UNITED REPUBLIC OF TANZANIA Prime Ministers Office for Regional Administration and Local Government

Transcription

1 UNITED REPUBLIC OF TANZANIA Prime Ministers Office for Regional Administration and Local Government The Dar es Salaam City Council CONSULTANCY SERVICES FOR THE CONCEPTUAL DESIGN OF A LONG TERM INTEGRATED DAR ES SALAAM BRT SYSTEM AND DETAILED DESIGN FOR THE INITIAL CORRIDOR ANNEX VOLUME 4 DEMAND MODELING AND FORECASTING CHAPTER 4 Draft Final Report Dar es Salaam April, 2007

2 ii TABLE OF CONTENTS 1. INTRODUCTION DEMAND MODEL CHARACTERISTICS GEOGRAPHIC BASE ROAD NETWORK ZONING AND SOCIO-ECONOMIC INFORMATION TRANSPORTATION MODES AND SERVICES FARE STRUCTURE TRAVEL TIME SUBJECTIVE VALUE PUBLIC TRANSPORTATION PASSENGER DEMAND MODEL CALIBRATION POPULATION AND DEMAND GROWTH FORECAST POPULATION EXPANSION DEMAND EXPANSION EXPANSION FACTORS MODELING RESULTS SCENARIO DEFINITION BASE AND FUTURE YEARS TO BE EVALUATED DART FARE STRUCTURE RESULTS ANNEXES...29 LIST OF TABLES TABLE 1 DAR ES SALAAM CITY SUBDIVISION SUMMARY 9 TABLE 2 FARE STRUCTURE SCENARIO FOR BRT 14 TABLE 3 POPULATION INDEX PER INCOME GROUP 18 TABLE 4 YEARLY POPULATION GROWTH FORECAST RESULTS 19 TABLE 5 DSM TRANSPORTATION NETWORK ORIGIN DESTINATION MATRICES FORECASTED (TOTAL TRIPS IN THE PEAK HOUR) 22 TABLE 6 YEAR EXPANSION FACTOR CALCULATION 23 TABLE 7 FARE STRUCTURE EVALUATED FOR OPERATIONAL DESIGN 25 TABLE 8 GENERALIZED TRAVEL TIME RESULTS 25 TABLE 9 PEAK HOUR RESULTS FARE STRUCTURE SELECTED 26 TABLE 10 DEMAND RESULTS PER STATION SCENARIO PEAK HOUR 27

3 iii LIST OF FIGURES FIGURE 1 EXAMPLE FOR DAR ES SALAAM CITY GEOGRAPHIC BASE-CBD AREA 5 FIGURE 2 EXAMPLE FOR ROAD NETWORK SYSTEM- CBD AREA 6 FIGURE 3 DAR ES SALAAM MODELING NETWORK GENERAL Y DETAIL VIEWS 8 FIGURE 4 TRANSPORT ZONES DIVISION 10 FIGURE 5 ZONING DETAIL WITH ZONE CODES 11 FIGURE 6 EMME2 MODELING ENVIRONMENT 16 FIGURE 7 LINEAR REGRESSION OF DEMAND MODEL RESULTS AT PEAK HOUR 17

4 iv ACRONYMS AND ABBREVIATIONS DSM: Dar es Salaam DCC: Dar es Salaam City Council PMU: Project Management Unit PMORALG: Prime Minister s Office for Local Government and Regional Administration TTSV: Travel Time Subjective Value GTC: Generalized Travel Cost TZS: Tanzanian Shillings GIS: Geographic Information System CBD: Central Business District DART: Dar Rapid Transit BRT: Bus Rapid Transit VBASu: Velocity Boarding and Alighting Survey ODSu: Origin Destination Survey Pax: Passenger

5 1 1. INTRODUCTION The process of planning a transportation system is in great part supported on the availability of a comprehensive and realistic method of depicting the existing conditions, regarding passenger demand values, travel times, corridor loads and vehicle fleet, among many others. Giving solution to this requirement, demand forecasting models have been implemented to approximate into a simulation the reality of transportation. This simulation is the combination of the preparation of a city s simplified road network, its public transportation lines and routes, and an existing trips distribution between production and attraction regions, the later mainly provided by a origin destination trip matrix. The validity of the model is verified and later adjusted by comparing the existing situation with the simulated on certain control points, containing information obtained from field surveys. The present volume explains the process undergone for constructing the demand forecast model for DSM.

