1. TSHWANE TRANSPORT DEMAND MODEL
South African Emme2 Users Conference
10-11 September 2004
Pretoria
Centurion
Midrand
Rooiwal
Mabopane
Winterveld
Garankuwa
Hartbeespoort
Kameeldrift
TSHWANE TRANSPORT DEMAND MODEL
Comment with regard to the title: It is possible to conclude that the title implies that this model is the first model in a post apartheid South Africa, which is not the case. It simply means that this is the first post apartheid model for the City of Tshwane.
Presented by: CM Olivier (CTMM)

2. CONTENTS MODEL LAND USE TRAFFIC ZONING AND TRANSPORT NETWORKS
TRIP GENERATION AND DISRIBUTION
MODAL SPLIT
LESSONS LEARNED
CONCLUSIONS

3. MODEL DATA MODEL UTILIZATION Traffic Counts TT Surveys RSI’s CPTR
RP & SP
ODHIS
Starting with the data. I don’t want to elaborate on the data and will therefore only mention the data sources, which are:
The 1998 Current Public Transport Record
The 1999 Origin-Destination Home Interview Survey
The 2000 RP & SP Survey in the Mabopane/Soshanguve area including RP & SP data of a research project for Mamelodi
2000 Road Side Interviews at 8 locations
2000 Classified Vehicle Traffic Counts
2000 Travel Time Surveys

4. MODEL DATA MODEL UTILIZATION ZONES PRIVATE NETWORK PUBLIC TRANSPORT
TRIP
GENERATION
ATTRACTION
DISTRIBUTION
MODAL SPLIT
EXTERNAL
TRIPS
ASSIGNMENT
CALIBRATION
ZONES
TRIP
GENERATION
ATTRACTION
CALIBRATION
Traffic Counts
TT Surveys
RSI’s
CPTR
RP & SP
ODHIS
Done first for free flow conditions
Then for congested conditions
PRIVATE
NETWORK
TRIP
DISTRIBUTION
PUBLIC
TRANSPORT
NETWORK
MODAL SPLIT
EXTERNAL
TRIPS
TRIP
ASSIGNMENT

5. MODEL DATA MODEL UTILIZATION ZONES TRIP GENERATION ATTRACTION
CALIBRATION
Traffic Counts
TT Surveys
RSI’s
CPTR
RP & SP
ODHIS
PRIVATE
NETWORK
TRIP
DISTRIBUTION
Land Use
Network
PUBLIC
TRANSPORT
NETWORK
MODAL SPLIT
Public Trans.
EXTERNAL
TRIPS
Not app..
TRIP
ASSIGNMENT

6. POPULATION & EMPLOYMENT
Population derived from:
Flats, duplex, simplex & sectional titles
Formal & informal houses
Hostels & single people
Population divided into:
Economic Active =
Economic non-active=
Age < 15 years
Scholar/full time student
Housewife
Pensioner
Other
Total =
Nett. Inflow of workers
Employment divided into:
Formal =
Retail
Office
Industrial
Ware house
Local serving
Other inside workers
Agriculture/mining
Construction
Transport
Informal =
Domestics
Informal at home, at work
Unemployed =
Unemployed at home, ?work
Total =

7. TRIP CHAINS RECORDED No Trip chain Trip Description Freq % Of Total
Cum% 1 13
Home-Education
8 588
41.241 2 12
Home-Work
7 737
37.154
78.395 3 14
Home-Shop
1 091
5.239
83.634 4 16
Home-Day mother
692
3.323
86.957 5 15
Home-Other
476
2.286
89.243 6
132
Home-Education-Work
329
1.580
90.823 7 18
Home-Friends house
240
1.153
91.976 8
131
Home-Education-Home
238
1.143
93.119 9
141
Home-Shop-Home
126
0.605
93.724 10
162
Home-Day mother-Work
122
0.586
94.309
212
Total
20 824

8. ZONES Total zones = 756 704 internal & 52 external
Zones were developed according to:
Homogeneity
Maximum number of Private vehicle Public transport person trips for target year 2020
Zones must fit within GTS2000 zones
Zones were aggregated into:
60 int+10 ext sub regions
& 19 functional areas for modeling & reporting purposes
The zones have been discussed in quite detail earlier in the presentation. Just the following comments based on the picture. The larger zones are on the periphery of the study area and the smallest zones are in the Pretoria CBD where each street block represents a zone. The density of zones thus represents the density of development.

