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