prime ministers office for regional administration …prime ministers office for regional...

UNITED REPUBLIC OF TANZANIA Prime Ministers Office for Regional

Administration and Local Government

The Dar es Salaam City Council

ANNEX VOLUME 4 DEMAND MODELING AND FORECASTING CHAPTER 4 Draft Final Report

CONSULTANCY SERVICES FOR THE CONCEPTUAL DESIGN OF A LONG TERM INTEGRATED DAR ES SALAAM BRT SYSTEM AND DETAILED DESIGN FOR THE INITIAL CORRIDOR

Dar es Salaam April, 2007

ii

TABLE OF CONTENTS

1. INTRODUCTION.............................................................................................................1

2. DEMAND MODEL CHARACTERISTICS...................................................................2

2.1. GEOGRAPHIC BASE.........................................................................................................5 2.2. ROAD NETWORK.............................................................................................................5 2.3. ZONING AND SOCIO-ECONOMIC INFORMATION ..........................................................9 2.4. TRANSPORTATION MODES AND SERVICES .................................................................11 2.5. FARE STRUCTURE .........................................................................................................12 2.6. TRAVEL TIME SUBJECTIVE VALUE .............................................................................14 2.7. PUBLIC TRANSPORTATION PASSENGER DEMAND......................................................15 2.8. MODEL CALIBRATION..................................................................................................16

3. POPULATION AND DEMAND GROWTH FORECAST.........................................18

3.1. POPULATION EXPANSION.............................................................................................18 3.2. DEMAND EXPANSION....................................................................................................19 3.3. EXPANSION FACTORS ...................................................................................................22

4. MODELING RESULTS.................................................................................................24

4.1. SCENARIO DEFINITION.................................................................................................24 4.1.1. BASE AND FUTURE YEARS TO BE EVALUATED...........................................................24 4.1.2. DART FARE STRUCTURE ............................................................................................24 4.2. RESULTS ........................................................................................................................25

5. 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 2009 . PEAK HOUR ______________________27

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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

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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

1 Demand Modeling and Forecasting

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.


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


equation, known in the transportation engineering as the Generalized Travel

Cost, in the following form:

TTSVFareTwafwaTwfwTvGTC +++= **

Where: GTC = Generalized Travel Cost

Tv = In vehicle time

fw = Walking time factor weighed against in vehicle time

Tw = Walking time

fwa = Waiting time weigh factor

Twa = 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.


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 GIS1 software, developed by Caliper Corporation, served as the

primary GIS analysis tool used.

1 TransCAD GIS, Geographic Information System software, Caliper Corporation.


2.1. 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


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


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.

8838 links of the road network. Represent the road sections between

intersections.

4443 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


Figure 3 Dar es Salaam Modeling Network – General y detail Views


2.3. 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 27 131 115

Ilala 22 101 70 Temeke 24 156 86

Total 73 388 271

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.


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 coverage3, 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 20024, 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 4.9 4 http://www.tanzania.go.tz/census/census


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


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 1915 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.

5 191 routes obtained as legally authorized routes by the time of the model elaboration. (April 2005)


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 stations6, 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.


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 400

2.6. 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 classes7, 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 144.0008

7 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.


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 Salary

Working Hours/Month TZS/Hour Travel

Cost/HourTZS 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).

2.7. 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).


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.

2.8. 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.


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 R2 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


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

1 630 2 630 3 490 4 350 5 210 6 70

The model adopted assumed a population expansion made for every zone i

starting from year 2002. Using a logistic model:

( ) ( ) ( )( )

( )⎟⎠⎞

⎜⎝⎛ −∗⎟⎟

⎠

⎞⎜⎜⎝

⎛⎟⎟⎠

⎞⎜⎜⎝

⎛∗+∗=+

totPtPtot

iPtPiktPitPi

max1

max11

With:

Pmax(i)= Maximum population for zone i

Pmax(i)= max(1.2*Pi(2002), area x maximum density)


Pi(2002)= Population on 2002 year for zone i

K= Constant for all zones= 0.0691

Pmaxtot= Maximum admitted total Dar population= 20 million

Following, K and Pmaxtot were adjusted in order to obtain an average of 0.043

yearly growth index (n) for the period 2002 – 2005, and a total increase ratio of

2.745 on the 2002 – 2032 period. 745.2)2002()2032(=

PtotPtot (This is the growth starting

with 0.043 and slowing down to 0.025 in 2032).

