Download SAEmme2_Konferensie

South African Emme2 Users
Conference
10-11 September 2004
Winterveld
Mabopane
Rooiwal
Garankuwa
Pretoria
TSHWANE TRANSPORT
DEMAND MODEL
Kameeldrift
Hartbeespoort
Centurion
Midrand
Presented by: CM Olivier (CTMM)
CONTENTS
• MODEL
• LAND USE
• TRAFFIC ZONING AND TRANSPORT NETWORKS
• TRIP GENERATION AND DISRIBUTION
• MODAL SPLIT
• LESSONS LEARNED
• CONCLUSIONS
MODEL
DATA
CPTR
ODHIS
RP & SP
RSI’s
Traffic Counts
TT Surveys
MODEL
UTILIZATION
MODEL
DATA
ZONES
TRIP
GENERATION
ATTRACTION
PRIVATE
NETWORK
TRIP
DISTRIBUTION
PUBLIC
TRANSPORT
NETWORK
MODAL SPLIT
CPTR
ODHIS
RP & SP
RSI’s
EXTERNAL
TRIPS
Traffic Counts
TT Surveys
UTILIZATION
MODEL
TRIP
ASSIGNMENT
CALIBRATION
Done first for free
flow conditions
Then for congested
conditions
MODEL
DATA
ZONES
TRIP
GENERATION
ATTRACTION
PRIVATE
NETWORK
TRIP
DISTRIBUTION
CPTR
ODHIS
RP & SP
UTILIZATION
MODEL
PUBLIC
TRANSPORT
NETWORK
CALIBRATION
Land Use
Network
MODAL SPLIT
Public Trans.
RSI’s
EXTERNAL
TRIPS
Traffic Counts
TT Surveys
TRIP
ASSIGNMENT
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
– Informal
=
103 300
•
•
•
•
•
•
•
•
•
Retail
Office
Industrial
Ware house
Local serving
Other inside workers
Agriculture/mining
Construction
Transport
• Domestics
• Informal at home, at work
– Unemployed = 246 800
• Unemployed at home, ?work
– Total
= 980 300
TRIP CHAINS RECORDED
Trip Description
No
Trip
chain
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
212
Freq
Total
20 824
% Of
Total
Cum%
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
Taxi
Atteridgeville
Bothlaba
Gare
Mamelodi
Pretoria
PUTCO Distribution
PUTCO Ekangala
PUTCO Homelands
PUTCO Soshanguve
Thari
TOTAL
ROUTES ROUTES
BEFORE
AFTER
696
83
154
90
87
327
180
8
547
67
46
2 285
462
68
114
77
77
283
165
6
82
59
26
1419
SEGMENTS SEGMENTS
BEFORE
AFTER
18
3
11
5
6
16
10
392
079
337
853
336
673
958
536
27 977
2 749
2 535
106 725
13
2
8
4
6
14
9
176
579
197
912
087
056
393
402
4 455
2 398
2 085
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)
Combine Without Car & With Car per trip purpose
Home-based work person trips
Non Vehicle
Vehicle
Primary
Car
Public Transport
Secondary
Rail
Tertiary
Bus
Taxi
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