9. PRIVATE NETWORK Expand network to cover area
Transfer bus only links to private network
Correct the network based on collective knowledge
Had to verify according to aerial photographs
Had to travel parts of the network
Correct network geographically
The previous model’s network was considered to be acceptable and that it could be used for this model, except for some minor changes. These minor changes include:
Expansion of the network to cover the whole study area.
Bus only links in the previous model where transfer to the private network as these links were considered important private links as well.
Corrections to the network based on collective knowledge of the study team and client.
A small budget where therefore allocated for network corrections. The surprise was that these corrections were not adequate and the following additional work had to be done:
The network had to be verified according to aerial photographs.
Some parts of the network had to traveled in order to gain the necessary information in order to correct the network.
The network had to be corrected geographically.
The additional work was necessitated by the public transport network. The incompleteness and incorrectness of the private network was clear from the public transport network’s point of view. This will be explained in more detail later.

10. PUBLIC TRANPORT NETWORK (1)
Major problems were experienced with CPTR data
The route data does not cover the whole study area
Bus route data
Some routes were incomplete
Directions changes along routes
650 routes had to be corrected by hand
Only 13% of the routes had time tables
Only 13% of the routes had passenger volumes
Taxi route data
More than two thirds of the routes were only bits & pieces – taxi data were therefore discarded
Rail data
Was not part of the CPTR data
One of the objectives of this study was to use the CPTR data to develop the public transport route network. The general consensus with regard to the CPTR data was that the data is both comprehensive and correct. It was also highlighted that one of the requirements was that the data must be usable for modeling purposes.
Major problems with the data were experienced right from the start.
The first problem was that the route data does not cover the whole study area. Routes were cut off at the GPMC boundaries.
Problems with regard to the bus route data are:
Some routes were incomplete resulting in gaps in the routes.
Direction of travel along certain routes were inconsistent (650 routes had to be corrected by hand).
Data associated with routes where incomplete. These are:
Time tables – only 13% of bus routes in the data base have time table information.
Passenger volumes – only 13% of bus routes in the data base have passenger volume information.
Problems with regard to the taxi route data were shocking. More than two thirds of the routes were only bits and pieces. The taxi data were therefore discarded.
No rail data was available, because it was not part of the CPTR data collection process.

11. PUBLIC TRANPORT NETWORK (3)
Rail
Railway lines from GIS
No operational data -> use previous model’s data
Bus
Route data based on CPTR
Aggregated
Operational data from CPTR and guessed
Taxi
Synthetic hub & spoke system
Operational data guessed
Not used – additional assignment
Walk
On all streets in residential and employment areas
At major transfer areas
The railway lines (shown in red) was originally generated from the 1:50000 electronic maps, but never used, because of the effort involved to transfer the operational data in the old model to the new model. The old model’s railway lines and operational data was therefore incorporated into the new model.
The bus routes (shown in blue) were generated from the CPTR data. The routes were first aggregated as explained previously and then transferred to the EMME/2 network. Both these tasks were done in ARCVIEW. The transfer of the route data to the EMME/2 network highlighted mistakes in the EMME/2 network and also clearly shows that the geographical orientation of the route data in the CPTR did correspond well with the geographical orientation of the EMME/2 network. Coordinates of the EMME/2 network then had to be corrected. But not only coordinate data but also link data as well.
It was mentioned that the taxi route data was discarded. An artificial hub and spoke taxi network was created (shown in green). Major taxi ranks were identified. A taxi rank was considered as major when more than a 1000 passengers per day uses the facility. Once plotted it became clear that not all of the study area will be serviced by taxis. Some additional ranks were then added. These ranks were then connected with each other bye means of routes on the shortest path between these ranks. A feeder network was developed for each major rank. This network ensures that a person can travel by taxi from any point on the network to any other point on the network. This network does not resembles reality and was therefore discarded.
Walk links were defined to make it possible to walk from one facility to another nearby facility and to be able to walk from the origin to a route and from a route to a destination. These walk links were defined in all residential and employment areas and at major transfer areas.

