, R – Agriculture, T – Ecological Issues: CBD, city centre and urban character areas Fragile Lands and Z – Military, and applied a rate of GIA have adequate building coverage. Peri-urban wards as in sqm per worker according to the land use. Kiburugwa, Charambe and Buza in Temeke, Kipawa in Ilala and Kimara and Kunduchi in Kionondoni have gaps in the building inventory coverages. Few rural wards have satisfactory coverage, although with gaps, as in Mjimwema, Vijibweni, Kibada, Kijichi and Mbagala Kuu in Temeke; Ukonga and Kinyerezi in Ilala. In some areas, the building shapefile did not match the land use, such as the existence of building clusters on land uses classified as open spaces, not residential. Although this shapefile cannot substitute cadastral data the building inventory, it is adequate for a citywide assessment. 171 Appendix A / Part A2 - Baseline Methodologies A2.2 Table A.2.5 shows the employment densities Urban Living Condition used for the employment estimation per land use levels. For land use level 3 code F – Mixed Use, we Drawing Numbers: DMDP-BM-01-013 to 014 assumed a ratio of 50:50 residential and commercial Methodology: The team combined residential activity, which will be subject to further refinement. density, infrastructure coverage and illiteracy index to Therefore, half of the F – Mixed Use GIA was used in the indicate areas that might present poor or degraded employment calculation. In order to capture additional living situation. The premise is that the more formal jobs within residential areas (Land Use Level 3, dense, the higher the illiteracy index and the lesser Codes A, B and F), we assumed that 5% of the planned infrastructure coverage, generate areas that have residential parcel area had a 25 GIA sqm per worker. Additionally, we captured the informal job distribution precarious living conditions. All three elements were by considering a percentage of the unplanned normalized for values between 0 – 1. residential area according to it character area with The team inversely normalized the infrastructure a 5 GIA sqm per worker (Table: Employment Density coverage, as poor living conditions have lower, not Calculation). higher access to infrastructure. After the normalization, Our estimations targeted the Employment Growth each area was scored based on the sum of the three Projections from CoLab for 2015, which assumed elements and normalized so it would be within a 0 – 715,000 formal jobs and 1,250,000 informal jobs, which 1 range for mapping. will be further estimated for 2017. Sources: (1) Dar es Salaam land use classification Sources:: (1) Formal Sector Employment and shapefile by Broadway Malyan; (2) Infrastructure Heat Earnings Survey 2015, National Bureau of Statistics, Tanzania, 2016; (2) Measuring Living Standards within Map by Aurecon; (3) Illiteracy index per ward 2012, Cities, World Bank, 2016; (3) Employment Projection National Bureau of Statistics, Tanzania. for 2015 from CoLab; (4) Dar es Salaam land use Issues: A combination of socio-economic indicators classification shapefile by Broadway Malyan; and that would cover health, security, education, (5) Dar es Salaam building inventory by Broadway employment and income would give a more complete Malyan. assessment of the area. However, data gaps prevent Issues: Employment densities are over-simplified this analysis across all wards. and should be used as guidance only. High levels of informal employment along with lack of employment density benchmarks for African cities corroborate the estimation character of this exercise. The distribution of informal jobs in unplanned residential areas, informal jobs allocation in planned residential areas, as well as the mixed-use ratio are subject to further study and calibration. Appendix A / Part A2 172 A2.2 Plot Analysis Drawing Numbers: DMDP-BM-01-017 to 018 All transformations were carried out with a zero root mean square error. Methodology: Plots and parcel files provided by the municipal councils were processed to better Sources: (1) Plot and parcels shapefiles provided by match the 2016 Ministry of Lands, Housing and the client; (2) 2017 MLHHSD Aerial Imagery. Human Settlements Development (MLHHSD) aerial Issues: The disparity between the surveyed photography. In order to align the separate plot and plots and the parcelization observed in the aerial parcel pieces, shapefile portions that were more photography might indicate that the surveyed plot similar to the aerial photography were separated and shapefiles are incomplete or outdated. The amount of combined with other portions underwent spatial void areas in areas classified as “planned” by our land adjustment transformations. All portions spatially use classification level 1(BM-MP-005) disables the use adjusted to the aerial photograph had no loss in area. of this analysis in the study area. Land Use Level 2 Land Use Level 3 Employment Density GIA m² / Code / Description Code Description Notes Worker A Residential Building - 25 GIA m² / worker within planned parcels 1 Residential B Special Residential - 5 GIA m² / worker within unplanned parcels F Mixed Use 11 50:50 Residential and Commercial Ratio C Retail and F&B 16 C1 Hotel 24 CC Offices 11 2 Commercial CCC Commercial Leisure 52 D Workshops 31 E Local Market 8 EE Fuel and Energy Services 36 G Public Building 20 H Community Facilities 36 I Religious 36 3 Institutional J1 Indoor Leisure 36 K Education 36 W Cultural and Historical Sites 36 Z Military - L Wholesale Warehouse 56 4 Industries M Service Trade 39 N Special Industries 36 P Transport Terminal Facilities 36 5 Transport Q Communications & Public Works 36 S Leave ways for Public Utilities 52 J2 Outdoor Leisure - Open R Agriculture - 6 Space T Ecological Fragile Lands - U Shooting Ranges 36 Table A.2.5 Employment Density Calculation 173 Appendix A / Part A2 - Baseline Methodologies A2.3 Mobility in Chain (Transport) Methodology Most of the map components were retrieved as shapefiles. Any evident gap in the transport network has been filled by retracing manually the networks represented within the analysed baseline documents. The team can assume that all maps representing current transport networks contain a complete set of information whereas this can not be stated totally for the planned networks, since in some cases still subject to an official approval by the planning authorities. Current Road Network Hierarchy Planned Road Network Hierarchy Drawing Numbers: Drawing Numbers: DMDP-MIC-01-001 DMDP-MIC-01-002 and DMDP-MIC-01-007 Methodology: Starting from OpenStreetMap, we Methodology: Starting from the current road have cross-checked the data with the shapefiles network base, JICA’s document was used as provided by the client. Furthermore, the road network the reference to draw the planned new main has been analysed and verified by checking roads and highlight the planned upgrades. JICA’s documents, which were also used as Furthermore, the team checked this information with reference to define the main road hierarchy. the World Bank data. Sometimes overlaps occurred More specifically, the diagram showing the road between the two sources regarding local road condition derives from OpenStreetMap information, upgrades, but the team kept JICA as the leading source while the number of lanes diagram is based on the and filled the gaps with the World Bank information. analysed JICA’s documents. In particular, the outer ring road alignment was drawn Sources:: (1) Basic structure of current road and retracing JICA future road network map. railway network from OpenStreetMap; (2) World Bank; Sources:: (1) Basic structure of current road and (3) Dar es Salaam Transport Policy and System railway network from OpenStreetMap; (2) Planned road Development Master Plan - Summary, JICA, June upgrades by World Bank; (3) The project for revision of 2008; (4) Dar es Salaam Transport Policy and System Dar Es Salaam Urban Transport Masterplan in United Development Master Plan - Technical Report Republic of Tanzania - Outline and Progress of Work, 1 Urban and Regional Planning, JICA, June 2008; JICA, 10th April 2017. (5) Dar es Salaam Transport Policy and System Issues: Road alignments that are still waiting for an Development Master Plan - Technical Report official approval might be subject to further changes. 7 Transport Modelling & Demand Forecast, JICA, June 2008; (6) The Project for Revision of Dar es Salaam Urban Transport Master Plan in United Republic of Tanzania - Inception Report, JICA, November 2016. Issues: The local road network lacks of details: fine-grained local road network and pedestrian links are missing. Appendix A / Part A2 174 A2.3 Current Public Transport Network Planned Public Transport Network Drawing Number: DMDP-MIC-01-003 Drawing Number: DMDP-MIC-01-004 Methodology: OpenStreetMap data has served Methodology: The planned public transport network as the network base that has been progressively map derives from the union between the current checked and refined by comparing it with the analysed transport network and the future documents. In particular, the railway network has BRT Phases shapefiles provided by DART. been categorized into commuter, national and freight Phase 2-3 alignment was adjusted according to Kyong services. Daladala routes have been added to the Dong document. Phase 4 was extended to Tegeta OpenStreetMap network by redrawing them following following DART indications. Phase 4-5-6 stations were the Kyong Dong document. In the same way, the long manually added and they have been approximately distance ferries path has been drawn on the map positioned each 500m. checking the routes. Furthermore, the BRT Phase 1 route wrongly continued beyond Kimara station and Sources: (1) Basic structure of current road and so it was shortened. railway network, daladala network, BRT Phase 1, ferry routes from OpenStreetMap; (2) DART Agency; Sources:: (1) Basic structure of current road and (3) World Bank; (4) Consulting services for design of railway network, daladala network, BRT Phase 1, 42.9 km of Bus Rapid Transit system Phase 2 and 3 in ferry routes from OpenStreetMap; (2) DART Agency; Dar Es Salaam city - 2.2 Operational plan report, Kyong (3) World Bank; (4) Consulting services for design of Dong. 42.9 km of Bus Rapid Transit system Phase 2 and 3 in Dar Es Salaam city - 2.1 Traffic Survey and Demand Issues: Phase 4-5-6 are subject to ongoing study and Forecasting report, Kyong Dong, January 2015. their alignments might be modified or adjusted. Issues: The document used as base to verify daladala Planned Railway Network routes might be outdated, because it is prior to Phase 1 implementation. Drawing Number: DMDP-MIC-01-005 Methodology: The planned railway network has been retraced on the current base network following GIBB’s document. Station location is approximate because the provided drawings were not precise enough to allow an accurate redrawing. Sources:: (1) Basic structure of current road and railway network, daladala network, BRT Phase 1, ferry routes from OpenStreetMap; (2) Dar es Salaam New Commuter Rail Project - Corridor Analysis Report Presentation, GIBB, 17th March 2016. Issues: This railway proposal is still under discussion and might be subject to further changes. 