6 2 2. DEMAND MODEL CHARACTERISTICS As aforementioned, the demand forecasting process for a transportation system can be summarized in three fundamental stages: potential demand calculation (origin destination matrix), transportation supply simulation (transport and road networks) and itinerary and services choice (models of modal distribution and allocation). A model makes understanding reality easier. By assuming simplifications in a complex phenomenon, one can select the most relevant aspects for that observation and assure that the relation among those characteristics is set in a way that reflects reality. These aspects are not exactly the same as reality, but correlate well with our understanding of it. One can then use the model for evaluation and planning either long-term or short-term planning. The transport demand modeling is based on the analysis and evaluation of trip strategies/alternatives between each origin and destination pair of zones. This strategy or choice for each user in the transportation network, depends as much on the transport supply (routes and frequencies), as of the costs of each possible combination of ways from the origin of the trip to the final destiny. For calculating the trip cost the time spent on each stage of the trip should be considered as well as the monetary cost of accessing each one of the of public transport vehicles boarded. The total times of trip can be disturbed in: Access from the origin to a public transportation stop or station. Waiting time. In vehicle travel time. Access between stops in case of transfers. Access from last stop to final destination. For modeling algorithm purposes, the weigh process performed for the different travel time and monetary cost components is expressed mathematically by an

7 3 equation, known in the transportation engineering as the Generalized Travel Cost, in the following form: Where: GTC = Tv + fw* Tw + fwa * Twa + GTC = Generalized Travel Cost Fare TTSV Tv fw Tw fwa Twa = In vehicle time = Walking time factor weighed against in vehicle time = Walking time = Waiting time weigh factor = Waiting time Fare = Transportation fare TTSV = Travel time subjective value for each user The analysis and election is based on the best option available for each trip to complete the travel desire from origin to destination by comparing the generalized cost of commuting, expressed in time units and choosing accordingly. Before the analysis is done, a crucial stage is the TTSV estimation and the population structure for which it will be applied, this value represents the equivalency in money of the travel time unit (e.g. X of TZS per minute traveled). This value is either obtained from stated preferences surveys discriminating the different income population or by determining an average population income based on local standards. Other elements such as waiting, in vehicle and walking time are calculated by the model algorithm. Waiting times and boarding probabilities are estimated based on the public transportation routes or available vehicles frequencies per route related to the combined routes frequency available on a single stop or boarding point.

8 4 By bringing together all the results from the modeling algorithm (boarding probabilities, generalized costs and travel times, modal choice for each trip within the origin destination matrix) the simulation then produces the results required such as operational information for registered transportation means and passenger demand volumes on the entire network for each mode registered. Structuring the model for optimal results requires the definition and consecution of the following: Geographic base map, positioning the city in global coordinates for accurate model referencing. Road network updated to existing accessibility conditions along which the transportation route network will be distributed. Regional division in transportation zones for travel demand representation. Existing public transportation routes itineraries, operational frequencies, vehicle typology and authorized fares applied. Weigh factors for generalized cost calculation. Estimation of TTSV. Origin destination trip matrix stating the travel desires between the transportation zones defined previously within the area of analysis. The entire modeling process for the DSM transportation network and later public transportation demand simulation was performed using the software emme2, developed by INRO Consultants. Further analysis and data information were simplified by simple calculations on spreadsheets and detailed revision and understanding of the process done should be completed before manipulating the simulation model information platform. TransCAD GIS 1 software, developed by Caliper Corporation, served as the primary GIS analysis tool used. 1 TransCAD GIS, Geographic Information System software, Caliper Corporation.

9 GEOGRAPHIC BASE For designing a transportation system involving road infrastructure definition and bus operations details, a thorough geographic base should be available and prepared as reference tool to the existing conditions. Detailed and updated cartography is required, for the present project, the PMU/DCC provided a geographic database, updated from early year 2000 based on digitalization form aerial photographs of DSM. The following figure is the geographic base of Dar es Salaam City. Figure 1 Example for Dar es Salaam City Geographic Base-CBD Area 2.2. ROAD NETWORK Though the geographic base was updated enough to the existing conditions, the road network was incomplete and lacked detail given the precision and

10 6 refinement required for structuring the demand forecasting model and the simulation platform for emme2 to run appropriately. This road network, was updated and corrected based on the digital cartography (geographic base) obtained from the client (DCC) (see figure 2). Main characteristics like number of lanes per road, street names and directions, etc. were added, when possible, as backup data for the digital geographic information file. No further analysis was done since the available information such as road condition, hierarchy, and infrastructure improvement, among others, was either never available or not included/processed in the database obtained. Figure 2 Example for Road Network System- CBD Area

11 7 Summarizing, the road network prepared for the simulation model contains (see figure 3): 271 Centroids. They are virtual connections that represent the transport zones where the travels is generated or attracted links of the road network. Represent the road sections between intersections links used by transportation routes. Observed Daladala speed flow obtained from VBASu2 introduced in the model as a link attribute. 2 Please refer to Annex Volume 3 Data Collection and Calibration

