MODELING TRANSIT MODE CHOICE FOR
INTER-REGIONAL COMMUTING TRIPS
Mohamed S. Mahmoud, M.Sc.
Ph.D. Candidate
ACT Canada Sustainable Mobility Summit
November 2012
More Transit = More Sustainability
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More Transit = More Sustainability
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How Would we Know?
A policy-sensitive comprehensive model is needed
WHY?
• Understand Individuals’ Behaviour
• Test Travel Demand Management
(TDM) Policies and Strategies
• Estimate Impacts on Transportation Systems
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Current Sate of Practice
Demand Side
• Travel Demand Models
– Discrete Choice Models (Disaggregate)
– Behavioural Factors
– Limitations:
• Data Quality and Availability
• Complex Model Structures
• Estimation Capabilities
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Current Sate of Practice
Supply Side
• Trip Assignment Models (Macro Vs. Micro)
– Uni-modal Trip Assignment
• Traffic Assignment
• Transit Assignment
Agent-Based
(Disaggregate) Models
– Multi-Modal Trip Assignment (Not Only in Theory!)
• GTHA Model is under development at UofT using MATSim
• 24-hr Agent-based Activity Scheduler
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Motivation
Why a better framework is needed?
• Enhanced Model Components (policy-sensitive)
• Demand and Supply Integration (Feedback)
• Analysis Resolution (Disaggregate/Agent-based)
• Detailed Output
• Universal and Easy to Update
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Framework Components
• Departure-time Choice Model
• Mode Choice Model
Image Credit: TRPC
• Access Mode and Access Location Choice for
Mixed Modes (P&R and K&R) Models
• Route Choice
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Image Credit: TACTRAN
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Cross-Regional Commuting Trips
Case Study
• GTHA
– Nine Local Transit Agencies
– Regional Transit (GO)
• Cross-Regional Trips
– Across Local Transit Jurisdictions
– Involve Inter-Modal Interaction
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Inter-Modal Trips
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Enhanced Mode Choice Model
• Joint Trivariate Choice Decision Structure
• Each Level Affects the other Two Choices
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Decision Structure
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Conceptual Framework
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Phase I – Understanding Users’ Behaviour
• Data
– Transportation Tomorrow Survey (TTS – 2006)
•
•
•
•
•
Largest Travel Survey in NA
5% Sample of the GTHA
Revealed Preference (RP) Survey
4500 Morning Peak Inter-Regional Trip Records
Detailed Transit Information
– Morning Peak Hour Level of Service Attributes
using GTHA EMME/2 Model
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Phase I – Understanding Users’ Behaviour
• Demand Model
– Three Model Structures
Joint
Main-Access Modes
D
P
TD
TP
TW
Sequential
Main Mode
Nested
Main Mode
D
P
T
Access Mode
D
P
T
Access Mode
Problematic!
D: Auto drive all way
P: Auto passenger all way
TD: Transit with auto driver access (P&R)
TP: Transit with auto passenger access (K&R)
TW: Transit with walk access
D
P
W
TD
TP TW
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Preliminary Results
Sequential Model
• Main Mode Choice
• Access Mode Choice
Coefficients
Estimate
t-value From Data
Coefficients
Suffer
Issues
Estimate
t-value
Drive:(intercept)
5.17E-01
3.5499
TD:(intercept)
-3.186827
-4.8706
***
Transit:(intercept)
-2.10E-01
-0.7716
TW:(intercept)
2.334677
3.8803
***
cost
5.25E-02
2.0709
*
cost
0.590367
3.5149
***
acost
1.41E+02
636.2149
***
acost
5.88269
9.2363
***
pcost
1.46E-04
0.0105
pcost
-0.227855
-3.1612
**
wtime
-1.24E-02
-4.6035
***
wtime
-0.084537
-3.4083
***
atime
-3.20E-02
-3.8404
***
atime
0.026966
1.4606
Drive:age25_orless
-1.95E+00
-17.2084
***
TD:age25_orless
-1.397522
-3.7521
***
Drive:gender_m
1.21E+00
11.2037
***
TD:gender_m
0.730712
2.2554
*
Transit:gender_m
4.08E-01
2.6076
**
TW:gender_m
0.304925
1.0057
Drive:trans_pass
-7.19E-01
-3.3239
***
TD:trans_pass
1.240256
3.4447
Transit:trans_pass
2.56E+00
11.99
***
TW:trans_pass
-0.235584
-0.744
Drive:n_vehicle
6.15E-01
10.2113
***
TD:n_vehicle
0.718567
3.5254
Transit:n_vehicle
-5.18E-01
-6.5336
***
TW:n_vehicle
-0.101998
-0.5489
Passenger:time
-1.04E-01
-9.5539
***
TP:time
-0.100357
-5.4426
***
Drive:time
-1.01E-01
-9.631
***
TD:time
-0.091635
-4.9909
***
Transit:time
-2.79E-02
-5.2324
***
TW:time
-0.104988
-5.605
***
sd.cost
8.88E-02
2.0126
*
sd.cost
0.289735
0.4657
***
***
***
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What is Next?
Phase I – (Cont.)
• Access Location Choice (Under Development)
– Generate access location choice set for individuals
– Generate level of service attributes of access modes for non-transit trips
• Trivariate Model Development
Phase II
• Conduct an experimental design ; Stated Preference (SP) Survey
• Activity-Based Model (update previously developed models) using:
– 24-hr activity data
– Multi-modal level of service attribute data
• Equilibrium: Demand – Supply Integration
• Account for Trip Dynamics and Household Resource Allocation
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Summary
• A policy-sensitive Comprehensive Modeling Framework
• Demand Model: Advanced Discrete Choice (behavioural )
Models using 24-hr Activity Data
• Supply Model: Micro simulation, Dynamic, and Agentbased Multi-modal Models
• Demand and Supply Integration (Feedback Loop)
• Case Study: Cross-Regional Commuting Trips (GTHA)
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[email protected]