175 Appendix A / Part A2 - Baseline Methodologies A2.3 Current Public Transport BRT Phase 1 Pedestrian Network, Phase 1 Operational Catchment Area Services and Demand Drawing Number: DMDP-MIC-01-008 Drawing Number: DMDP-MIC-01-006 Methodology: The catchment area was built connecting all the paths that are located in a 500m Methodology: Ridership data derive from DART buffer centred in each station. The 1000m and 1500m monthly counts. The monthly counts, which cover catchments apply the same process to a wider area. a period of 8 months and 13 days, have been The aim of this map is to show the area that most divided into the corresponding number of days. likely refers to each station of the BRT corridor. As a result, we obtained a mean daily ridership for In fact, 400-500 m is generally considered an each month. The average between all the mean daily acceptable distance for people to walk to a public ridership gave as a result the average daily ridership. transport station, even if it has to be noted that the Sources:: (1) Basic structure of current road and disposition of walking depends also on other factors railway network, daladala network, BRT Phase 1, as climate, people characteristics, purpose of the ferry routes from OpenStreetMap; (2) DART Agency; trip, characteristics of the environment, mode of (3) World Bank; (4) Consulting services for design of transport of the stop that has to be reached, among 42.9 km of Bus Rapid Transit system Phase 2 and 3 in others. The 1000m and 1,500m limit are indicated as a Dar Es Salaam city - 2.1 Traffic Survey and Demand reference distance for bicycle mobility. Forecasting report, Kyong Dong, January 2015. Sources:: (1) Basic structure of current road and Issues: The daily ridership is approximate and needs railway network, daladala network, BRT Phase 1, ferry a further check with the boarding and alighting data. routes from OpenStreetMap. Issues: The local road network lacks of details and fine-grained pedestrian links are missing. Appendix A / Part A2 176 A2.3 Isochronal Analysis, Private and Public Transport Accessibility Drawing Number: DMDP-MIC-01-009 Methodology: The isochronal analysis map shows areas defined by isochrones between different points. Isochrones lines connect the points of the different streets that can be reached in the same travel time. The whole area covers a 30 minutes travel time subdivided into six isochronal steps of 5 minutes. The analysis was run for both private and public transport mobility. Average speeds used for the isochronal analysis are based on MIC’s observations on similar contexts (i.e. analysis conducted in Luanda, Angola). Public transport average speeds are 21 km/h for BRT, 18 km/h for feeder route to Kimara, 12 km/h for daladala, 12 km/h for railway, 9 km/h for ferry and for pedestrian 4,5 km/h. Private transport, primary, trunk and secondary roads have an average speed of 15 km/h, residential, service, territory roads and local streets of 12 km/h, while bicycle is assumed to have an average speed of 9 km/h. Sources:: (1) Basic structure of current road and railway network, daladala network, BRT Phase 1, ferry routes from OpenStreetMap; (2) DART Agency; (3) World Bank; (4) Consulting services for design of 42.9 km of Bus Rapid Transit system Phase 2 and 3 in Dar Es Salaam city - 2.1 Traffic Survey and Demand Forecasting report, Kyong Dong, January 2015. Issues: The local road network lacks of details and many pedestrian links are missing. 177 Appendix A / Part A2 - Baseline Methodologies A2.4 CoLab (Socio-Economics & Real Estate) Methodology Unless stated otherwise, the source of the following maps is the Census 2012 provided by the World Bank obtained by the Municipal Councils. As data is shown by ward, the team was able to map it directly to the wards, and display a gradient of shading to reflect the levels of the various wards, relative to each other. The team assumed the census data has been verified and in its conversion into spreadsheet formats, has not been altered since post-collection processing. Internet Connectivity Population Densities Drawing Number: DMDP-COL-01-001 Drawing Number: DMDP-COL-01-003 Methodology: The team was given census data for Methodology: The team combined data for the raw internet connectivity in spreadsheet format, as part of population (as per drawing 002) with the area of the the data set entitled “Poverty Indicators”. wards, both of which we obtained in spreadsheet and shapefile format respectively, from the World Bank, via Issues: The main issue was the missing data. the Municipal Councils, to obtain population densities, Wards which did not have any data, as there were gaps for the wards. No accounting was made for whether which the team continues to work with the client to fill, or not land was habitable or not, or residential in were displayed with their ward name in the colour red. use, or not, during the calculation of these densities Raw Population Figures (they are calculated on the entire area of each ward.) Issues: Since the population and area data for the Drawing Number: DMDP-COL-01-002 wards were complete, there were no gaps in this Methodology: The team was given census data for mapping. the population in each ward in spreadsheet format. Employer-Owned Rental Housing, Issues: Since the population and area data for the wards were complete, there were no gaps in this Provided at no Rental Cost mapping. Drawing Number: DMDP-COL-01-004 Methodology: The team was given housing tenure for the population in each ward, in spreadsheet format. This was represented as a percentage of the total population living in this type of housing tenure situation. Issues: The main issue was the missing data. Wards which did not have any data, as there were gaps which we continue to work with the client to fill, were displayed with their ward name in the colour red. Appendix A / Part A2 178 A2.4 Employment Ownership of Landline Telephones Drawing Number: DMDP-COL-01-005 Drawing Number: DMDP-COL-01-008 Methodology: The team was given census data for Methodology: The team was given census data for employment in each ward, in spreadsheet format. the ownership of land-line telephones, in spreadsheet This was represented as a percentage of the total ward format, as part of the data set entitled “Poverty population. Answers were deemed to mean employed Indicators”. formally or informally. Issues: The main issue was the missing data. Issues: The main issue was the missing data. Wards which did not have any data, as there were gaps Wards which did not have any data, as there were gaps which the team continues to work with the client to fill, which the team continues to work with the client to fill, were displayed with their ward name in the colour red. were displayed with their ward name in the colour red. Sheet Metal Roofing High Educational Attainment Drawing Number: DMDP-COL-01-009 Drawing Number: DMDP-COL-01-006 Methodology: The team was given census data Methodology: The team was given census data for pertaining to the materials of residential roofs, in educational attainment in each ward, in spreadsheet spreadsheet format, as part of the data set entitled format. This was represented as a percentage of the “Poverty Indicators” . total ward population. Issues: The main issue was the missing data. Issues: The main issue was the missing data. Wards which did not have any data, as there were gaps Wards which did not have any data, as there were gaps which the team continues to work with the client to fill, which the team continues to work with the client to fill, were displayed with their ward name in the colour red. were displayed with their ward name in the colour red. Owner-Occupied Housing Illiteracy Drawing Number: DMDP-COL-01-010 Drawing Number: DMDP-COL-01-007 Methodology: The team was given housing tenure Methodology: The team was given census for the population in each ward, in spreadsheet format, data for educational attainment, which included as part of the data set entitled “Poverty Indicators”. literacy in each ward, in spreadsheet format. This was represented as a percentage of the total This was represented as a percentage of the total ward population living in this type of housing tenure population. situation. Issues: The main issue was the missing data. Issues: The main issue was the missing data. Wards which did not have any data, as there were gaps Wards which did not have any data, as there were gaps which the team continues to work with the client to fill, which the team continues to work with the client to fill, were displayed with their ward name in the colour red. were displayed with their ward name in the colour red. 179 Appendix A / Part A2 - Baseline Methodologies A2.4 Privately-Rented Housing Social Housing (Government- Drawing Number: DMDP-COL-01-011 Owned, Subsidised Rental) Methodology: The team was given housing tenure Drawing Number: DMDP-COL-01-013 for the population in each ward, in spreadsheet Methodology: The team was given housing tenure format. This was represented as a percentage of the for the population in each ward, in spreadsheet total population living in this type of housing tenure format. This was represented as a percentage of the situation. total population living in this type of housing tenure situation. Issues: The main issue was the missing data. Wards which did not have any data, as there were gaps Issues: The main issue was the missing data. which the team continues to work with the client to fill, Wards which did not have any data, as there were gaps were displayed with their ward name in the colour red. which the team continues to work with the client to fill, were displayed with their ward name in the colour red. Percentage of Population Employed as Professionals or Managers “Top Quality” Roofing on Housing Drawing Number: DMDP-COL-01-012 Drawing Number: DMDP-COL-01-014 Methodology: The team was given census data Methodology: The team was given census data for employment type in each ward, in spreadsheet pertaining to the materials of residential roofs, in format. This was represented as a percentage of the spreadsheet format, as part of the data set entitled total ward population – in this case those employed “Poverty Indicators”. at professional or managerial level. Answers were deemed to mean employed formally or informally. Issues: The main issue was the missing data. Wards which did not have any data, as there were gaps Issues: The main issue was the missing data. which the team continues to work with the client to fill, Wards which did not have any data, as there were gaps were displayed with their ward name in the colour red. which the team continues to work with the client to fill, were displayed with their ward name in the colour red. Unemployment Drawing Number: DMDP-COL-01-015 Methodology: The team was given census data for employment type in each ward, in spreadsheet format. This was represented as a percentage of the total ward population. Answers were deemed to mean the respondant was neither formally or informally employed. Issues: The main issue was the missing data. Wards which did not have any data, as there were gaps which the team continues to work with the client to fill, were displayed with their ward name in the colour red. Appendix A / Part A2 180 A2.4 Car Ownership Real Estate Rental Rates and Land Drawing Number: DMDP-COL-01-016 Value Series (Commercial, Residential, Retail, Industrial) Methodology: The team was given census data for the ownership of cars, in spreadsheet format, as part of Drawing Numbers: DMDP-COL-01-021 to 031 the data set entitled “Poverty Indicators”. Methodology: The team consulted Pangani Issues: The main issue was the missing data. Real Estate, a property consultant and sales agent Wards which did not have any data, as there were gaps in Dar es Salaam, on rental and sale rates for which the team continues to work with the client to fill, Dar es Salaam. Pangani was