12 8 Figure 3 Dar es Salaam Modeling Network General y detail Views

13 ZONING AND SOCIO-ECONOMIC INFORMATION Dar es Salaam is divided into three municipalities. The three municipalities are Kinondoni on the Northern region, Ilala on the central and southeastern regions and Temeke on the south and southwestern areas, each one having regional autonomy and local administration. For administration and management issues, the municipalities subdivide into wards on a first stage and then into sub wards (see table 1). The analysis done used the existing division and adjusted it to the simulation model s requirements of representing the areas based on its attractor or generator of trips characteristics. Table 1 Dar es Salaam City Subdivision Summary Municipality Wards Subwards Transportation Zones Kinondoni Ilala Temeke Total The definition of transportation zones was mainly supported on the existing subward division (figure 4), with participation form the ward level division. The absence of a street numeration or nomenclature made difficult the definition or assumption of a new division scheme and considering that this division was done based on regional characteristics, administrative alikeness and/or land use similarities, the zoning process was based solely on sub ward level on the urbanized areas and ward level on the city s outskirts.

14 10 Figure 4 Transport Zones Division Nevertheless the advance the sub ward and ward divisions offered to the model set up process, detailed analysis had to be carried out particularly on zones too big to be considered a homogeneous demand zone. The process then was focused to the re-division of these zones, detailing the precision on demand forecasting and transportation supply and coverage 3, particularly on the CBD and along DART corridors on Morogoro Road and Kawawa Road. Socio-economic information is classified following ward-subward division and mainly supported on the national census from , National Bureau of Statistics and World Bank information, basically on the matters of modal choice shares, average road conditions, employment levels and activities, poverty, population growth, etc. 3 See Annex Volume 3 - Field Surveys and Data Calibration Section

15 11 As a result, 271 zones were defined for structuring the simulation model, and are discriminated as seen in Table 1 and figure 5. Figure 5 Zoning Detail with Zone Codes 2.4. TRANSPORTATION MODES AND SERVICES As commonly found in modern urban centers, the citizens commute basically either on private or public transportation. Private modes are those varying from pedestrian access to private vehicles, enclosed in between bicycles, motorcycles, man powered carts, animal powered chariots, and many others. Public transportation modes comprise the flow of buses, microbuses and taxis serving the commuters under the charges of fixed and variable fares, depending on distance traveled or vehicle boarding. Dar es Salaam transportation network supports the movement of 5 basic means of transportation or modes, grouped as usual on private and public use. On the

16 12 private modes: pedestrians, bicycles (including tricycles) and private cars and on the public modes: taxi cars and public daladala buses and microbuses. The demand simulation and forecast model implemented for Dar es Salaam considered a public transportation trip matrix. As usual along with this configuration and considering that in every trip generated there is a portion of it done by foot, a pedestrian mode was allowed. Likewise, representing the existing situation the network supports daladala mode and eventually private modes. Based on the defined available modes, the present public transportation system network was included in the model represented by daladala routes. The itineraries were drawn and adjusted on top of the road network previously defined, with PMU assistance and support. There is no other major public transportation mean within DSM so the analysis just focused on those daladala routes identified by PMU staff and updated during the field surveys. Summarizing the transportation modes included in the simulation model are: Public Transportation Daladala Routes Public Transportation DART Services Public Transportation Feeder Services Pedestrian 2.5. FARE STRUCTURE The system currently considers the charges of a standard and generalized fare for boarding daladalas of TZS 200 (value of the Base Scenario). In the future scenarios, fares for the different components of DART system have been separated into two groups depending on the level of fare integration desired and which offers better financial and economic scenarios for the correct system s operation routes obtained as legally authorized routes by the time of the model elaboration. (April 2005)

17 13 The fare element is considered a complementary and crucial input for simulation and modeling purposes which allows the valuation of travel generalized costs for each user through the different modes their modal choice takes them. Pedestrian modes are not being fare penalized allowing their flow in every available link of the network except those segregated links along the trunk corridors, avoiding undesired and non realistic pedestrian movements on this imaginary links. The trunk system network has been structured and modeled as segregated parallel network to the existing road network. Access and transferences between these two components (networks) is done through special access modes, allowing the identification and quantification of passenger demand volumes. The fare is charged to the pedestrian user as an additional time equivalent to the individual passenger fare or additional time penalization due to connection and/or transfer delays or commute time between integration stations 6, when applicable. Exit movements on pedestrian modes are not penalized and represent no charge to the user. Developed in parallel with the financial and economical evaluations, the final fare structure scenario defined for a future DART operation, and based on the available and feasible fare structures applicable for the local situation, the simulation was done for a structure as shown in Table 2. DART services and the eventual number of boardings done to one or many trunk services are independent with the fare paid per user, allowing the possibility of boarding as many services as desired with no additional charges other than the initial paid fare and transfer and waiting time penalization. Transfer penalty is weighed based on the inconvenience a vehicle change represents to the user and always is referred as time consumption charge. 6 Feeder integration at intermediate stations and terminals.