The yearly estimatives of Dar es Salaam population are:

Table 4 Yearly Population Growth Forecast Results Year Population Year Population2002 2,487,289 2019 4,778,820 2003 2,596,593 2020 4,940,369 2004 2,709,158 2021 5,104,491 2005 2,824,990 2022 5,271,096 2006 2,944,086 2023 5,440,090 2007 3,066,441 2024 5,611,373 2008 3,192,043 2025 5,784,842 2009 3,320,874 2026 5,960,389 2010 3,452,912 2027 6,137,904 2011 3,588,127 2028 6,317,271 2012 3,726,484 2029 6,498,373 2013 3,867,942 2030 6,681,088 2014 4,012,455 2031 6,865,293 2015 4,159,971 2032 7,050,861 2016 4,310,432 2033 7,237,665 2017 4,463,774 2034 7,425,575 2018 4,619,928 2035 7,614,459

3.2. DEMAND EXPANSION

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:


( ) ⎟⎟⎠

⎞⎜⎜⎝

⎛∗=

)2005,(),(2005,),(

iPtiPiVtiV otot

Vot(i,t)= Total origins for zone i at year t

P(i,t)= Population of zone i on year t (according to population

forecast).

For the destination analysis and forecast by zone it was assumed for each zone

a logistic curve:

( ))(max

1)(),( itmtke

iVdtiVd −+= (1)

Where:

Vd(i,t)= Total destination by zone in year t

K= Constant

Vdmax(i)= Maximum destination for zone i

tm(i)= Specific logistic parameter adjusted for zone i

( )( ))(,2005,2.1max)( maxmax itViVdiVd =

Where:

Vdmaxt(i)= Is the theoretical maximum attraction value for this zone:

niaccfdbiareaiVd )()()(max ∗∗=

Where:

Area(i) = Area in square meters of zone i

fdb= Constant (maximum attraction per square meters)

n= Parameter = 2

fdb= Constant adopted=150 trips/hectare


acc(i)= Accessibility index of zone i ( )( )itmt

iaccmed

med=)( which is the

average trip time( on public network) from all zones to zone i

Where:

tmed(i)= Trip duration(on public transportation network) from all zones

to zone i

tmed(m)= Minimum off all tmed(i)

From (1) applied to the basic year (2005) and a given year t, we can obtain for

each zone i

⎟⎟⎠

⎞⎜⎜⎝

⎛−

+

=

1)2005,()(

1

)(),(

max

max

iVdiVd

G

iVdtiVd

Where:

G= )2005( tke −

For t=2005 and G=1, )2005,(),( iVdttiVd = , for t>>2005 and G=0

)(),( max iVdtiVd =

G is adjusted for each basic year forecast (2015, 2025, and 2035) to obtain parity

between Total origins and Total destinations.

∑ ∑ ===i

VtdiVoVtdiVd )()(


Table 5 DSM Transportation Network Origin Destination Matrices Forecasted (total trips in the peak hour)

Year Trip Matrix 2005 123,047 2008 138,069 2012 158,097 2016 178,702 2020 201,036 2025 228,952 2035 286,585

3.3. 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.


Table 6 Year Expansion Factor Calculation Day Type Year Amount Weekday % Value in Weekdays Saturdays 52 70 36.4 Sundays 52 50 26.0

National Holidays 9 50 4.5 Private School Holidays 68 80 54.4 Public School Holidays 53 90 47.7

Total 131 100 131.0 Year Expansion Factor 300


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.

4.1. 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).

4.1.1. 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 2035.

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.

4.1.2. 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.


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 400 400 400 500 400 Tariff - Trunk+Feeder 500 400 400 500 500 Tariff - Feeder Only 400 400 300 500 300 Daladala 300 300 300 300 300

*Is the same for financial model.

4.2. 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.