able to use their database were displayed with their ward name in the colour red. of rental rates and land sales, to give us a table of Uniform Population Growth average rates, for various key locations in the city. This data was mapped in GIS. The land value data Projections (2015, 2020, 2025, 2030) provided herewith relates to actual sales values – Drawing Numbers: DMDP-COL-01-010 to 020 not valuations for the purposes of taxation or rates. Methodology: The used the 2012 census data as The team assumes that the data assembled consists a baseline. Having reviewed available literature on of a representative sample of various different areas of growth projections for Dar es Salaam from African Dar es Salaam and is based on recent transactions. Development Bank, World Bank and UN Habitat we Sources: Pangani Real Estate concluded that the World Bank projection in 2014 Issues: Whilst the idea of obtaining more detailed was the most useful (World Bank www.worldbank. real estate information for Dar es Salaam, across more org/tanzania/economicupdate June 2014). areas and sectors is appealing, it has thus far proved Although there were variations in this projection, it taxing to obtain even this data, and as such, the team showed a relatively stable growth rate at a higher rate does not believe it would be cost effective to in relation than the UN Habitat figure. The team took the linear to the utility of this data to gather more. growth from this projection to be 5.65% per annum. We then applied this growth rate to the 2012 “raw Income Survey Series population” to project the population at five year (Sewerage Connection, Piped Water, intervals to 2032, across each ward of the city. Higher Education, Wages, Prosperity, Issues: Since the population and area data for the Professions, High Quality Roofing, Housing wards were complete, there were no gaps in this Ownership, Housing Rental, Ages) mapping. Drawing Numbers: DMDP-COL-01-032 to 042 Methodology: The Income Survey undertaken by Aurecon in September 2017 coded responses into geographic clusters. These 24 areas describe spaces within the 1km zone around BRT Line 1. Responses to income survey questions were put into a database, and tagged to the geographic cluster to which they corresponded, and mapped in GIS. The relative weight of positive vs negative responses for each question, in each question, is represented as a percentage, which is drawn as a colour gradient. Sources: Income Survey, September 2017 181 Appendix A / Part A2 - Baseline Methodologies A2.5 Aurecon (Natural Environment, Social Facilities and Infrastructure) Methodology Most of the map components were sourced from the relevant local authorities in shapefile format. These authorities include DAWASCO, TANESCO, Ministry of Lands, Housing and Human Settlements Development, PO-RALG and the World Bank. The maps have been produced on the assumption that the data received from local authorities and the World Bank are accurate and up to date. In the cases where the Consultant saw obvious gaps in the data, extrapolations and visual observations were used to fill such gaps. This includes income, household and streetscape survey data. Slope Gradient Plan Duplications between these various sources were then eliminated through identifying which data source is Drawing Number: DMDP-AUR-01-001 the most complete and correlates best to the Aerial Methodology: : In order to produce the Imagery and Contour data provided by the Ministry of slope/gradient plan, the consultant produced a Digital Lands. All these different shapefiles were then merged Terrain Model (DTM) which was generated from the 1m into one layer and labelled as potential flood risk contours for Dar es Salaam at a 5m cell size resolution. areas. This contour data was received from the Ministry of Sources: (1) Flood Areas OpenStreetMap DSM; Lands in Dar es Salaam and the DTM generated was (2) World Bank; (3) TANESCO; (4) DAWASCO; used to derive a Slope grid measured in degrees and (5) Kinondoni Municipal Council. classified into the following classes: • < 1 : 6 Community Facility Plans • 1:4 - 1:6 Drawing Number: DMDP-AUR-01-003 • 1:2 - 1:4 to DMDP-AUR-01-015 • > 1 : 2 Methodology: : The community facility service level maps were produced through using the Council of The slope grid was then used within the study area to Scientific and Industrial Research (CSIR) Guidelines determine built up areas that fell within different slope for the provision of community facilities in South classes. African settlements as a benchmark. The reason for Sources: Ministry of Lands, Housing and Human this primarily relates to the fact that the Tanzanian Settlements Development (MLHHSD). standards does not include travel times to facilities across the spectrum of facilities analysed. Flood Risk Areas Plan In this sense, the acceptable travel distance for each Drawing Number: DMDP-AUR-01-002 facility type, as stipulated by the CSIR guideline, were used as a benchmark for Dar es Salaam as Methodology: Starting from OpenStreetMap, we South Africa shares many similar characteristics. have cross-checked the data with the shapefiles These benchmarks, for individual facilities, for provided by the client as well as local government acceptable travel times were then fed into a GIS model agencies such as TANESCO, DAWASCO and Kinondoni that produces isochrones that display areas which Municipal Council for wetland, rivers, water bodies, are within acceptable travel distance of community swamps and streams. facilities. These standards are displayed in the tables below for individual community facility types. Appendix A / Part A2 182 A2.5 Education: CSIR Standard - South African Cities Facility Type Average Threshold (Population) Acceptable Travel Distance (KM) Secondary School 12,500 5 km (+- 1 hour) Primary School 7,000 5 km (+- 1 hour) Education: Adapted Standard - Dar es Salaam Facility Type Average Threshold (Population) Acceptable Travel Distance (KM) 2.5 km (+- 30 min walking distance Secondary School 12,500 depending on topography) 2.5 km (+- 30 min walking distance Primary School 7,000 depending on topography) Health: CSIR Benchmarks for South African Cities Facility Type Average Threshold (Population) Acceptable Travel Distance (KM) Tertiary Hospital L3 2,400,000 Variable Regional Hospital L2 1,770,000 Variable District Hospital L1 300,000-900,000 30 km Community Health Centre 100,000-140,000 90% of population served within 5 km Primary Health Clinic 24,000-70,000 90% of population served within 5 km Health: CSIR Benchmarks Adapted for Dar es Salaam Facility Type Average Threshold (Population) Acceptable Travel Distance (KM) 90 % of Population served within Hospital 300,000 5 km (+-1 hour walking distance depending on topography) 90% of population served within 2.5 km (+- 30 min Health Clinic 24,000-70,000 walking distance depending on topography) Table A.2.6 Adapted from CSIR (2015), Guidelines for the provision of social facilities in South African settlements Sources: Ministry of Lands, Housing and Human Settlements Development (MLHHSD) Issues: The Tanzanian community facility standards do not stipulate acceptable travel distances to individual facility types, and thus the South African standards were utilised as a benchmark. 183 Appendix A / Part A2 - Baseline Methodologies A2.5 Infrastructure Service Issues: The installed kVA capacity for the Kinondoni Level Plan - Water South region was not digitally available. As such, it was not possible to draw network balance and geometry Drawing Number: DMDP-AUR-01-017 conclusions for this portion of the corridor. Methodology: The water infrastructure service map was produced to illustrate the current water Infrastrucutre Service Level distribution network within the BRT corridor, based Plan - Sewer & Sanitation on the available information. CAD based asset data, consisting of water pipe layouts and pipe diameters, Drawing Number: DMDP-AUR-01-021 was converted into GIS format and overlaid onto the Methodology: The sewer infrastructure service BRT corridor along with the existing road network, map was produced to illustrate the current sewer planned development areas, unplanned development conveyance network within the BRT corridor, based areas and open space. Key water infrastructure on the available information. The data consisted of elements, namely water treatment plants, storage two distinct sets, namely existing sewer conveyance reservoirs, pump stations and distribution pipelines networks and proposed/future networks that were extracted from the datasets and highlighted in authorities are planning for. CAD based asset data, the map, to facilitate the analysis of the network and to consisting of sewer pipe layouts and pipe diameters, assist with drawing conclusions on the extent of water was converted into GIS format and overlaid onto provision within the BRT corridor. the BRT corridor along with the existing road Sources: (1) DAWASCO Water and Sewer network network, planned development areas, unplanned layouts; (2) World Bank development areas and open space. Key sewer infrastructure elements, namely sewerage treatment Issues: The main issue was the missing data. works, maturation ponds and conveyance pipelines Wards which did not have any data, as there were gaps were extracted from the datasets and highlighted which the team continues to work with the client to fill, in the map, to facilitate the analysis of the network were displayed with their ward name in the colour red. and to assist with drawing conclusions on the extent of sewerage conveyance within the BRT corridor, Infrastructure Service particularly in view of the relatively low coverage of the Level Plan - Electricity existing network versus the proposed future networks. Drawing Number: DMDP-AUR-01-020 Sources: (1) DAWASCO Water and Sewer network layouts; (2) World Bank Methodology: The electricity infrastructure service map was produced to illustrate the current electricity Issues: While the graphic illustration of the existing distribution network within the BRT corridor, based and proposed sewerage network is comprehensive, on the available information. CAD based asset data, inherent sewer network capacity data was not consisting of electricity power-line layouts and voltage available for analysis. Capacity and sewerage yield capacity, was converted into GIS format and overlaid conclusions were calculated based on land use figures onto the BRT corridor along with the existing road and design parameters based on the network size. network, planned development areas, unplanned development areas and open space. Key electricity infrastructure elements, namely local power plants (Ubungo power plant), primary and distribution substations and MV lines were extracted from the datasets and highlighted in the map, to facilitate the analysis of the network and to assist with drawing conclusions on the extent of electricity provision within the BRT corridor. Sources: (1) TANESCO electricity network layouts; (2) World Bank Appendix A / Part A2 184 A2.5 Infrastrucutre Service Level Infrastrucutre Service Level Plan - Drainage & Stormwater Plan - Drainage & Stormwater Drawing Number: DMDP-AUR-01-023 Drawing Number: DMDP-AUR-01-025 Methodology: The stormwater drainage Methodology: The waste infrastructure service infrastructure service map was produced to illustrate map was produced to illustrate the current waste the current stormwater management network within facility network within the BRT corridor, based on the BRT corridor, based on the available information. the available information. CAD