18 14 Table 2 Fare Structure Scenario for BRT Fare (TZS) Transportation Mode DART Only User 400 Feeder Only User (1 Route) 400 Feeder Only User (2 Routes) 800 Trunk + Feeder User 500 Daladala User 300 Feeder to Trunk 100 Trunk to Feeder 0 Trunk to Daladala 300 Daladala to Trunk 400 Daladala to Feeder TRAVEL TIME SUBJECTIVE VALUE The modal choice induced by the demand modeling process is mainly directed by this value and its correct estimation. Estimation procedure followed includes the income level standards identification for the population, which can be done based on social division by wealth or simply by average income. Dar es Salaam income distribution is predominantly represented by low income classes 7, also the ones that stand for the highest readership in public transportation. Therefore, for calculating the TTSV for the city, the analysis was focused in obtaining an average population income amount. With the assistance and knowledge of local experts the analysis then assumes the following labor legislation facts and common practice information for employers and employees for the final TTSV calculation: Monthly hours worked: 160 Monthly average salary: TZS Please refer to Annex Volume 2 Background in Public Transportation for Income level estimates and distribution performed for Dar es Salaam population. 8 Done in October 2005.

19 15 Based on general experience and knowledge from other cities with extensive analysis of stated and revealed preferences, form weighing the value of traveled time against the worked time, the first one is valued as a third of the last, this will mean that per three minutes worked, a user would be willing to spend one for commute or travel. Travel/Work Time Factor: 1/3 Following on the analysis: Monthly Average Working Salary Hours/Month TZS/Hour Travel Cost/Hour TZS 144,000 TZS 160 TZS 900 TZS 300 This value for hour is equivalent to TZS 5 per minute and so, represents the TTSV for Dar es Salaam transport demand simulation model. Furthermore and explaining this value and the effect it has, an average user will agree walking 40 minutes to avoid paying one standard daladala fare (TZS 200) PUBLIC TRANSPORTATION PASSENGER DEMAND Following the process of structuring the model, the demand source was established as an origin destination trip matrix. Surveys were carried out for approximately two and a half months, one of which was directed to identify the trip desires, later being basic material to build a trip matrix between the transportation zones previously defined for the network. Bearing in mind the necessity of identifying the current system s critical situation or period of time where the largest volume of people is simultaneously onboard a public service and actually traveling, the combination between two different measurements (all day and morning period surveyed points) enable the identification of the peak hour to be from 07:00 to 08:00 (see figure 6).

20 16 Figure 6 Emme2 Modeling Environment Summarizing the process carried out, approximately 33,000 people were surveyed on 35 points all over the city. For the Origin Destination Survey (ODSu) completed, and regarding the issue of considering within the passenger demand analysis the double counting of one single trip between different survey points, the database depuration executed cut off this redundant information through an adjustment process by identifying this Origin/Destination pairs passing through several sections along its path in conjunction with the daladala route in which the trip was surveyed. After these adjustments and double counting elimination the origin destination matrix for the peak hour contained 123,047 trips MODEL CALIBRATION Upon information collected form field surveys (dispatch frequencies, passenger volumes, boarding and alighting passengers at stations, etc.) the model s calibration process was executed.

21 17 Having the survey points as control points within the model to monitor the gap between the modeled and existing situations, the model was adjusted to match the existing conditions by internal trip matrix internal adjustments procedures. Indicating the quality level reached by the calibrated model, a linear regression is performed to compare statistically the accuracy and approximation achieved by the demand simulation offered by the model (see figure 7). The regression quality and data relation (between modeled and observed data) are also evaluated by measuring the correlation index R 2 and angle coefficient. For this process the values reached are 0.98 and 1.0 respectively. Again, the analysis was carried out by comparing the control point information with modeled one. Figure 7 Linear Regression of Demand Model Results at Peak Hour

22 18 3. POPULATION AND DEMAND GROWTH FORECAST 3.1. POPULATION EXPANSION With a transportation simulation model calibrated for the existing stage at hand and relevant information regarding present conditions already processed, the course was then directed to establish a future scenario for which DART system will be up un running and from then forecast the operation by expanding the trip matrix based on population travel patterns and inner city growth and expansion. The date suggested by the Client was 2009 to hold the inaugural day for DART being operational. From year 2002, Tanzania census information was available and served as a reference year to begin a forecasting analysis, to be based on regional growth, along with existing and maximum (critical) population density values per transportation zone. The maximum density was determined according to area income group as follows: Table 3 Population Index per Income Group Income group inhabitants/ hectare The model adopted assumed a population expansion made for every zone i starting from year Using a logistic model: With: Pi ( t + 1 ) = Pi( t) ( t) () i Pi 1+ k P max 1 Ptot() t P maxtot P max (i)= P max (i)= Maximum population for zone i max(1.2*pi(2002), area x maximum density)