Table 8 Generalized Travel Time Results

Time 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 176,497 210,740 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) 121 126

Time In Vehicle + Time Aux /pax (min) 53 51 Assigned Demand 137,668 137,669

Average Fare/pax (TZS) 220 234 Time Saved per Pax (min) 1.3


Table 9 Peak Hour Results Fare Structure Selected

Attributes

Current Situation Scenario

2008

No DART Situation Scenario

2035

DART 1st Phase

Scenario 0 2009

DART 1st Phase

Scenario 5 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) 20.85 km 20.85 km 20.85 km 20.85 km 20.85 km 20.85 kmNumber of Stations 31 31 31 31 31Terminals 5 5 5 5 5Bus Depots 2 2 2 2 2Trunk Services 7 7 7 7 7Feeder Routes 15 15 15 15 15Daladala Routes 191 ? 148 148 148 148 148Trip Matrix 138,069 286,585 158,097 178,702 201,036 228,952 286,585 Demand Daladalas Total Boardings 149,355 333,805 135,059 159,910 179,899 213,497 276,877Average Occupancy Demand DART Total DART System Peak Hour 36,720 40,576 43,930 49,418 54,491 66,642Total Boardings System 49,933 55,177 60,471 68,027 75,694 93,331Boardings DART Services 35,615 39,354 42,238 47,515 51,940 63,020Boardings Feeder Routes 14,319 15,823 18,233 20,512 23,754 30,311Pax Paying Trunk 35,615 39,354 42,238 47,515 51,940 63,020Pax Paying Feeder fare Only 1,106 1,222 1,692 1,903 2,551 3,622Passengers Riding Trunk Only 21,936 24,239 24,922 28,035 29,501 34,567Passengers Riding Trunk+Feeder 13,678 15,115 17,316 19,480 22,439 28,453 System Efficiency Indexes Pax.Km Trunk 235,619 260,358 285,194 320,835 357,845 442,451Pax.Km Feeder 49,283 54,457 64,280 72,312 86,220 113,470Pax.Km Daladala 1,212,533 2,950,019 983,013 1,086,220 1,320,077 1,485,058 1,811,002 2,412,279Pax.Hour Trunk 9,909 10,950 12,009 13,510 15,085 18,668Pax.Hour Feeder 2,382 2,632 3,087 3,473 4,101 5,337Pax.Hour Daladala 71,181 167,072 55,318 61,126 73,693 82,902 100,397 132,943Veh.Traveled Kms Trunk 3,152 3,462 3,475 3,933 4,302 5,301Veh.Traveled Kms Feeder 1,866 2,059 2,387 2,667 3,179 4,197Veh.Traveled Kms Daladala 100,566 100,566 85,807 94,550 111,410 125,042 149,555 197,203Veh.Traveled Time Trunk 130 144 148 166 181 222Veh.Traveled Time Feeder 98 106 123 139 165 217Veh.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 5,301Trunk Operational Fleet 138 150 152 174 186 228Trunk Fleet + Reserve 145 158 160 183 196 240Traveled Kms Feeder 1,817 2,059 2,387 2,667 3,179 4,197Feeder Operational Fleet 112 124 137 154 179 231Feeder Reserve Fleet 118 131 144 162 188 243Traveled Kms Daladala 101,826 235,933 85,807 94,550 111,410 125,042 149,555 197,203Daladala Operational Fleet 6,041 13,594 4,854 5,350 6,250 7,017 8,339 10,938


Table 10 Demand Results per Station Scenario 2009. Peak Hour

Station Boardings Thru Alighting Corridor Code Name Initial Transfer Total Passengers Final Transfer Total Kawawa 7102 Kinondoni Mjini 1089 1089 7944 1238 1238 Kawawa 7200 Mwinyijuma 117 537 653 10217 99 544 643 Kawawa 7202 Kanisani 295 295 10977 488 488 Kawawa 7300 Kinondoni A 1886 2310 4197 10327 1258 2310 3568 Kawawa 7302 Usalama 53 53 14348 64 64 Kawawa 8111 Morocco Terminal 516 6623 7139 - 495 6623 7118