based asset data, CAD based asset data, consisting of stormwater consisting of waste collection points and other waste pipelines and pipe diameters, as well as canals and elements were converted into GIS format and overlaid open drains was converted into GIS format and onto the BRT corridor along with the existing road overlaid onto the BRT corridor along with the existing network, planned development areas, unplanned road network, planned development areas, unplanned development areas and open space. Key waste development areas and open space. Key drainage infrastructure elements, namely collection points, gas infrastructure elements, namely water bodies, energy projects, landfill sites and composting sites wetlands and existing water courses were extracted were extracted from the datasets and highlighted in from the datasets and highlighted in the map, to the map, to facilitate the analysis of the network and to facilitate the analysis of the network and to assist assist with drawing conclusions on the extent of waste with drawing conclusions on the extent of stormwater provision within the BRT corridor. provision within the BRT corridor. Sources: (1) World Bank Sources: (1) World Bank Issues: The waste network demonstrates general Issues: While the graphic illustration of the drainage coverage of facilities, but does not demonstrate level network is comprehensive, the capacity of that of service in terms of solid waste provision within the network to cater for current urban stormwater runoff corridor. The income survey was utilised to determine was not available for analysis. Stormwater capacity existing service levels for waste collection within the conclusions were calculated based on land use area, corridor. assumed impervious coverage and design parameters based on the network size. 185 Appendix A / Part A2 - Baseline Methodologies A2.5 Infrastructure Heat Map Series (Water, Electricity, Sanitation, Solid Waste) Drawing Numbers: DMDP-AUR-01-016; 018; 019; 022; 024; 026 Methodology: : The infrastructure heat maps are a graphical representation of the relative density of various forms of infrastructure (water, sewer, stormwater and electricity), calculated by measuring the combined length of these infrastructure elements within a unit area, resulting in a heat map highlighting regions of high infrastructure density (brown) versus those regions where infrastructure is sparse or non-existent (blue). The motivation behind developing these illustrations is premised on the assumption that regions with high infrastructure densities are likely to have a higher municipal level of service than regions with lower infrastructure densities. While the heat map delineations do not represent a tangible metric for measuring infrastructure provision, they do provide a comparative tool for illustrating relative levels of service, in terms of coverage. Given that this is purely a comparative exercise, it is recommended to not subdivide the measurement of infrastructure density into too granular a count, but to rather keep a macro focus so that each subdivision can be reasonably described. It is therefore recommended that the infrastructure heat maps be subdivided using four polygons of measurement, i.e. divide the current heat map measurements into four ranges. This will provide a comparison that is aligned with the level of accuracy currently being applied, and is also consistent with the current method of assessing infrastructure in the report, using four descriptions for levels of service. The four ranges of infrastructure density can be described as follows: Polygon Range Ascribed Infrastructure Density Infrastructure Polygon Description Little to no infrastructure coverage. Level of 1 0 service can be considered non-existent. Minimal infrastructure coverage. A basic level 2 2 of service is present, albeit minimal. Improved infrastructure coverage. A general spread of 3 5 infrastructure services, implying an improved level of service Comprehensive infrastructure coverage. A full spread of 4 10 all infrastructure services, implying full service coverage It is important to note the following: • The ascribed infrastructure density score does not increase linearly per range – this is intentional. This is to emphasise that Polygon ranges 1 and 2 have significantly less coverage than Polygon level 4, for instance, and therefore should be numerically rated lower. • The polygon ranges are purely representations of infrastructure coverage. They do not account for actual municipal level of service. Therefore, while some areas may for example have a density score of 10 based on the coverage, they could well have sub-par levels of service, such as no running water in the existing water mains, or no electricity provision, even though the infrastructure is in place. • The infrastructure polygon descriptions are intentionally generic in nature, given that this is a purely numerical representation of elements that have not been qualitatively assessed for their actual performance. The conclusions to draw here are comparative ones, and not actual levels of service conclusions. Sources: (1) World Bank; (2) TANESCO; (3) DAWASCO; (4) Kinondoni Municipal Council. Appendix A / Part A2 186 A2.5 187 Appendix A / Part A3 - TOD Matrix Methodology Appendix A / Part A3 188 Appendix A Part A3 TOD Matrix Methodology 189 Appendix A / Part A3 - TOD Matrix Methodology A3.1 TOD Matrix Introduction Indicators’ Categories The aim of the TOD Matrix is to assist in the evaluation The TOD Matrix indicators were divided into three process by enabling a range of quantitative and qualitative categories: criteria that may affect each station. The matrix comprises 18 • Market Potential (MAR); indicators, both qualitative and quantitative in nature. • Development Readiness (DEV); Data for the quantitative indicators are drawn from the • TOD Characteristics (TOD). corridor baseline assessment (full technical details in Volume 2), from a range of primary sources and supported Market Potential assesses the station areas ability to by survey work undertaken by the CDS Team between support new development considering the demand and January and September 2017. attractiveness to investment. The qualitative data comes from a range of primary sources Development Readiness explores whether the legal, (legal texts, government websites, press reports and physical, and infrastructure framework of the station area is interviews), secondary reports and data sources, scored by ready to support new development assessing major drivers the CDS Team based on their comprehensive knowledge of of supply in the land market. African and international cities. TOD Characteristics compares the station areas compatibility and potential to achieve the ‘TOD Principles’ identified for Dar es Salaam. Morocco Terminal Kinondoni Kivukoni Mwanamboka Terminal Kimara Terminal Mkwajuni Posta ya Mwembechai Ubungo Maji Zamani Shekilango Argentina Morocco Hotel Tip Top Bucha Kisutu Kona Magomeni Hosp. DIT Korogwe Kibo Ubungo Terminal Magomeni Mapipa City Council Urafiki Manzese Kagera Usalama Jangwani Fire Baruti Msimbazi Police Gerezani Terminal Figure A.3.1 Stations Assessed in the TOD Matrix Appendix A / Part A3 190 A3.1 1 Scoring Weighting the Index All qualitative indicators have been scored on an integer At the conclusion of the indicator scoring and normalisation, scale. This scale ranges from 0–4 depending on the the CDS Team selected a series of weightings and definitions and scoring scheme formulated for each sub-weightings deemed appropriate for the overall index indicator. Scores are assigned by the relevant experts in calculation. the CDS Team based on their expertise. The integer scores The weightings represent the judgement of the CDS Team are then transformed to a 0–100 score to make them as well as outcomes of consultations with key stakeholsers comparable with the quantitative indicators in the index. regrading priorities and objectives for the city and corridor. 2 However, these weightings are not final and can continue to be refined and adjusted in the future as priorities Normalisation change or may be used to test different scenarios at will. 3 Indicator scores are normalised and then aggregated across Modelling and weighting the indicators and categories in categories to enable a comparison of broader concepts the index results in scores of 0–100 for each station, where across stations. Normalisation rebases the raw indicator 100 represents the highest quality and performance, and data to a common unit so that it can be aggregated. 0 the lowest. The 32 stations assessed can then be ranked according to these scores. The indicators have been normalised on the basis of: x = (x – Min(x)) / (Max(x) – Min(x)) where Min(x) and Max(x) are, respectively, the lowest and highest values in the 18 stations for any given indicator. The normalised value is then transformed from a 0–1 value to a 0–100 score to make it directly comparable with other indicators. Where a theoretical minimum and maximum are known (e.g. an entropy value cannot be higher than 1 or 100%) these values are used in place of the highest and lowest values in the range. This in effect means that the station with the highest raw data value will score 100, while the lowest will score 0. In indicators where a low score was desirable the calculation was inversed, as seen in the “High Value Desirable?” column. 191 Appendix A / Part A3 - TOD Matrix Methodology A3.2 Indicators Attractiveness to Investors Business Willingness Category: Market Potential Category: Market Potential Indicator #1 Indicator #2 Measurement: Recent value growth 2012-2016 and yield Measurement: Recent development activity, recent potential within 1000m of a station. planning applications and site assembly activity within 1000m of a station. Methodology: In order to ascertain the attractiveness of the property market in a given station area, scoring was Methodology: The team evaluated the level of property done based on evidence of recent market trends and future market interest in a given station area by measuring the potential to make investment returns. level of development activity, site assembly and planned development activity. The evaluation was done per station The team evaluated the move in property market values area for its Development Readiness by expert opinion between 2012 and 2015 based on government valuation based on evidence. The aggregated score is the business data. The score of each station area was based on the shift willingness score. Scoring ranged from 0 to 4, where in values in the local area over this period – this is the recent 0 indicates no active or planned development and 4, growth score. numerous active or planned development. Each station area was also evaluated based on market evidence showing the relationship between land costs and Sources: Property market data from Pangani. rent in the local area, considering possible development density, as in the indicator #16. Scoring was done per station area for its Development Readiness by expert opinion based Nodal Value on evidence. Category: Market Potential The scores were aggregated for recent growth and Indicator #3 Development Readiness to show the attractiveness to investors. Scoring ranged from 0 to 4, where 0 indicates Measurement: Betweenness centrality. low growth / returns potential and 4, high growth / returns Methodology: This criterion estimates how significant a potential. node is to facilitate movement across the whole network. Sources: (1) Government property valuation data per square Its