23 19 Pi(2002)= Population on 2002 year for zone i K= Constant for all zones= P maxtot = Maximum admitted total Dar population= 20 million Following, K and P maxtot were adjusted in order to obtain an average of yearly growth index (n) for the period , and a total increase ratio of Ptot(2032) on the period. = (This is the growth starting Ptot(2002) with and slowing down to in 2032). The yearly estimatives of Dar es Salaam population are: 3.2. DEMAND EXPANSION Table 4 Yearly Population Growth Forecast Results Year Population Year Population ,487, ,778, ,596, ,940, ,709, ,104, ,824, ,271, ,944, ,440, ,066, ,611, ,192, ,784, ,320, ,960, ,452, ,137, ,588, ,317, ,726, ,498, ,867, ,681, ,012, ,865, ,159, ,050, ,310, ,237, ,463, ,425, ,619, ,614,459 The trips expansion was made for total origins, total destinations and by applying the Fratar model for obtaining the forecast information for years 2015, 2025 and 2035, using 2005 morning peak demand matrix as base. The total trip origins by zone i was based on simple rule of 3 for population:

24 20 V ot (i,t)= P(i,t)= forecast). V ot ( i, t) = V ot P( i, t) P( i,2005) ( i,2005) Total origins for zone i at year t Population of zone i on year t (according to population For the destination analysis and forecast by zone it was assumed for each zone a logistic curve: Where: Vd(i,t)= K= Vd max (i)= tm(i)= ( Vd ( i) Vd i, t) 1+ e i = max k ( t tm( )) (1) Total destination by zone in year t Constant Maximum destination for zone i Specific logistic parameter adjusted for zone i Where: Vd max t(i)= ( Vd( i,2005), V t( i) ) Vd max ( i) = max 1.2 max Is the theoretical maximum attraction value for this zone: Where: Vd ( i) = area( i) fdb acc( i) max n Area(i) = fdb= Area in square meters of zone i Constant (maximum attraction per square meters) n= Parameter = 2 fdb= Constant adopted=150 trips/hectare

25 acc(i)= Where: Accessibility index of zone i 21 ( m) () i tmed acc( i) = which is the t average trip time( on public network) from all zones to zone i med t med (i)= to zone i t med (m)= Trip duration(on public transportation network) from all zones Minimum off all t med (i) From (1) applied to the basic year (2005) and a given year t, we can obtain for each zone i Where: G= e Vd( i, t) k ( 2005 t) = Vd max ( i) Vd max ( i) 1+ G Vd( i,2005) 1 For t=2005 and G=1, ( i, t) Vdt( i,2005) Vd ( i, t ) = Vd max( i ) Vd =, for t>>2005 and G=0 G is adjusted for each basic year forecast (2015, 2025, and 2035) to obtain parity between Total origins and Total destinations. Vd ( i) = Vtd = Vo( i) = i Vtd

26 22 Table 5 DSM Transportation Network Origin Destination Matrices Forecasted (total trips in the peak hour) Year Trip Matrix , , , , , , , EXPANSION FACTORS Usual calculations and analysis are performed based on the critical period of time (peak hour) during which the operational conditions are at their top and the system is working on full throttle. The morning peak was identified as the one appropriate to be modeled from 07:00 to 08:00. However, further analysis and calculations require obtaining daily, monthly and yearly figures. Expansion factors then are estimated based on the share of representation the peak hour has into the daily demand, the day into the month and/or into the year. From the data collection phase and field surveys, a selection of 6 points located on the heaviest points of passenger volumes flows occur around the city, was subjected to all day counts from 05:00 to 21:00. With the figures obtained from these counts and that from the peak hour, a peak hour to day ratio was calculated thus obtaining the Day Expansion Factor of 10.7 for Dar es Salaam public transportation system. For operational index expansion, particularly on traveled kilometers for trunk, feeder and daladala vehicles, the day expansion factor obtained was 14. Year expansion factor requires the identification within the local calendar year, the amount of public holidays (national heritage holidays, school holidays and any other extraordinary date), amount of Saturdays and Sundays and effective weekdays. From experience, demand for each type of day behaves as a percentage of an average weekday. The Table 6 shows the calculation made.