Morogoro 10000 Kimara Terminal 1286 2160 3446 - 1547 2160 3706 Morogoro 10001 Kimara Resort 331 331 4993 398 398 Morogoro 10002 Kimara Thomas 221 221 5721 265 265 Morogoro 10003 Baruti 178 178 6207 113 113 Morogoro 10004 Corner 183 183 6498 117 117 Morogoro 10005 Kibo 139 139 6798 99 99 Morogoro 10006 Chai Bora 147 147 7037 93 93 Morogoro 10100 Ubungo Tanesco 168 155 323 7277 37 143 180 Morogoro 10102 Ubungo Terminal 762 5335 6097 7545 920 5335 6255 Morogoro 10200 Shekilango 863 1552 2415 13775 776 1552 2328 Morogoro 10300 Urafiki Mahakama 537 2516 3053 15628 510 2516 3026 Morogoro 10301 Tip Top 497 497 18045 341 341 Morogoro 10303 Bakheresa 1350 220 1570 17347 1400 220 1620 Morogoro 10304 Manseze Argentina 1202 1202 18977 345 345 Morogoro 10305 Magomeni Kagera 1704 1704 19605 505 505 Morogoro 10306 Mwembe Chai 177 177 21269 2 2 Morogoro 10307 Baptist Church 47 1336 1383 20113 1336 1336 Morogoro 10308 Magomeni Mapipa 916 826 1742 23513 199 826 1025 Morogoro 10309 Jangwani 7 7 24052 63 63 Morogoro 10311 Fire Station 1050 2485 3534 18748 2058 2485 4542 Morogoro 10315 Lybia Street 167 167 6201 496 496 Morogoro 10318 City Council 220 220 4968 1014 1014 Kiv Front 10322 Old Posta 258 258 3456 1254 1254 Kiv Front 10324 National Bank 69 69 2921 466 466 Kiv Front 10328 Bibititi 163 95 258 6864 714 157 871 Kiv Front 10400 Kivukoni Terminal 797 627 1424 - 1357 140 1498 Msimbazi 19002 Kariakoo Market 4162 4162 1375 6008 6008 Msimbazi 19100 Kariakoo Terminal 181 369 550 - 410 415 825 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.


5. ANNEXES

Annex 1. Peak Hour Results Fare Structure Alternative 1

AttributesCurrent Situation

Scenario 600 2008

No DART Situation

Scenario 605 2035

DART 1st Phase Scenario 610

2008


2012


2016


2020


2025


2035

Basic InformationExtension (km) 20.85 km 20.85 km 20.85 km 20.85 km 20.85 km 20.85 kmNumber of Stations 29 29 29 29 29 29Terminals 5 5 5 5 5 5Bus Depots 2 2 2 2 2 2Trunk Services 7 7 7 7 7 7Feeder Routes 15 15 15 15 15 15Daladala Routes 191 ? 148 148 148 148 148 148Trip Matrix 138.069 286.585 138.069 158.097 178.702 201.036 228.952 286.585

Demand DaladalasTotal Boardings 149.355 333.805 111.165 127.279 150.767 169.607 201.279 260.955Average Occupancy

Demand DARTTotal DART System Peak Hour 39.600 45.344 49.336 55.502 61.480 75.506Total Boardings System 62.170 71.186 78.740 88.578 99.456 123.562Boardings DART Services 38.401 43.971 47.407 53.332 58.531 71.265Boardings Feeder Routes 23.769 27.215 31.333 35.246 40.925 52.297Pax Paying Trunk 38.401 43.971 47.407 53.332 58.531 71.265Pax Paying Feeder fare Only 1.199 1.373 1.929 2.170 2.949 4.241Passengers Riding Trunk Only 16.976 19.438 19.753 22.222 23.077 26.678Passengers Riding Trunk+Feeder 21.425 24.533 27.654 31.110 35.454 44.587

System Efficiency IndexesPax.Km Trunk 245.925 281.601 309.914 348.645 390.508 484.634Pax.Km Feeder 93.383 106.929 126.332 142.120 170.433 225.277Pax.Km Daladala 1.212.533 2.950.019 888.579 1.017.479 1.238.693 1.393.499 1.700.677 2.266.028Pax.Hour Trunk 10.408 11.919 13.129 14.770 16.558 20.562Pax.Hour Feeder 5.138 5.883 6.910 7.774 9.269 12.177Pax.Hour Daladala 71.181 167.072 50.006 57.259 69.118 77.756 94.194 124.721Veh.Traveled Kms Trunk 3.191 3.612 3.730 4.140 4.579 5.579Veh.Traveled Kms Feeder 3.500 3.945 4.570 5.115 6.098 7.939Veh.Traveled Kms Daladala 100.566 100.566 77.560 88.367 103.898 116.702 139.092 183.074Veh.Traveled Time Trunk 134 152 156 176 192 236Veh.Traveled Time Feeder 192 223 254 285 333 433Veh.Traveled Time Daladala 5.925 5.925 4.227 4.858 5.682 6.392 7.614 9.996