significance is indicated by the number of times the meter for Dar es Salaam 2012, 2015 provided by PO-RALG; node acts as a bridge along the shortest path between two (2) Market data showing rents and capital values by sector other nodes. The result displays the number of shortest for each station area provided by Pangani. paths within the network passing through the node. Higher centrality of a node is linked to higher distribution of movements across the network. Therefore, a station with high Betweenness Centrality has a greater influence over the transfer of passengers through the network. These station areas have a high growth potential and more employment opportunities. The Betweenness Centrality assessment has been performed for all BRT implementation phases (Phase 1, Phase 1-2- Appendix A / Part A3 192 A3.2 3, and Phase 1-2-3-4-5-6). The team chose to show only onto the current public transport networks (BRT Phase 1, the fully implemented network figures in order to better existing feeder route from Mbezi to Kimara and ferries). understand the role that the station is planned to have in The team ran the analysis taking into account road and the future. In this way, the measurement is a useful tool to public transport pattern as well as speed and delay levels. project future network conditions. The public transport average speeds used are 21 km/h for As there is still no final project defined for Phases 4-5-6, BRT, 18 km/h for feeder route to Kimara, 9 km/h for ferry and stations have been approximately located along these BRT for pedestrian 4.5 km/h. lines each 500m. Sources: (1) Basic structure of current road and railway Sources: (1) Basic structure of current road and railway network, dala dala network, BRT Phase 1, ferry routes network, dala dala network, BRT Phase 1, ferry routes from Open Street Map; (2) Baseline information on Public from Open Street Map; (2) Baseline information on Public Transport by DART Agency; (3) Baseline information on Transport by DART Agency; (3) Baseline information on Public Transport by World Bank; (4) Consulting services for Public Transport by World Bank; (4) Consulting services design of 42.9 km of Bus Rapid Transit system Phase 2 and for design of 42.9 km of Bus Rapid Transit system Phase 3 in Dar Es Salaam city - 2.1 Traffic Survey and Demand 2 and 3 in Dar Es Salaam city - 2.1 Traffic Survey and Forecasting report, Kyong Dong, January 2015; (5) Average Demand Forecasting report, Kyong Dong, January 2015; Public Transport speeds based on MIC GPS analysis and (5) Methodology from Urban Network Analysis by City previous projects; (6) Population data from 2017 Population Form Lab, Singapore University of Technology & Design in Projection by CoLab; (7) Land use analysis submitted by collaboration with MIT. Broadway Malyan on July 3rd 2017. Station PT Catchment (Employment) Station PT Catchment (Population) Category: Market Potential Category: Market Potential Indicator #5 Indicator #4 Measurement: Employment catchment within 30 minutes Measurement: Population catchment within 30 minutes from a station. from a station. Methodology: The indicator measures the number of jobs Methodology: The indicator measures the number that are located within the 30 minutes catchment of the of inhabitants that are located within the 30 minutes station. A large number of jobs indicates good access to catchment area of the station. A large population catchment employment opportunities and therefore a high amount provides a quantum of customers that can support of people that potentially need to reach the station. businesses and of people that can potentially access the The number has been obtained by defining the amount of station. The number was obtained by defining the amount formal and informal jobs reachable from each station of of inhabitants reachable from each station of origin through origin through a travel time that is considered typical for a travel time that is considered typical for urban commuting urban commuting trips. The aim of an isochronal analysis trips. The aim of an isochronal analysis is to replicate the is to replicate the layout of networks and reproduce their layout of networks and reproduce their accurate footprint in accurate footprint in terms of actual travel time. terms of actual travel time. As with the population catchment criteria, time-based Time-based travel maps were produced for different modes travel maps have been created for different modes of of transportation by creating a multi-modal transport transportation by creating a multi-modal transport network model of Dar es Salaam’s metropolitan area. network model of Dar es Salaam’s metropolitan area. The model simulates a combined pedestrian and public The model simulates a combined pedestrian and public transport trip by superimposing the pedestrian network 193 Appendix A / Part A3 - TOD Matrix Methodology A3.2 transport trip by superimposing the pedestrian network Prosperity on to the current public transport networks (BRT Phase 1, existing feeder route from Mbezi to Kimara and ferries). Category: Market Potential The analysis has been run taking into account road and Indicator #6 public transport pattern as well as speed and delay levels. Measurement: Income band, housing type, low poverty The public transport average speeds used are 21 km/h for indicators, high wealth indicators. BRT, 18 km/h for feeder route to Kimara, 9 km/h for ferry and for pedestrian 4.5 km/h. The employment distribution Methodology: The team analysed the survey derives from an estimation and distribution of formal and data from a 2,028 point household income survey. informal employment in land use parcels based on 2017 At this stage of analysis the data was analysed at cluster land use figures. summary level, hence some judgement was made regarding the applicability of data clusters in the 1000m radius of a Sources: (1) Basic structure of current road and railway station. The team scored the proportion of a station area network, dala dala network, BRT Phase 1, ferry routes that earned more than 500,000 shillings per month. from Open Street Map; (2) Baseline information on Public Transport by DART Agency; (3) Baseline information on The proportion with higher status professions, such as Public Transport by World Bank; (4) Consulting services for Legislators Administrators and Managers, Professional, design of 42.9 km of Bus Rapid Transit system Phase 2 and Technicians and Associate Professionals Bank Account, 3 in Dar Es Salaam city - 2.1 Traffic Survey and Demand Smart-phones, Air Conditioning, Motor Cycle, Landline, Forecasting report, Kyong Dong, January 2015; (5) Average Mobile Phone, Computer or Laptop, Internet, Motor vehicle, Public Transport speeds based on Mobility In Chain GPS Fridge were also scored. The area’s proportion with College analysis and previous projects; (6) Employment distribution or University Education was also scores. as per latest land use analysis submitted by Broadway The proportion of the area that contained houses with Malyan on July 3rd 2017. access to piped water and sewerage and also those that used a roofing material other than sheet metal was scored. The aggregated scores of all these indicators is the prosperity score. Scoring ranged from 0 to 4, from the survey and for the relevant wards or sub-wards, where 0 represents strong poverty indicators and 4, strong wealth indicators. Sources: (1) Project’s income survey in September 2017, where 2,028 households were randomly sampled in clusters across the BRT corridor. Appendix A / Part A3 194 A3.2 Ridership Ownership Category: Market Potential Category: Development Readiness Indicator #7 Indicator #9 Measurement: Daily ridership of station (as of 2nd June Measurement: Hectares of land in public ownership within 2017) 500m. Methodology: Daily ridership indicates the number of Methodology: The amount of public ownership area passengers boarded and alighted at the station during within a 500m buffer (Euclidean Radius) of each station one operational day. High ridership means a high amount was measured in order to capture a potential development of passengers who ride the BRT corridor and therefore easiness. Given the availability of data, publicly owned characterises a desirable location with a generally high land was presumed to be areas classified by the Land footfall to support local business. Use Level 2 code 3 (Institutional). The team assumed that within the BRT context, publicly owned land could facilitate The team chose to analyse the most recent data development, rather than relying on the development of received from DART, which refer to June 2017. land in private ownership. In particular, the daily values refer to the 2nd of June 2017 that has been the day with the highest ridership during June Sources: (1) Kinondoni, Ilala and Temeke municipality 2017. land use and boundary shapefiles provided by the client; (2) Satellite imagery with 1m resolution provided from Data for Baruti station was unexplainedly missing for the ArcGIS World Imagery combined from Esri, DigitalGlobe, data set so in this instance the value was estimated by taking GeoEye, Earthstar Geographics, CNES/Airbus DS, USDA, an average value from stations on either side. USGS, AEX, Getmapping, Aerogrid, IGN, IGP, swisstopo, Sources: (1) Ridership data received from DART on the July and the GIS User Community updated in May 2017; 18th 2017. (3) ward boundaries shapefile provided by the client; (4) Dar es Salaam City Master Plan 2012-2032 from the Ministry of Lands, Housing and Human Settlements Development in consortium with Dodi Moss, Buro Happold, Land Assembly Afri-Arch Associates and QConsult. Category: Development Readiness Land Use Level 1 Indicator #8 Code Description 1 Planned Measurement: Number of buildings within 500m of a 2 Unplanned station. 3 Un-built Up Methodology: Given the availability of data, the team 4 Water Bodies measured the amount of buildings that would fall within Table A.3.1 Land Use Level 1 Codes and Descriptions a 500m (Euclidean Radius) of each station in order to assess the possibility of land assembly. A higher number of Land Use Level 2 buildings can be correlated with multiple owners, which Code Description can make land assembly difficult. The more difficult land 1 Residential assembly is, the lesser Development Readiness the station 2 Commercial has. The building inventory described in the Baseline 3 Institutional Appendix was used as a base for this measurement. 4 Industries Sources: (1) Building footprints from Open Street Map; 5 Transport and Utilities (2) Satellite imagery with 1m resolution provided from 6 Open Space ArcGIS World Imagery combined from Esri, DigitalGlobe, 7 Water Bodies GeoEye, Earthstar Geographics, CNES/Airbus DS, USDA, 8 Mining USGS, AEX, Getmapping, Aerogrid, IGN, IGP, swisstopo, Table A.3.2 Land Use Level 2 Codes and Descriptions and the GIS User Community updated in May 2017; (3) Photographic documentation of an aerial citywide survey from January 2017 by Broadway Malyan. 