27 23 Table 6 Year Expansion Factor Calculation Day Type Year Amount Weekday % Value in Weekdays Saturdays Sundays National Holidays Private School Holidays Public School Holidays Total Year Expansion Factor 300

28 24 4. MODELING RESULTS Parallel to the elaboration and preparation of the demand model, a hypothetical set of fare structure scenarios was prepared for the initial year of DART operation, considering that this is the one most important element when it comes to evaluate the feasibility and self sustainability of the entire system SCENARIO DEFINITION Establishing the starting point was the definition and refinement for the current scenario, which was already calibrated and adjusted in a way that the simulation offered excellent standards for depicting and simulating the current public transportation events (without DART) BASE AND FUTURE YEARS TO BE EVALUATED From views obtained from the client and based on the political will for having DART operational on 2009 first quarter, the first phase scenario was set to happen on that year, thus using the figures of population and demand forecasted. Along with this, a long term project assessment was prepared for generating demand and operational data for the years 2012, 2016, 2020, 2025 and Base scenario for reference was determined to be 2005 for being this the one the data collection took place and the actual demand study was developed DART FARE STRUCTURE In the future scenarios were considered for the current daladala services a fare of TZS 300 with little exceptions of TZS 400 on certain long routes. DSM population, and particularly that riding the public transport, is considerably sensible to fare changes. Local experiences carried out in recent years on the matter of daladala fare policy changes had shown to be catastrophic for the city arriving to strikes, both from users and operators. Each proposal had to determine the impact on the user of the new transportation system as well as the system s financial well-being.

29 25 The system as structured will have three different modes all of them with potential different fare. These modes are DART trunk services, Feeder services and part of the DART BRT system and Daladala routes. Giving more options and flexibility to arrive to a decision for evaluating the financial feasibility and operational design of the project, the shows the fare structure selected according to the financial model and the alternative fares evaluated. Table 7 Fare Structure Evaluated for operational design Fare Structure (TZS) Selected* Alternative 1 Alternative 2 Alternative 3 Alternative 4 Tariff - Trunk Only Tariff - Trunk+Feeder Tariff - Feeder Only Daladala *Is the same for financial model RESULTS The results presented include demand, operational values and generalized travel time data for the scenario aforementioned (selected) in the peak morning hour. The annex 1 includes the results for the alternatives 1 to 4. Time Table 8 Generalized Travel Time Results Base Scenario 2008 Scenario Dart 2009 In vehicle Time (min) mf81 4,269,917 3,910,685 Auxiliary modes Time(min) mf82 2,975,012 3,159,158 Waiting Time (min) mf63 176, ,740 Fare Time (min) mf85 6,069,071 6,450,783 Transfer Penalty (min) 0 179,302 Generalized Time (min) mf84 16,641,998 17,280,562 Generalized Time/pax (min) Time In Vehicle + Time Aux /pax (min) Assigned Demand 137, ,669 Average Fare/pax (TZS) Time Saved per Pax (min) 1.3

30 26 Table 9 Peak Hour Results Fare Structure Selected Attributes Current Situation Scenario 2008 No DART Situation Scenario 2035 DART 1st Phase Scenario DART 1st Phase Scenario DART 1st Phase Scenario 2016 DART 1st Phase Scenario 2020 DART 1st Phase Scenario 2025 DART 1st Phase Scenario 2035 Basic Information Extension (km) km km km km km km Number of Stations Terminals Bus Depots Trunk Services Feeder Routes Daladala Routes 191? Trip Matrix 138, , , , , , ,585 Demand Daladalas Total Boardings 149, , , , , , ,877 Average Occupancy Demand DART Total DART System Peak Hour 36,720 40,576 43,930 49,418 54,491 66,642 Total Boardings System 49,933 55,177 60,471 68,027 75,694 93,331 Boardings DART Services 35,615 39,354 42,238 47,515 51,940 63,020 Boardings Feeder Routes 14,319 15,823 18,233 20,512 23,754 30,311 Pax Paying Trunk 35,615 39,354 42,238 47,515 51,940 63,020 Pax Paying Feeder fare Only 1,106 1,222 1,692 1,903 2,551 3,622 Passengers Riding Trunk Only 21,936 24,239 24,922 28,035 29,501 34,567 Passengers Riding Trunk+Feeder 13,678 15,115 17,316 19,480 22,439 28,453 System Efficiency Indexes Pax.Km Trunk 235, , , , , ,451 Pax.Km Feeder 49,283 54,457 64,280 72,312 86, ,470 Pax.Km Daladala 1,212,533 2,950, ,013 1,086,220 1,320,077 1,485,058 1,811,002 2,412,279 Pax.Hour Trunk 9,909 10,950 12,009 13,510 15,085 18,668 Pax.Hour Feeder 2,382 2,632 3,087 3,473 4,101 5,337 Pax.Hour Daladala 71, ,072 55,318 61,126 73,693 82, , ,943 Veh.Traveled Kms Trunk 3,152 3,462 3,475 3,933 4,302 5,301 Veh.Traveled Kms Feeder 1,866 2,059 2,387 2,667 3,179 4,197 Veh.Traveled Kms Daladala 100, ,566 85,807 94, , , , ,203 Veh.Traveled Time Trunk Veh.Traveled Time Feeder Veh.Traveled Time Daladala 5,925 5,925 4,693 5,194 6,090 6,856 8,186 10,777 Bus Operations total Traveled Kms Trunk 3,151 3,462 3,475 3,933 4,302 5,301 Trunk Operational Fleet Trunk Fleet + Reserve Traveled Kms Feeder 1,817 2,059 2,387 2,667 3,179 4,197 Feeder Operational Fleet Feeder Reserve Fleet Traveled Kms Daladala 101, ,933 85,807 94, , , , ,203 Daladala Operational Fleet 6,041 13,594 4,854 5,350 6,250 7,017 8,339 10,938

