TSHWANE TRANSPORT
DEMAND MODELPretoria
Centurion
Midrand
RooiwalMabopane
Winterveld
Garankuwa
Hartbeespoort
Kameeldrift
Presented by: CM Olivier (CTMM)
South African Emme2 Users Conference
10-11 September 2004
CONTENTS
• MODEL
• LAND USE
• TRAFFIC ZONING AND TRANSPORT NETWORKS
• TRIP GENERATION AND DISRIBUTION
• MODAL SPLIT
• LESSONS LEARNED
• CONCLUSIONS
MODEL
Traffic Counts
TT Surveys
RSI’s
CPTR
RP & SP
ODHIS
DATA MODEL UTILIZATION
MODEL
Traffic Counts
TT Surveys
RSI’s
CPTR
RP & SP
ODHIS
DATA MODEL UTILIZATION
ZONES
PRIVATENETWORK
PUBLICTRANSPORTNETWORK
TRIPGENERATIONATTRACTION
TRIPDISTRIBUTION
MODAL SPLIT
EXTERNALTRIPS
TRIPASSIGNMENT
CALIBRATION
Done first for free flow conditionsThen for congested conditions
ZONES
PRIVATENETWORK
PUBLICTRANSPORTNETWORK
TRIPGENERATIONATTRACTION
TRIPDISTRIBUTION
MODAL SPLIT
EXTERNALTRIPS
TRIPASSIGNMENT
CALIBRATION
MODEL
Traffic Counts
TT Surveys
RSI’s
CPTR
RP & SP
ODHIS
DATA MODEL UTILIZATION
ZONES
PRIVATENETWORK
PUBLICTRANSPORTNETWORK
TRIPGENERATIONATTRACTION
TRIPDISTRIBUTION
MODAL SPLIT
EXTERNALTRIPS
TRIPASSIGNMENT
CALIBRATION
Land Use
Network
Public Trans.
Not app..
POPULATION & EMPLOYMENT• Population derived from:
– Flats, duplex, simplex & sectional titles
– Formal & informal houses– Hostels & single people
• Population divided into:– Economic Active =
910 800– Economic non-active=1 140
500• Age < 15 years• Scholar/full time student• Housewife• Pensioner• Other
– Total =2 051 300
– Nett. Inflow of 69 500 workers
• Employment divided into:– Formal = 630 200
• Retail• Office• Industrial• Ware house• Local serving• Other inside workers• Agriculture/mining• Construction• Transport
– Informal = 103 300
• Domestics• Informal at home, at work
– Unemployed = 246 800• Unemployed at home, ?work
– Total = 980 300
TRIP CHAINS RECORDED
NoTrip chain
Trip DescriptionFreq
% Of Total Cum%
1 13 Home-Education 8 588 41.241 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
212Total
20 824 100.000
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
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
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
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
PUBLIC TRANSPORT NETWORK (2)
OPERATOR ROUTES
BEFORE
ROUTES
AFTER
SEGMENTS
BEFORE
SEGMENTS AFTER
Taxi 696 462 18 392 13 176Atteridgeville 83 68 3 079 2 579Bothlaba 154 114 11 337 8 197Gare 90 77 5 853 4 912Mamelodi 87 77 6 336 6 087Pretoria 327 283 16 673 14 056PUTCO Distribution
180 165 10 958 9 393
PUTCO Ekangala 8 6 536 402PUTCO Homelands 547 82 27 977 4 455PUTCO Soshanguve
67 59 2 749 2 398
Thari 46 26 2 535 2 085TOTAL 2 285 1419 106 725 67 740
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
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
MODAL SPLIT (2)
Home-based work person trips
Non Vehicle Vehicle
Car Public Transport
Rail Bus Taxi
Primary
Secondary
Tertiary
Combine Without Car & With Car per trip purpose
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
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
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
THANK YOU