195 Appendix A / Part A3 - TOD Matrix Methodology A3.2 Non-Developable Land Access to Infrastructure Category: Development Readiness Category: Development Readiness Indicator #10 Indicator #12 Measurement: Area of non-developable land, which Measurement: Reliability of utility provision. quantifies risk areas and leave ways for public works within Methodology: This indicator quantified the existing level 500m of a station. of municipal services within the vicinity of a BRT station by Methodology: Non-developable land is a combination of drawing on the findings of the income survey undertaken in flooding areas present in the baseline assessment and Land the study corridor in September 2017. As part of this survey, Use Level 3 codes R, S, T and U - respectively Agriculture, specific data in terms of utility provision (water, sewer, Leaveways for Public Utilities, Ecological Fragile Lands and electricity, drainage, solid waste) was captured, both in Shooting Ranges. The team assumes that higher levels of terms of connectivity and reliability. The results of this non-developable land makes the land undesirable for TOD survey, being geo-located, were then allocated to each development. specific station within the corridor, to provide a performance rating in terms of overall utility provision. This resembles Sources: (1) Kinondoni, Ilala and Temeke municipality a change in approach compared to versions of the TOD land use and boundary shapefiles provided by the client; matrix prior to the income survey, which purely relied on (2) Satellite imagery with 1m resolution provided from linear meterage of infrastructure assets (pipes, cables, ArcGIS World Imagery combined from Esri, DigitalGlobe, etc) as a measure of infrastructure supply. The income GeoEye, Earthstar Geographics, CNES/Airbus DS, USDA, survey findings therefore highlights which stations are USGS, AEX, Getmapping, Aerogrid, IGN, IGP, swisstopo, best positioned for infrastructure, based on actual utility and the GIS User Community updated in May 2017; provision to residents within the station vicinity. (3) ward boundaries shapefile provided by the client; (4) Dar es Salaam City Master Plan 2012-2032 from the Sources: (1) Income Survey findings and analysis Ministry of Lands, Housing and Human Settlements Development in consortium with Dodi Moss, Buro Happold, Afri-Arch Associates and QConsult; (5) Flooding area part of Appendix A Part A2. Planning Status Category: Development Readiness Indicator #11 Measurement: Percentage of land within 1000m that has a TOD supportive plan approved. Methodology: The planning status of land was measure by the area inside a 1000m buffer (Euclidean Radius) of each station that would be part of an approved TOD supportive plan. These plans should be aligned in terms of directives, guidelines and regulations that are favourable to implement TOD development principles. Land that is subject to approved redevelopment plans that are supportive of TOD will be easier to develop, with less friction to getting development approved. Sources: Various Area Redevelopment and Project Plans as provided by DLAs (refer to Volume 2 Part 3 for full bibliography) Appendix A / Part A3 196 A3.2 Land Use Level 1 Land Use Level 2 Land Use Level 3 General Land Use Grouping Code Description Code Description Code Description Description 1 Planned A Residential Building Planned Residential 2 Unplanned A Residential Building Unplanned Residential 1 Residential B Special Residential Special Residential F Mixed Use Mixed Use C1 Hotel Hotel C Retail and F&B Retail, F&B and Local Market E Local Market 2 Commercial EE Fuel and Energy Services Fuel and Energy Services CC Offices Offices D Workshops Workshops Z Military Military G Public Building Public Building K Education Education 3 Institutional H Community Facilities Community Facilities 1 I Religious Religious Planned / W Cultural and Historical Sites Cultural and Historical Sites 2 Unplanned / L Wholesale Warehouse Wholesale Warehouse Un-Built up 4 Industries M Service Trade Service Trade 3 N Special Industries Special Industries Communications Q Communications & Public Works & Public Works Transportation 5 P Transport Terminal Facilities Transport Terminal Facilities and Utilities Leave ways for S Public Utilities Public Utilities 6 Open Space R Agriculture Agriculture U Shooting Ranges 6 Open Space Leisure, Sport and Recreation J Outdoor Leisure Open Space/ Water 6/7/8 T Ecological Fragile Lands Ecological Fragile Lands Bodies/Mining 4 Water Bodies 7 Water Bodies T Ecological Fragile Lands Water Table A.3.3 Land Use Grouping 197 Appendix A / Part A3 - TOD Matrix Methodology A3.2 Connectivity to Public Transport station and identifies the station influence area. A higher population/employment number within this buffer Category: TOD Characteristics indicates a greater degree to which an existing area could Indicator #13 support TOD activities. Unlike the Network Radius analysis (isochronal), the Measurement: Number of public transport stops within extracted figures are based on a purely geometric defined 500m (BRT, feeder, dala dala, rail, ferry) of a station. area without considering the street network to assess the Methodology: Connectivity to public transport connectivity. This way, the analysis is distance-based rather indicates the amount of BRT, feeder, dala dala, rail, than time-based. and ferry stops located within a buffer of 500m from Sources: (1) Basic structure of current road and railway the station. High number of connections generally network, dala dala network, BRT Phase 1, ferry routes demonstrates a high level of accessibility and connectivity. from Open Street Map; (2) Baseline information on Public 500m identifies an adequate walking distance, which fosters Transport by DART Agency; (3) Baseline information on a good integration between different transport modes. Public Transport by World Bank; (4) Consulting services Therefore, a high number of public transport stops indicates for design of 42.9 km of Bus Rapid Transit system Phase that the station has an elevated level of connectivity with 2 and 3 in Dar Es Salaam city - 2.1 Traffic Survey and other parts of the city. Demand Forecasting report, Kyong Dong, January 2015; Additional to the BRT station itself, other transport stops (5) Population data from 2017 Population Projection by within the catchment area have been aggregated to CoLab; (6) Employment distribution as per latest land use generate the overall score. No weightings were used to analysis submitted by Broadway Malyan on July 3rd 2017. differentiate the transport modes. The indicator has been analysed throughout the different implementation phases of the current and planned BRT network (Phase 1, Phase 1-2-3 and Phase 1-2-3-4-5-6). For the station, matrix only the Development Density values related to the final BRT Phase 1 implementation are Category: TOD Characteristics considered. Indicator #15 Sources: (1) Basic structure of current road and railway network, dala dala network, BRT Phase 1, ferry routes Measurement: Gross Floor Area Ratio (FAR) within 500m of from Open Street Map; (2) Baseline information on Public a station. Transport by DART Agency; (3) Baseline information on Methodology: The development density was measured Public Transport by World Bank; (4) Consulting services for using the total Gross Floor Area (GFA) in the building design of 42.9 km of Bus Rapid Transit system Phase 2 and inventory and dividing it by the 500m buffer area 3 in Dar es Salaam city - 2.1 Traffic Survey and Demand (Euclidean Radius) centred in each station. Forecasting report, Kyong Dong, January 2015. This provided a gross Floor Area Ration (FAR) that helped to assess how efficiently the land was being used in comparison to other station buffer areas. Activity Density This indicator helped identify areas that are potential being inefficiently used and therefore should be optimised as a Category: TOD Characteristics priority development area. Indicator #14 Sources: (1) Building footprints from Open Street Map; Measurement: Population and employment catchment (2) Satellite imagery with 1m resolution provided from within 500m of a station. ArcGIS World Imagery combined from Esri, DigitalGlobe, GeoEye, Earthstar Geographics, CNES/Airbus DS, USDA, Methodology: The indicator measures the number USGS, AEX, Getmapping, Aerogrid, IGN, IGP, swisstopo, of inhabitants and jobs that are located within a and the GIS User Community updated in May 2017; buffer of 500m (Euclidean Radius) from the station. (3) Photographic documentation of an aerial citywide survey This distance is generally considered as the average from January 2017 by Broadway Malyan. acceptable one for people to walk to a public transport Appendix A / Part A3 198 A3.2 Mix of Uses By mapping the walksheds as derived from the street network it is possible to assess connectivity, identify barriers, Category: TOD Characteristics and evaluate where potential infrastructure improvements Indicator #16 would be most beneficial. Sources: (1) Basic structure of current road network Measurement: Balance between the mix of land uses. from Open Street Map; (2) Baseline information on Public Methodology: The mix of land uses was measured using Transport by DART Agency; (3) Baseline information on an entropy index that evaluates the balance between the Public Transport by World Bank. percentage of each land use in the mix. The measurement is based on the Land Use level 3, as described in the baseline appendix. This indicator assumes that the land Community Facilities use mix is best when balanced among land use types. A high entropy value indicates a strong diversity of land uses Category: TOD Characteristics and a lower prevalence of mono-functional land uses. Indicator #18 Sources: (1) Building footprints and heights from Open Measurement: Number of distinct community facilities Street Map; (2) Satellite imagery with 1m resolution within 1000m. provided from ArcGIS World Imagery combined from Esri, DigitalGlobe, GeoEye, Earthstar Geographics, CNES/Airbus Methodology: This indicator measures the number of DS, USDA, USGS, AEX, Getmapping, Aerogrid, IGN, IGP, distinct social facilities that fall within a 1km buffer of a BRT swisstopo, and the GIS User Community updated in station. In the case that two facilities of the same sub-type May 2017; (3) Photographic documentation of an aerial fall within the buffer of an individual station, only one was citywide survey from January 2017 by Broadway Malyan; accounted for. This is done in order to capture the diversity (4) Dar es Salaam land use classification shapefile by of existing facilities. The team decided on this methodology Broadway Malyan. to provide a more accurate picture of how well a station is served in terms of a range of social and civic facilities. The facilities were further classified in types and sub-types, as seen in the Community Facilities Classification table. Block Permeability Sources: (1) PO-RALG social facilities data; (2) Open Street Category: TOD Characteristics Maps data downloads. Indicator #17 Community Facilities Measurement: Linear meterage of Type Sub-Type pedestrian paths and routes. Primary School Methodology: The indicator reflects the directness of links Educational Secondary School and the density of connections located in close proximity University and Tertiary Training Facilities to the stations. A highly permeable network has many short Dispensary/Clinic links, numerous intersections, and minimal dead-ends. Healthcare Healthcare Centre As connectivity increases, travel distances decrease and Hospital route options increase, allowing more direct travel between Library destinations and creating a more accessible and resilient Post Office transportation system. A greater pedestrian connectivity and Civic Police Station permeability provide a higher potential to benefit from TOD Fire Station development. Sport Facility The analysis was carried out by defining the walking Recreational Facility (Community Recreational distance that can be reached in 10 minutes from Centre/Recreation) each station assuming a walking speed of 4.3 km/h. Religious Facility The analysis equally rates all routes regardless of qualitative Table A.3.4 Community Facilities Classification aspects as paving or state but focusing on their quantity. 