12. PUBLIC TRANSPORT NETWORK (2)
OPERATOR
ROUTES BEFORE
ROUTES AFTER
SEGMENTS BEFORE
SEGMENTS AFTER
Taxi
696
462
18 392
13 176
Atteridgeville 83 68
3 079
2 579
Bothlaba
154
114
11 337
8 197
Gare 90 77
5 853
4 912
Mamelodi 87
6 336
6 087
Pretoria
327
283
16 673
14 056
PUTCO Distribution
180
165
10 958
9 393
PUTCO Ekangala 8 6
536
402
PUTCO Homelands
547 82
27 977
4 455
PUTCO Soshanguve 67 59
2 749
2 398
Thari 46 26
2 535
2 085
TOTAL
2 285
1419
67 740
The most significant reduction in the number of routes and segments through the aggregation process was achieved for PUTCO Homelands.

13. TRIP GENERATION & ATTRACTION (1)
Start with activity based approach
Too many market segments
End with 5 trip purposes (2 two leg trip chains)
Accept statistic reliable trip generation rates:
Rates based on sub area, functional area or PDI/non-PDI areas
Separate rates for car users and non-car users
Trip generation & attraction is done in EXCEL
Reasons
Socio-economic data, rates and number of trips on one spreadsheet
Easy to balance production & attractions
Easy to determine the effect of assuming rates for external trips & secondary study area
Automate the calculation process for future scenarios

14. MODAL SPLIT (1) Multi Nomial Logit model Hierarchical split
Done per group and per trip purpose
Utilities are based on the following variables:
Trip distance
Personal income
Household income
Population density
Employment density
Population & employment mix
Walk time
Transfer time
Total travel time
Fare
Historical choice

15. MODAL SPLIT (2) Combine Without Car & With Car per trip purpose
Home-based work person trips
Non Vehicle
Vehicle
Car
Public Transport
Rail
Bus
Taxi
Primary
Secondary
Tertiary

16. LESSONS LEARNT - Consultant
Expectations must be in line with the budget & available data
Don’t try to save money by scaling down on:
surveys
tasks
Don’t interrupt the process
Data collection not for modeling purposes, but to be used for modeling purposes does not work
The purpose(s) of the model must be clear
The accuracy of the model must be in line with the purpose(s) & available data
Authority must have a modeler
Simple easy to use models stand better chance to be used than complicated and clumsy models
Problems experienced with the data are:
level of detail is not sufficient
the data is not comprehensive enough
the data could be too old
the data base is developed such that it hamper the transfer of data

17. LESSONS LEARNT - Client
Ensure fully committed budget before appointments
Evaluate available data in advance
Comprehensiveness
Mistakes & Format
Pilot study may be needed
Don’t be too ambiguous – start with simplified model
Data in general are expensive
Make sure that data are collected for all important processes dependant on the data
Modelers should drive the data collection process
Design model before planning data collection
A well designed public transport model needs:
Proper survey procedures (checks & balances)
Comprehensive data, including agreements & contracts
All public transport modes included
Sufficient resources
Problems experienced with the data are:
level of detail is not sufficient
the data is not comprehensive enough
the data could be too old
the data base is developed such that it hamper the transfer of data

18. CONCLUSIONS In conclusion it can be stated/confirmed that:
Several draw backs were experienced throughout the project
This resulted in unexpected delays & over expenditure of the project
The negative effect of insufficient PT data were overcome to such an extend that
A reasonable model could be developed and calibrated

19. THANK YOU