Tel Aviv Global City Work Plan 2011 Tel Aviv-Yafo Municipality.
A New Policy Sensitive Travel Demand Model for Tel Aviv
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Transcript of A New Policy Sensitive Travel Demand Model for Tel Aviv
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A New Policy Sensitive Travel Demand Model
for Tel Aviv
Yoram Shiftan
Transportation Research Institute
Faculty of Civil and Environmental Engineering
The Technion
The Israel Regional Science AssociationJune 21, Haifa University
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Introduction and Motivation
Need for a policy-sensitive model
Range of transportation policies under study:• Congestion pricing
• Parking policies
• Land use and growth management
• Highway and transit improvements
Need for an integrated appraisal for air quality, environmental impact assessment, and induced demand
Tour models can capture complex travel behavior patterns better than traditional models
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Home
Work
Dinner
Tour-based Approach:Two Inter-related Tours
ShoppingTravel is a derived demand from the demand for activities
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Space
At home
At work
At store
At home
At home
At dinner
Travel to work
Travel to store
Travel to home
Travel to dinner
Travel to home
Tim
e
H
W
S
D
H
H
Example of a Daily Travel Pattern
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Trip-based Approach:Five Independent Trips
Home-based Work
Non-home Based
Home-based Shop
Home-based Other
Home-based Other
H
W
S
DH
H
WS
H
D
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Review of the Current Tel Aviv Model System
A trip-based model
Traditional model components
• Four-step model – trip generation, trip distribution, mode choice and network assignment
Designed for evaluating mass transit alternatives
• Sophisticated mode choice model development
• Lack of level of service variables in trip generation
• Reliance on a gravity model for trip distribution
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Review of the Tel-Aviv Model System
“Best practice” tour-based model system
Builds on existing data sources• National travel diary survey (NTHS)• Mass transit stated preference survey (NTA)
Reliance on new surveys• Parking supply survey• Rail corridor random survey• Tour-based stated-preference survey
Other enhancements• Revised transit and highway networks• Refined level of service estimates• Zone attributes based on NTA’s approach
Policy Sensitive
Can account for induced demand
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The Data
A three-day trip diary (NTHS)
An extension of the NTHS in communities adjacent torail corridors
A stated-preference survey conducted for a previous study to analyze the potential for a new rapid transit system
A tour-based stated-preference survey designed and conducted for this study
A detailed parking survey that includes information on demand and supply
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The Stated-Preference Survey
Details about one’s actual tour
Various auto restraint policies
• Congestion pricing
• Parking pricing
Various alternative responses
• Change mode/access mode
• Change number of stops
• Change time of travel
6 choice experiments per respondent
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Main Activity
Main Destination
Work Education Shopping Other No tour
Dest 1 Dest 2 Dest 3 Dest 100 Dest 1219
Automobile Ownership
Zero One Two +
Time of Day
cCombination of arriving to and departure from main acitivity
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Tour Main Mode
Revealed-preference: NTHS & Rail Corridor surveyStated-preference: New SP survey & NTA survey
Taxi Driver Pass. Bus Rail Employer Transport
P&R, K&R,Walk, Bus
P&R, K&R,Walk
“Before Stop” Type / “After Stop” Type
Work Education Shopping Other No stop
No stops Before After Before and After
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“Before Stop” Mode / “After Stop” Mode
Taxi Driver Pass. Bus Rail
“Before Stop” Destination / “After” Stop” Destination
Same mode Otheras in the Tour
Dest 1 Dest 2 Dest 3 Dest 100 Dest 1219
“Before Stop” Arrival time / “After” Stop” Departure time
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Model Application Program
Proposed approach• Sample enumeration• Monte Carlo simulation• Incremental approach
Practical considerations• Validation standards and targets• Simplifications in the model structure• Tradeoffs between model sensitivity and model run times
Flexible and modular architecture
Ability to run individual model components
Ability to apply with different sample sizes
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Model Application Program
Representative Population
Activity-basedModels:
Tours / Destinations / Stops / Modes
NetworkAssignment
Zonal dataNTHS
Census
LOS Data
De-compose Tours
Segment time of Day and mode
External tripsTruck tripsBus trips
O-D Trip Tablesby Mode and by
Time of Day
Auto Ownershipmodel
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Policy Evaluation: Congestion Pricing
Policy: Introduce congestion pricing in an area, a
corridor, or a facility during different times of day
Potential impacts on:
• Tour generation
• Share of different modes
• Traffic levels on alternate route(s)
• Distribution of travel by time of day
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Potential Response to Congestion Pricing
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What reactions to Congestion Pricingcan different models capture?
New Model Trip Model
Cancel a trip or reduce total number of trips Yes
Delay departure time for work-related travel Yes
Change mode for one or more trips/tours Yes Yes
Combine trips by increasing number of stops Yes
Change mode and departure time Yes
Shift most non-work trips to of-peak time periods
Yes
Change route choices in response to pricing Yes Yes
Change destination Yes
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Parking Policies
Parking cost increase by region or time of day
Reduced parking supply
Prohibited parking zones
Park and Ride/Kiss and Ride
Time limits
Parking location (walk time)
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What reactions to Parking Policiescan different models capture?
New Model Trip Model
Cancel a trip or reduce total number of trips
Yes
Delay departure time for work-related travel
Yes
Change mode for one or more trips/tours
Yes Yes
Combine trips by increasing number of stops
Yes
Change mode and departure time Yes
Shift most non-work trips to off-peak time periods
Yes
Change destination Yes
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Land Use and Growth Management Policies
Land development incentives around fixed
transportation infrastructure
Concentrated vs. dispersed development
Transit Oriented Development
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What reactions to Land Use and Growth Management can different models capture?
New Model Trip Model
Increase stops / combine trips with dense mixed development
Yes
Concentrate trips with transit-oriented development
Yes
Increase transit market share with service improvements
Yes Yes
Reduce length of travel due to mixed development patterns
Yes Yes
Account for attractiveness of different zones in study area
Yes
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Highway and Transit Improvements
Increase transit investment in different corridors
Traffic management
HOV lanes/Busways
Road development
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What reactions to Highway and Transit Improvements can different models capture?
New Model Trip Model
Increase trip making (induced demand) to better served areas
Yes
Shift travel to areas with improved accessibility
Yes
Increase transit market share with service improvements
Yes Yes
Reduce highway travel times and increase auto share
Yes Yes
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Model Capability Summary
More policies can be analyzed
• Parking supply and congestion pricing
More impacts can be analyzed
• Trip chainning, change destination, cancel trip.
Account for induced demand
Provide more realistic response to policies
Provide better input for air quality analysis
• Enable estimation of cold and hot starts