199 Appendix A / Part A3 - TOD Matrix Methodology A3.2 B1-A-01 B1-A-02 B1-A-03 B1-A-04 B1-A-05 B1-A-06 B1-A-07 B1-A-08 B1-A-09 B1-A-10 High Value Kivukoni Magomeni Cat ID Criteria Measurement Sub-Weighting Rationale Posta ya Zamani City Council Kisutu DIT Fire Station Jangwani Usalama Mwembechai Desirable? Terminal Mapipa Investors will be attracted to neighbourhoods Attractiveness to Recent value growth 2012-16 & yield MAR 1 15.0% where there is eveidence of growth and the 1.00 1.00 1.00 2.00 2.00 2.00 1.50 3.00 2.50 2.50 Investors potential potential to earn a profit Recent development activity, recent Indicates the extent to which the business MAR 2 Business Willingness planning applications and site 15.0% community is willing to invest in the station 2.50 2.50 2.50 2.50 1.50 1.50 1.50 2.00 1.50 1.50 assembly activity area Higher centrality of a node is linked to higher distribution of movements across the network MAR 3 Nodal Value Betweeness Centrality 15.0% - 832 472 642 1,372 3,238 3,246 3,464 6,020 5,838 with greater growth potential and more employment opportunities Station PT Catchment Population catchment within 30 Large population catchment provides MAR 4 15.0% 399,266 490,616 533,753 613,363 649,758 708,387 750,912 836,401 840,794 851,139 (Population) minutes of station quantum of customers to support businesses Station PT Catchment Employment catchment within 30 Indicates good access to employment MAR 5 15.0% 281,816 304,967 314,666 332,441 341,872 355,760 366,407 388,007 385,585 384,869 (Jobs) minutes of station opportunities Income band, housing type, low Indicators show a general prosperity of the MAR 6 Prosperity poverty indicators, high wealth 15.0% area. The lower the prosperity the greater 0.75 - - 1.75 2.50 3.50 3.00 2.00 2.25 1.25 indicators opportunity there is for regeneration Daily ridership of station High ridership indicates desireable location MAR 7 Ridership 10.0% 15,592 10,424 9,217 5,827 10,009 11,051 528 7,144 5,095 3,713 (as of 02-06-2017) and high footfall to support local business 100% Large number of buildings indicates potential Number of buildings within 500m of DEV 8 Land Assembly 25% of multiple owners which can make land 128 296 604 998 893 1,186 669 2,736 1,670 2,663 station assembly difficult Hectares of land in public ownership Land in public ownership is potentially easier DEV 9 Ownership 20% 19.88 Ha 11.19 Ha 10.00 Ha 21.63 Ha 28.47 Ha 16.65 Ha 8.70 Ha 6.64 Ha 10.24 Ha 5.63 Ha within 500m to develop than land in private ownership Hectares of non-developable land. Non-Developable High level of undevelopable land makes the DEV 10 Quantifies risk areas and leaveways for 25% 2.76 Ha 1.86 Ha 1.37 Ha 0.00 Ha 0.00 Ha 8.97 Ha 128.71 Ha 7.25 Ha 1.75 Ha 4.86 Ha Land area undesirable for TOD development public works within 500m Land that is subject to approved Composite score of land within 1000m redevelopment plans that is supportive of DEV 11 Planning Status that has a TOD supportive plan 5% 7.27 9.56 10.01 18.71 21.76 23.52 20.64 31.63 31.63 31.63 TOD will be easier to develop, with less approved friction to getting development approved Good current access to public utilities can Access to Access to reliable water and electricity DEV 12 25% help make new development more 0.68 0.68 0.68 0.60 0.54 0.41 0.47 0.49 0.49 0.35 Infrastructure supply viable/attractive 100% Number of public transport stops within High number of connections demonstrates a Connectivity to public TOD 13 500m (BRT, Feeder, Dala Dala, Rail, 15% high level of connectivity, accessibility and 3 4 5 3 4 3 1 5 2 1 transport Ferry) mobility A higher population / employment number Population and employment within a catchment indicates a greater degree TOD 14 Activity Density 20% 4,869 29,376 51,136 68,376 46,906 38,260 8,490 30,008 23,110 39,573 within 500m of station to which an existing area could support TOD activities Gross Floor Area Ratio (FAR) within Land that is inefficiently used should be TOD 15 Development Density 15% 0.390 1.426 1.925 1.497 1.111 0.874 0.144 0.422 0.309 0.426 500m of station optimised as a priority High entropy value indicates a strong Entropy value for land uses within TOD 16 Mix of Uses 20% diversity of land uses and lower prevalance of 0.39 0.69 0.67 0.51 0.55 0.50 0.35 0.42 0.56 0.39 500m mono-functional land uses The higher the linear meterage of paths Linear meterage of pedestrian paths would indicate greater pedestrian connectivity TOD 17 Block Permeability 15% 5 14 15 22 23 20 7 29 8 29 and routes and permeabaility with a higher potential to benefit from TOD development No. of community assets within 500m The higher the number of facilities the greater TOD 18 Community Facilities 15% 6 7 12 12 10 13 10 9 8 11 of station the potential to support TOD 100% Table A.3.5 TOD Matrix Station Data ( Kivukoni Terminal to Magomeni Mapipa ) B1-A-01 B1-A-02 B1-A-03 B1-A-04 B1-A-05 B1-A-06 B1-A-07 B1-A-08 B1-A-01 09 B1-A-02 10 B1-A-03 11 B1-A-04 12 B1-A-05 13 B1-A-06 14 B1-A-07 15 B1-A-08 16 B1-A-09 17 B1-A-10 18 High Value Kivukoni Magomeni High Value Kivukoni Magomeni Rationale Cat ID Posta ya Zamani Criteria City Council Measurement Kisutu DIT Sub-Weighting Rationale Jangwani Fire Station Usalama Mwembechai Posta ya Zamani Kagera City Council Argentina Kisutu Manzese DIT Tip Fire Top Station Urafiki Jangwani Shekilango Ubungo Terminal Usalama Ubungo Maji Mwembechai Desirable? Terminal Mapipa Desirable? Terminal Mapipa attracted to neighbourhoods Investors will be attracted to neighbourhoods Attractiveness to Recent value growth 2012-16 & yield veidence of growth and the MAR 1 1.00 1.00 1.00 2.00 15.0% 2.00 2.00 of growth and1.50 where there is eveidence the 3.00 2.50 1.00 2.50 1.00 2.50 1.00 2.50 2.00 2.50 2.00 2.50 2.00 1.00 1.50 1.00 3.00 1.50 2.50 2.00 2.50 Investors potential a profit potential to earn a profit ent to which the business Recent development activity, recent Indicates the extent to which the business ing to invest in the station MAR 2 2.50 Willingness 2.50 Business 2.50 planning applications and site 2.50 15.0% community is willing1.50 1.50 1.50 to invest in the station 2.00 1.50 2.50 1.50 2.50 1.50 2.50 1.50 2.50 1.50 1.50 1.50 1.50 2.00 2.00 1.50 2.00 1.50 assembly activity area of a node is linked to higher Higher centrality of a node is linked to higher ovements across the network distribution of movements across the network MAR 3 Nodal-Value 832 472 Betweeness Centrality 642 15.0% 1,372 3,238 3,246 3,464 6,020 - 832 5,838 5,794 472 642 5,910 1,372 14,912 3,238 3,310 2,960 3,246 3,464 2,672 6,020 2,482 5,838 2,796 wth potential and more with greater growth potential and more ortunities employment opportunities catchment provides Station PT Catchment Population catchment within 30 Large population catchment provides MAR 4 399,266 490,616 533,753 613,363 15.0% 649,758 708,387 750,912 836,401 840,794 399,266 851,139 490,616 862,906 533,753 613,363 860,964 649,758 868,025 708,387 823,217 804,543 750,912 836,401 743,289 840,794 700,544 851,139 635,859 omers to support businesses (Population) minutes of station quantum of customers to support businesses ccess to employment Station PT Catchment Employment catchment within 30 Indicates good access to employment MAR 5 281,816 304,967 314,666 332,441 15.0% 341,872 355,760 366,407 388,007 385,585 281,816 384,869 304,967 384,518 314,666 332,441 381,921 341,872 382,932 355,760 364,469 353,720 366,407 388,007 314,221 385,585 272,416 384,869 232,724 (Jobs) minutes of station opportunities a general prosperity of the Income band, housing type, low Indicators show a general prosperity of the he prosperity the greater MAR 6 0.75 Prosperity - poverty - wealth indicators, high 1.75 15.0% 2.50 area. The lower the3.50 3.00 prosperity the greater 2.00 2.25 0.75 - 1.25 - 2.00 1.75 2.50 2.50 1.75 3.50 3.00 1.00 2.00 0.75 2.25 0.25 1.25 0.75 is for regeneration indicators opportunity there is for regeneration dicates desireable location Daily ridership of station High ridership indicates desireable location MAR 7 15,592 Ridership 10,424 9,217 5,827 10.0% 10,009 11,051 528 7,144 5,095 15,592 3,713 10,424 5,266 9,217 5,827 2,856 9,276 10,009 7,036 11,051 6,837 528 7,144 9,504 5,095 8,561 3,713 13,865 to support local business (as of 02-06-2017) and high footfall to support local business 100% buildings indicates potential Large number of buildings indicates potential Number of buildings within 500m of s which can make land DEV 8 128 Land Assembly 296 604 998 25%893 of multiple owners 1,186 which can make 669land 2,736 128 1,670 296 2,663 4,251 604 998 5,068 893 4,699 1,186 3,844 1,470 669 959 2,736 1,670 1,071 602 2,663 station t assembly difficult wnership is potentially easier Hectares of land in public ownership Land in public ownership is potentially easier DEV 9 19.88 Ha Ownership 11.19 Ha 10.00 Ha 21.63 Ha 28.47 Ha 20% 16.65 Ha 8.70 Ha 6.64 Ha 19.88 Ha 10.24 5.63 Ha 11.19 Ha 3.68 Ha 10.00 Ha 2.78 Ha 21.63 Ha 0.00 Ha 28.47 Ha 3.87 Ha 16.65 Ha 3.85 8.70 Ha 6.64 Ha 3.34 8.26 Ha 10.24 Ha 20.44 Ha 5.63 Ha and in private ownership within 500m to develop than land in private ownership Hectares of non-developable land. evelopable land makes the Non-Developable High level of undevelopable land makes the DEV 10 2.76 Ha 1.86 Ha Quantifies1.37 risk Ha 0.00 Ha areas and leaveways for 0.00 Ha 25% 8.97 Ha 128.71 Ha 7.25 Ha 2.76 Ha 1.75 1.86 Ha 4.86 13.79 Ha 1.37 Ha 15.00 Ha 0.00 Ha 6.58 0.00 Ha 8.97 Ha 5.45 9.22 Ha 128.71 Ha 7.25 Ha 1.77 1.75 Ha 4.20 4.86 Ha 6.35 for TOD development Land area undesirable for TOD development public works within 500m ect to approved Land that is subject to approved Composite score of land within 1000m lans that is supportive of redevelopment plans that is supportive of DEV 11 7.27Planning Status9.56 10.01 that has a TOD 18.71 supportive plan 5%21.76 23.52 20.64 31.63 31.63 7.27 31.63 9.56 24.09 10.01 15.71 18.71 15.71 21.76 15.71 23.52 15.59 20.64 14.59 31.63 13.99 31.63 14.70 31.63 er to develop, with less TOD will be easier to develop, with less approved development approved friction to getting development approved cess to public utilities can Good current access to public utilities can Access to Access to reliable water and electricity evelopment more DEV 12 0.68 0.68 0.68 0.60 25%0.54 help make new 0.41 0.47 development more 0.49 0.49 0.68 0.35 0.68 0.32 0.68 0.51 0.60 0.33 0.54 0.37 0.41 0.58 0.47 0.53 0.49 0.53 0.49 0.53 0.35 Infrastructure supply viable/attractive 100% connections demonstrates a Number of public transport stops within High number of connections demonstrates a Connectivity to public nectivity, accessibility and TOD 13 3 4 500m (BRT, Feeder, 5 3 Dala Dala, Rail, 15% 4 high level of connectivity, 3 1 accessibility and 5 3 2 4 1 5 1 3 2 4 2 3 1 1 2 5 2 2 4 1 3 transport Ferry) mobility on / employment number A higher population / employment number nt indicates a greater degree Population and employment within a catchment indicates a greater degree TOD 14 4,869 29,376 Activity Density 51,136 68,376 46,906 20% 38,260 8,490 30,008 4,869 23,110 29,376 39,573 51,136 44,644 68,376 48,594 46,906 46,233 38,260 40,489 8,490 17,167 30,008 10,280 23,110 6,886 39,573 6,313 ing area could support TOD within 500m of station to which an existing area could support TOD activities iciently used should be Gross Floor Area Ratio (FAR) within Land that is inefficiently used should be TOD 15 0.390 1.426 Development Density 1.925 1.497 15%1.111 0.874 0.144 0.422 