Bus Operations totalTraveled Kms Trunk 3.191 3.612 3.730 4.140 4.579 5.579Trunk Operational Fleet 142 160 162 182 198 242Trunk Fleet + Reserve 150 168 171 192 208 255Traveled Kms Feeder 3.500 3.945 4.570 5.115 6.098 7.939Feeder Operational Fleet 209 236 267 302 351 448Feeder Reserve Fleet 220 248 281 318 369 471Traveled Kms Daladala 101.826 235.933 77.560 88.367 103.898 116.702 139.092 183.074Daladala Operational Fleet 6.041 13.594 4.391 5.004 5.843 6.558 7.766 10.158



Attributes

Current Situation

Scenario 600 2008

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) 20.85 km 20.85 km 20.85 km 20.85 km 20.85 km 20.85 kmNumber of Stations 29 29 29 29 29 29Terminals 5 5 5 5 5 5Bus Depots 2 2 2 2 2 2Trunk Services 7 7 7 7 7 7Feeder Routes 15 15 15 15 15 15Daladala Routes 191 148 148 148 148 148 148Trip Matrix 138,069 138,069 158,097 178,702 201,036 228,952 286,585 Demand Daladalas Total Boardings 149,355 109,401 125,277 148,415 166,955 198,221 257,082 Demand DART Total DART System Peak Hour 41,021 46,974 51,187 57,582 63,834 78,440Total Boardings System 65,330 74,812 83,042 93,416 105,106 130,754Boardings DART Services 39,024 44,687 48,237 54,264 59,581 72,548Boardings Feeder Routes 26,306 30,125 34,805 39,152 45,525 58,206Pax Paying Trunk 39,024 44,687 48,237 54,264 59,581 72,548Pax Paying Feeder fare 1,997 2,287 2,950 3,318 4,253 5,892Passengers Riding Trunk Only 16,455 18,843 19,019 21,395 22,085 25,410Passengers Riding Trunk+Feeder 22,569 25,844 29,218 32,869 37,496 47,138 System Efficiency Indexes Pax.Km Trunk 248,274 284,290 313,047 352,169 394,409 489,231Pax.Km Feeder 103,149 118,114 139,890 157,372 188,681 249,046Pax.Km Daladala 1,212,533 878,293 1,005,700 1,224,604 1,377,653 1,682,058 2,242,223Pax.Hour Trunk 10,519 12,045 13,279 14,938 16,747 20,790Pax.Hour Feeder 5,687 6,512 7,675 8,634 10,300 13,519Pax.Hour Daladala 71,181 49,447 56,621 68,359 76,902 93,203 123,468Veh.Traveled Kms Trunk 3,181 3,619 3,737 4,181 4,559 5,564Veh.Traveled Kms Feeder 3,658 4,162 4,827 5,437 6,434 8,391Veh.Traveled Kms Daladala 100,566 76,917 87,640 103,001 115,636 137,827 181,537Veh.Traveled Time Trunk 135 154 158 175 192 236Veh.Traveled Time Feeder 205 234 271 303 355 464Veh.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 5,564Trunk Operational Fleet 142 162 162 182 198 242Trunk Fleet + Reserve 150 171 171 192 208 255Traveled Kms Feeder 3,658 4,162 4,827 5,437 6,434 8,391Feeder Operational Fleet 221 248 286 317 372 476Feeder Reserve Fleet 233 261 301 333 391 500Traveled Kms Daladala 101,826 76,917 87,640 103,001 115,636 137,827 181,537Daladala Operational Fleet 6,041 4,354 4,962 5,791 6,496 7,694 10,078




Scenario 600 2008

No DART Situation

Scenario 605 2035


2008


2012


2016


2020


2025


2035



Demand DARTTotal DART System Peak Hour 28.612 32.763 35.141 39.532 43.227 52.515Total Boardings System 41.977 48.067 52.305 58.841 65.204 80.256Boardings DART Services 28.140 32.222 34.446 38.751 42.240 51.165Boardings Feeder Routes 13.837 15.845 17.859 20.090 22.964 29.091Pax Paying Trunk 28.140 32.222 34.446 38.751 42.240 51.165Pax Paying Feeder fare Only 472 541 695 781 987 1.350Passengers Riding Trunk Only 15.123 17.316 17.781 20.003 20.942 24.344Passengers Riding Trunk+Feeder 13.017 14.906 16.665 18.748 21.298 26.821