31 27 Table 10 Demand Results per Station Scenario Peak Hour Station Boardings Thru Alighting Corridor Code Name Initial Transfer Total Passengers Final Transfer Total Kawawa 7102 Kinondoni Mjini Kawawa 7200 Mwinyijuma Kawawa 7202 Kanisani Kawawa 7300 Kinondoni A Kawawa 7302 Usalama Kawawa 8111 Morocco Terminal Morogoro Kimara Terminal Morogoro Kimara Resort Morogoro Kimara Thomas Morogoro Baruti Morogoro Corner Morogoro Kibo Morogoro Chai Bora Morogoro Ubungo Tanesco Morogoro Ubungo Terminal Morogoro Shekilango Morogoro Urafiki Mahakama Morogoro Tip Top Morogoro Bakheresa Morogoro Manseze Argentina Morogoro Magomeni Kagera Morogoro Mwembe Chai Morogoro Baptist Church Morogoro Magomeni Mapipa Morogoro Jangwani Morogoro Fire Station Morogoro Lybia Street Morogoro City Council Kiv Front Old Posta Kiv Front National Bank Kiv Front Bibititi Kiv Front Kivukoni Terminal Msimbazi Kariakoo Market Msimbazi Kariakoo Terminal Feeder Virtual Integration Feeder Physical Integration From the table: Initial Boarding: Is the number of passengers that board a transit vehicle of the first line used on their trip. Transfer Boarding: The number of passengers that board a transit line, after alighting from another line. Transfer Alighting: The number of passengers that alight from a transit line, in order to transfer to another transit line. Final Alighting: this is the number of passengers that alight from a transit vehicle of the last line used on their trip.

32 28

33 29 5. ANNEXES Annex 1. Peak Hour Results Fare Structure Alternative 1 Attributes No DART Current Situation Situation Scenario Scenario DART 1st Phase Scenario DART 1st Phase Scenario DART 1st Phase Scenario DART 1st Phase Scenario DART 1st Phase Scenario DART 1st Phase Scenario Basic Information Extension (km) km km km km km km Number of Stations Terminals Bus Depots Trunk Services Feeder Routes Daladala Routes 191? Trip Matrix Demand Daladalas Total Boardings Average Occupancy Demand DART Total DART System Peak Hour Total Boardings System Boardings DART Services Boardings Feeder Routes Pax Paying Trunk Pax Paying Feeder fare Only Passengers Riding Trunk Only Passengers Riding Trunk+Feeder System Efficiency Indexes Pax.Km Trunk Pax.Km Feeder Pax.Km Daladala Pax.Hour Trunk Pax.Hour Feeder Pax.Hour Daladala Veh.Traveled Kms Trunk Veh.Traveled Kms Feeder Veh.Traveled Kms Daladala Veh.Traveled Time Trunk Veh.Traveled Time Feeder Veh.Traveled Time Daladala Bus Operations total Traveled Kms Trunk Trunk Operational Fleet Trunk Fleet + Reserve Traveled Kms Feeder Feeder Operational Fleet Feeder Reserve Fleet Traveled Kms Daladala Daladala Operational Fleet