0.390 0.309 1.426 0.426 1.925 0.496 1.497 0.488 1.111 0.520 0.874 0.498 0.144 0.375 0.422 0.361 0.309 0.213 0.426 0.106 riority 500m of station optimised as a priority ue indicates a strong High entropy value indicates a strong Entropy value for land uses within uses and lower prevalance of TOD 16 0.39Mix of Uses 0.69 0.67 0.51 20%0.55 0.50uses and lower 0.35 diversity of land prevalance of 0.42 0.56 0.39 0.39 0.69 0.31 0.67 0.22 0.51 0.17 0.55 0.27 0.50 0.54 0.35 0.57 0.42 0.66 0.56 0.57 0.39 500m land uses mono-functional land uses near meterage of paths The higher the linear meterage of paths eater pedestrian connectivity Linear meterage of pedestrian paths would indicate greater pedestrian connectivity TOD 17 14 5 Block Permeability 15 22 15% 23 20 7 29 8 5 29 14 34 15 40 22 38 23 32 20 21 7 14 29 13 8 15 29 y with a higher potential to and routes and permeabaility with a higher potential to D development benefit from TOD development umber of facilities the greater No. of community assets within 500m The higher the number of facilities the greater TOD 18 7 6 Community Facilities 12 12 15% 10 13 10 9 8 6 11 7 9 12 8 12 10 10 13 10 6 9 7 8 5 11 support TOD of station the potential to support TOD 100% Table A.3.6 TOD Matrix Station Data ( Usalama to Shekilango ) Appendix A / Part A3 200 A3.2 B1-A-06 B1-A-07 B1-A-08 B1-A-09 B1-A-10 B1-A-11 B1-A-12 B1-A-13 B1-A-14 B1-A-15 B1-A-16 B1-A-01 17 B1-A-18 02 B1-A-19 03 B1-A-20 04 B1-A-21 05 B1-A-22 06 B1-A-23 07 B1-A-08 24 B1-B- A-01 09 B1- Magomeni High Value Kivukoni Magomeni Ger Fire Station Jangwani Cat ID Usalama Mwembechai Criteria Measurement Argentina Kagera Manzese Sub-Weighting Tip Top Rationale Urafiki Shekilango Ubungo Ubungo Terminal Posta Maji ya Zamani CityKibo Council Kona Kisutu Baruti DIT Bucha Fire Station Korogwe Jangwani Kimara Terminal Msimbazi Police Usalama Mwem Mapipa Desirable? Terminal Mapipa Term Investors will be attracted to neighbourhoods Attractiveness to Recent value growth 2012-16 & yield 2.00 1.50 MAR 3.00 1 2.50 2.50 2.50 2.50 15.0% 2.50 2.50 of growth and1.00 where there is eveidence the 1.00 1.50 1.00 2.00 1.00 3.00 1.00 3.00 2.00 3.00 2.00 3.00 2.00 3.00 1.50 2.50 3.00 3.00 2.50 Investors potential potential to earn a profit Recent development activity, recent Indicates the extent to which the business 1.50 1.50 MAR 2.00 2 1.50 Willingness 1.50 Business 1.50 planning applications and site 1.50 15.0% community is willing1.50 1.50 1.50 to invest in the station 1.50 2.00 2.50 2.00 2.50 1.00 2.50 1.00 2.50 1.00 1.50 1.00 1.50 1.00 1.50 1.00 2.00 3.00 1.50 assembly activity area Higher centrality of a node is linked to higher distribution of movements across the network 3,238 3,246 MAR 3,464 3 6,020 Nodal Value 5,838 5,794 Betweeness Centrality 5,910 15.0% 14,912 3,310 2,960 2,672 2,482 - 2,796 832 2,340 472 1,880 642 1,416 1,372 948 3,238 476 3,246 - 3,464 2,530 6,020 with greater growth potential and more employment opportunities Station PT Catchment Population catchment within 30 Large population catchment provides 708,387 750,912 MAR 836,401 4 840,794 851,139 862,906 860,964 15.0% 868,025 823,217 804,543 743,289 700,544 399,266 635,859 490,616 519,957 533,753 468,913 613,363 432,052 649,758 387,470 708,387 336,071 750,912 253,244 836,401 651,344 840,794 (Population) minutes of station quantum of customers to support businesses Station PT Catchment Employment catchment within 30 Indicates good access to employment 355,760 366,407 MAR 388,007 5 385,585 384,869 384,518 381,921 15.0% 382,932 364,469 353,720 314,221 272,416 281,816 232,724 304,967 168,663 314,666 151,270 332,441 141,337 341,872 127,985 355,760 112,447 366,407 87,006 388,007 345,611 385,585 (Jobs) minutes of station opportunities Income band, housing type, low Indicators show a general prosperity of the 3.50 3.00 MAR 2.00 6 2.25 Prosperity poverty indicators, 2.00 1.25 high wealth 2.50 15.0% area. The lower the1.75 2.50 1.00 prosperity the greater 0.75 0.25 0.75 0.75 - - 3.25 3.25 1.75 2.50 3.00 2.75 3.50 2.50 3.00 2.00 3.25 2.25 indicators opportunity there is for regeneration Daily ridership of station High ridership indicates desireable location 11,051 528 MAR 7,144 7 5,095 Ridership 3,713 5,266 2,856 10.0% 9,276 7,036 6,837 9,504 8,561 15,592 13,865 10,424 4,735 9,217 4,717 5,827 4,284 10,009 3,401 11,051 10,809 528 28,604 7,144 24,034 5,095 (as of 02-06-2017) and high footfall to support local business 100% Large number of buildings indicates potential Number of buildings within 500m of 1,186 669 2,736 DEV 8 Land Assembly2,663 1,670 4,251 5,068 25%4,699 3,844 which can make of multiple owners land 1,470 959 128 1,071 296 602 604 1,839 998 2,042 893 1,629 1,237 1,186 1,062 669 1,004 2,736 0 1,670 9 2, station assembly difficult Hectares of land in public ownership Land in public ownership is potentially easier 16.65 Ha 8.70 Ha 6.64 Ha DEV 9 10.24 Ha Ownership 5.63 Ha 3.68 Ha 2.78 Ha 0.00 Ha 20% 3.87 Ha 3.85 Ha 3.34 Ha 8.26 Ha 19.88 Ha 20.44 Ha 11.19 0.00 Ha 10.00 Ha 0.00 Ha 21.63 Ha 0.00 Ha 28.47 Ha 0.22 Ha 16.65 Ha 0.57 Ha 8.70 0.00 Ha 6.64 7.33 Ha 10.24 Ha 10.8 5.6 within 500m to develop than land in private ownership Hectares of non-developable land. Non-Developable High level of undevelopable land makes the 8.97 Ha 128.71 Ha 7.25 Ha DEV 10 1.75 Ha 13.79 4.86 Ha Quantifies risk areas Ha and leaveways 15.00 Ha for 6.58 Ha 25% 5.45 Ha 9.22 Ha 1.77 Ha 4.20 Ha 2.76 6.35 Ha 1.86 0.68 Ha 1.37 0.08 Ha 0.00 0.05 Ha 0.00 0.00 Ha 8.97 0.06 Ha 128.71 Ha 0.15 Ha 7.25 4.59 Ha 1.75 2.8 4.8 Land area undesirable for TOD development public works within 500m Land that is subject to approved Composite score of land within 1000m redevelopment plans that is supportive of 23.52 20.64 31.63 DEV 11 31.63 31.63 Planning Status 24.09 that has a TOD supportive plan15.71 5%15.71 15.71 15.59 14.59 13.99 7.27 14.70 9.56 15.71 10.01 15.71 18.71 15.71 21.76 15.71 23.52 15.71 20.64 15.71 31.63 23.06 31.63 21 31 TOD will be easier to develop, with less approved friction to getting development approved Good current access to public utilities can Access to Access to reliable water and electricity 0.41 0.47 0.49 DEV 12 0.49 0.35 0.32 0.51 25%0.33 help make new 0.37 0.58 development more 0.53 0.53 0.68 0.53 0.68 0.87 0.68 0.73 0.60 0.66 0.54 0.66 0.41 0.66 0.47 0.73 0.49 0.41 0.49 0 Infrastructure supply viable/attractive 100% Number of public transport stops within High number of connections demonstrates a Connectivity to public 3 1 TOD 5 13 2 1 1 500m (BRT, Feeder, Dala Dala, Rail, 2 15% 2 1 high level of connectivity, 2 accessibility and 2 3 4 4 3 5 1 3 1 4 2 3 2 1 5 3 2 1 transport Ferry) mobility A higher population / employment number Population and employment within a catchment indicates a greater degree 38,260 8,490 30,008 TOD 14 23,110 39,573 Activity Density 44,644 48,594 46,233 20% 40,489 17,167 10,280 4,869 6,886 29,376 6,313 51,136 17,315 68,376 10,303 46,906 5,202 38,260 5,530 8,490 4,762 30,008 4,205 23,110 133,402 39 33 within 500m of station to which an existing area could support TOD activities Gross Floor Area Ratio (FAR) within Land that is inefficiently used should be 0.874 0.144 0.422 TOD 15 0.309 0.426 Development Density 0.496 0.488 15%0.520 0.498 0.375 0.361 0.390 0.213 1.426 0.106 1.925 0.244 1.497 0.244 1.111 0.199 0.874 0.173 0.144 0.146 0.422 0.169 0.309 1.347 0. 500m of station optimised as a priority High entropy value indicates a strong Entropy value for land uses within 0.50 0.35 0.42 TOD 16 0.56Mix of Uses 0.39 0.31 0.22 0.27uses and lower 0.54 20%0.17 diversity of land prevalance of 0.57 0.66 0.39 0.57 0.69 0.21 0.67 0.24 0.51 0.30 0.55 0.28 0.50 0.33 0.35 0.25 0.42 0.25 0.56 0 500m mono-functional land uses The higher the linear meterage of paths Linear meterage of pedestrian paths would indicate greater pedestrian connectivity 20 7 TOD29 17 8 Block Permeability 29 34 40 15% 38 32 21 14 13 5 15 14 20 15 20 22 19 23 18 20 15 7 8 29 26 8 1 2 and routes and permeabaility with a higher potential to benefit from TOD development No. of community assets within 500m The higher the number of facilities the greater 13 10 TOD 9 18 11 8 Community Facilities 9 8 15% 10 10 10 6 7 6 5 7 3 12 0 12 5 10 1 13 0 10 4 9 12 8 1 of station the potential to support TOD 100% Table A.3.7 TOD Matrix Station Data ( Ubungo Terminal to Morocco Terminal ) Transit Station Evaluation Matrix A set of evaluation criteria is defined for this concept Evaluation criteria are grouped in like categories to facilitate options study. Evaluation criteria should cover as broader understanding. spectrum of issues as possible, and cover areas that may Each indicator’s definition, methodology and sources are have a significant influence on the project’s development or described in the following pages. ongoing viability. 201 Appendix A / Part A3 - TOD Matrix Methodology A3.2 Market Potential: Development Readiness: Rankings Rankings Rank Station Score Rank Station Score 1 Manzese 72.2 1 Kivukoni Terminal 79.2 2 Magomeni Hospital 71.7 2 DIT 78.8 3 Msimbazi Police 68.9 3 Kisutu 75.3 4 Gerezani Terminal 67.8 4 Posta ya Zamani 73.0 5 Morocco Terminal 65.6 5 Ubungo Maji 71.4 6 Morocco Hotel 62.9 6 City Council 70.8 7 Mkwajuni 62.4 7 Morocco Terminal 69.8 8 Magomeni Mapipa 61.7 8 Kibo 67.5 9 Argentina 61.0 9 Gerezani Terminal 65.9 10 Usalama 60.6 10 Kimara Terminal 65.2 11 Kagera 60.0 11 Korogwe 62.4 12 Mwanamboka 59.0 12 Msimbazi Police 61.6 13 Fire Station 58.5 13 Fire Station 61.5 14 Kinondoni 56.8 14 Bucha 61.3 15 Mwembechai 56.2 15 Usalama 61.3 16 Tip Top 55.2 16 Ubungo Terminal 60.8 17 Jangwani 52.4 17 Kona 60.1 18 DIT 50.2 18 Baruti 59.2 19 Kisutu 47.4 19 Shekilango 58.5 20 Urafiki 45.2 20 Magomeni Hospital 58.2 21 Kibo 42.3 21 Urafiki 57.1 22 Ubungo Maji 42.0 22 Morocco Hotel 54.5 23 Shekilango 41.4 23 Magomeni Mapipa 52.5 24 Kona 39.8 24 Mwembechai 46.6 25 Ubungo Terminal 39.5 25 Mwanamboka 40.2 26 Baruti 36.8 26 Jangwani 37.6 27 City Council 34.9 27 Mkwajuni 37.4 28 Kivukoni Terminal 34.8 28 Kinondoni 37.4 29 Posta ya Zamani 34.1 29 Tip Top 36.9 30 Bucha 33.3 30 Argentina 34.3 31 Korogwe 32.4 31 Kagera 32.4 32 Kimara Terminal 31.1 32 Manzese 27.7 Appendix A / Part A3 202 A3.2 TOD Characteristics: Overall Score: Rankings Rankings Rank Station Score Rank Station Overall Station Score 1 City Council 65.8 1 Msimbazi Police 63.2 2 Kisutu 58.8 2 DIT 62.6 3 Msimbazi Police 58.0 3 Magomeni Hospital 61.6 4 DIT 54.1 4 Gerezani Terminal 61.5 5 Magomeni Hospital 52.7 5 Kisutu 61.4 6 Posta ya Zamani 49.5 6 Morocco Terminal 58.2 7 Fire Station 49.1 7 Fire Station 57.3 8 Magomeni Mapipa 47.6 8 City Council 57.0 9 Gerezani Terminal 45.6 9 Magomeni Mapipa 54.5 10 Manzese 42.0 10 Morocco Hotel 54.1 11 Argentina 41.7 11 Posta ya Zamani 53.5 12 Morocco Hotel 41.3 12 Usalama 53.1 13 Mwembechai 38.9 13 Ubungo Maji 50.2 14 Kagera 38.4 14 Kivukoni Terminal 49.7 15 Mkwajuni 38.3 15 Mwembechai 48.0 16 Tip Top 37.4 16 Urafiki 47.8 17 Urafiki 36.5 17 Ubungo Terminal 46.9 18 Mwanamboka 35.7 18 Manzese 46.8 19 Ubungo Terminal 35.0 19 Mkwajuni 46.4 20 Kinondoni 30.0 20 Kibo 46.1 21 Usalama 29.3 21 Mwanamboka 45.7 22 Morocco Terminal 29.0 22 Argentina 45.5 23 Shekilango 28.1 23 Shekilango 44.9 24 Ubungo Maji 27.7 24 Kagera 43.9 25 Kivukoni Terminal 23.2 25 Tip Top 43.4 26 Baruti 21.7 26 Kinondoni 42.4 27 Jangwani 20.4 27 Baruti 42.0 28 Kimara Terminal 17.5 28 Kimara Terminal 41.4 29 Kibo 17.3 29 Kona 41.3 30 Bucha 16.0 30 Bucha 40.2 31 Kona 13.3 31 Korogwe 39.1 32 Korogwe 11.3 32 Jangwani 38.5 Page left intentionally blank DAR ES SALAAM METROPOLITAN DEVELOPMENT Produced for PROJECT The President’s Office Regional Administration and Local Government (PO-RALG)