System Efficiency IndexesPax.Km Trunk 189.083 216.513 237.149 266.790 297.762 368.568Pax.Km Feeder 47.993 54.955 63.471 71.404 84.282 110.429Pax.Km Daladala 1.212.533 2.950.019 987.987 1.131.306 1.370.265 1.541.522 1.873.806 2.488.979Pax.Hour Trunk 7.968 9.124 10.005 11.255 12.576 15.580Pax.Hour Feeder 2.458 2.814 3.207 3.608 4.200 5.428Pax.Hour Daladala 71.181 167.072 55.673 63.750 76.611 86.184 104.049 137.406Veh.Traveled Kms Trunk 2.539 2.820 2.954 3.287 3.632 4.393Veh.Traveled Kms Feeder 1.983 2.259 2.528 2.817 3.244 4.210Veh.Traveled Kms Daladala 100.566 100.566 85.890 97.564 114.644 128.541 153.722 202.371Veh.Traveled Time Trunk 104 121 124 139 151 186Veh.Traveled Time Feeder 104 118 133 148 170 217Veh.Traveled Time Daladala 5.925 5.925 4.678 5.379 6.275 7.058 8.424 11.078

Bus Operations totalTraveled Kms Trunk 2.539 2.820 2.954 3.287 3.632 4.393Trunk Operational Fleet 112 126 132 144 160 194Trunk Fleet + Reserve 118 133 139 152 168 204Traveled Kms Feeder 1.983 2.259 2.528 2.817 3.244 4.210Feeder Operational Fleet 122 133 146 163 184 228Feeder Reserve Fleet 129 140 154 172 194 240Traveled Kms Daladala 101.826 235.933 85.890 97.564 114.644 128.541 153.722 202.371Daladala Operational Fleet 6.041 13.594 4.840 5.524 6.433 7.221 8.575 11.226




Scenario 600 2008

No DART Situation

Scenario 605 2035


2008


2012


2016


2020


2025


2035



Demand DARTTotal DART System Peak Hour 36.593 41.903 45.471 51.153 56.500 69.201Total Boardings System 49.811 57.039 62.856 70.712 79.076 97.929Boardings DART Services 34.065 39.008 41.843 47.072 51.434 62.394Boardings Feeder Routes 15.746 18.031 21.013 23.640 27.642 35.535Pax Paying Trunk 34.065 39.008 41.843 47.072 51.434 62.394Pax Paying Feeder fare Only 2.528 2.895 3.628 4.081 5.066 6.807Passengers Riding Trunk Only 21.127 24.193 24.870 27.977 29.435 34.487Passengers Riding Trunk+Feeder 12.938 14.815 16.973 19.095 21.999 27.907

System Efficiency Indexes peak hourPax.Km Trunk 226.433 259.279 283.995 319.489 356.340 440.599Pax.Km Feeder 58.130 66.562 80.508 90.570 110.148 146.907Pax.Km Daladala 1.212.533 2.950.019 939.144 1.075.380 1.305.471 1.468.623 1.789.375 2.381.958Pax.Hour Trunk 9.523 10.904 11.959 13.453 15.022 18.591Pax.Hour Feeder 4.296 4.920 5.918 6.657 7.990 10.482Pax.Hour Daladala 71.181 167.072 52.807 60.468 72.798 81.895 99.060 131.060Veh.Traveled Kms Trunk 3.020 3.447 3.475 3.933 4.272 5.253Veh.Traveled Kms Feeder 2.105 2.394 2.868 3.220 3.860 5.097Veh.Traveled Kms Daladala 100.566 100.566 77.387 93.340 109.729 123.273 146.984 193.290Veh.Traveled Time Trunk 126 143 148 166 181 222Veh.Traveled Time Feeder 112 128 153 172 208 270Veh.Traveled Time Daladala 5.925 5.925 4.211 5.129 6.005 6.761 8.036 10.558

75Bus Operations totalTraveled Kms Trunk 3.020 3.447 3.475 3.933 4.272 5.253Trunk Operational Fleet 132 150 152 174 186 226Trunk Fleet + Reserve 139 158 160 183 196 238Traveled Kms Feeder 2.105 2.394 2.868 3.220 3.860 5.097Feeder Operational Fleet 127 144 168 188 219 287Feeder Reserve Fleet 134 152 177 198 230 302Traveled Kms Daladala 98.534 235.933 77.387 93.340 109.729 123.273 146.984 193.290Daladala Operational Fleet 5.803 13.594 4.354 5.282 6.161 6.912 8.186 10.714

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