34 30 Annex 2. Peak Hour Results Fare Structure Alternative 2 Attributes Current Situation Scenario DART 1st Phase Scenario 2008 DART 1st Phase Scenario 2012 DART 1st Phase Scenario 2016 DART 1st Phase Scenario 2020 DART 1st Phase Scenario 2025 DART 1st Phase Scenario 2035 Basic Information Extension (km) km km km km km km Number of Stations Terminals Bus Depots Trunk Services Feeder Routes Daladala Routes Trip Matrix 138, , , , , , ,585 Demand Daladalas Total Boardings 149, , , , , , ,082 Demand DART Total DART System Peak Hour 41,021 46,974 51,187 57,582 63,834 78,440 Total Boardings System 65,330 74,812 83,042 93, , ,754 Boardings DART Services 39,024 44,687 48,237 54,264 59,581 72,548 Boardings Feeder Routes 26,306 30,125 34,805 39,152 45,525 58,206 Pax Paying Trunk 39,024 44,687 48,237 54,264 59,581 72,548 Pax Paying Feeder fare 1,997 2,287 2,950 3,318 4,253 5,892 Passengers Riding Trunk Only 16,455 18,843 19,019 21,395 22,085 25,410 Passengers Riding Trunk+Feeder 22,569 25,844 29,218 32,869 37,496 47,138 System Efficiency Indexes Pax.Km Trunk 248, , , , , ,231 Pax.Km Feeder 103, , , , , ,046 Pax.Km Daladala 1,212, ,293 1,005,700 1,224,604 1,377,653 1,682,058 2,242,223 Pax.Hour Trunk 10,519 12,045 13,279 14,938 16,747 20,790 Pax.Hour Feeder 5,687 6,512 7,675 8,634 10,300 13,519 Pax.Hour Daladala 71,181 49,447 56,621 68,359 76,902 93, ,468 Veh.Traveled Kms Trunk 3,181 3,619 3,737 4,181 4,559 5,564 Veh.Traveled Kms Feeder 3,658 4,162 4,827 5,437 6,434 8,391 Veh.Traveled Kms Daladala 100,566 76,917 87, , , , ,537 Veh.Traveled Time Trunk Veh.Traveled Time Feeder Veh.Traveled Time Daladala 5,925 4,191 4,819 5,638 6,337 7,545 9,919 Bus Operations total Traveled Kms Trunk 3,181 3,619 3,737 4,181 4,559 5,564 Trunk Operational Fleet Trunk Fleet + Reserve Traveled Kms Feeder 3,658 4,162 4,827 5,437 6,434 8,391 Feeder Operational Fleet Feeder Reserve Fleet Traveled Kms Daladala 101,826 76,917 87, , , , ,537 Daladala Operational Fleet 6,041 4,354 4,962 5,791 6,496 7,694 10,078

35 31 Annex 3. Peak Hour Results Fare Structure Alternative 3 Attributes No DART Current Situation Situation Scenario Scenario DART 1st Phase Scenario DART 1st Phase Scenario DART 1st Phase Scenario DART 1st Phase Scenario DART 1st Phase Scenario DART 1st Phase Scenario Basic Information Extension (km) km km km km km km Number of Stations Terminals Bus Depots Trunk Services Feeder Routes Daladala Routes 191? Trip Matrix Demand Daladalas Total Boardings Average Occupancy Demand DART Total DART System Peak Hour Total Boardings System Boardings DART Services Boardings Feeder Routes Pax Paying Trunk Pax Paying Feeder fare Only Passengers Riding Trunk Only Passengers Riding Trunk+Feeder System Efficiency Indexes Pax.Km Trunk Pax.Km Feeder Pax.Km Daladala Pax.Hour Trunk Pax.Hour Feeder Pax.Hour Daladala Veh.Traveled Kms Trunk Veh.Traveled Kms Feeder Veh.Traveled Kms Daladala Veh.Traveled Time Trunk Veh.Traveled Time Feeder Veh.Traveled Time Daladala Bus Operations total Traveled Kms Trunk Trunk Operational Fleet Trunk Fleet + Reserve Traveled Kms Feeder Feeder Operational Fleet Feeder Reserve Fleet Traveled Kms Daladala Daladala Operational Fleet

36 32 Annex 4. Peak Hour Results Fare Structure Alternative 4 Attributes No DART Current Situation Situation Scenario Scenario DART 1st Phase Scenario DART 1st Phase Scenario DART 1st Phase Scenario DART 1st Phase Scenario DART 1st Phase Scenario DART 1st Phase Scenario Basic Information Extension (km) km km km km km km Number of Stations Terminals Bus Depots Trunk Services Feeder Routes Daladala Routes 191? Trip Matrix Demand Daladalas Total Boardings Average Occupancy Demand DART Total DART System Peak Hour Total Boardings System Boardings DART Services Boardings Feeder Routes Pax Paying Trunk Pax Paying Feeder fare Only Passengers Riding Trunk Only Passengers Riding Trunk+Feeder System Efficiency Indexes peak hour Pax.Km Trunk Pax.Km Feeder Pax.Km Daladala Pax.Hour Trunk Pax.Hour Feeder Pax.Hour Daladala Veh.Traveled Kms Trunk Veh.Traveled Kms Feeder Veh.Traveled Kms Daladala Veh.Traveled Time Trunk Veh.Traveled Time Feeder Veh.Traveled Time Daladala Bus Operations total Traveled Kms Trunk Trunk Operational Fleet Trunk Fleet + Reserve Traveled Kms Feeder Feeder Operational Fleet Feeder Reserve Fleet Traveled Kms Daladala Daladala Operational Fleet