A New Policy Sensitive Travel Demand Model for Tel Aviv

1. 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 Association June 21, Haifa University

2. 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 capturecomplex travel behavior patterns better than traditional models

3. Tour-based Approach:Two Inter-related Tours Dinner Home Travel is a derived demand from the demand for activities Shopping Work

4. Space Travel to work Travel to store Travel to home Travel to dinner Travel to home Example of a Daily Travel Pattern At home H Time W At work At store S At home H D At dinner At home H

5. Trip-based Approach:Five Independent Trips H Home-based Work W W Non-home Based S S Home-based Shop H Home-based Other H D D Home-based Other H

6. 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

7. 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

8. 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

9. 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

10. Automobile Ownership Zero One Two + Main Activity Work Education Shopping Other No tour Time of Day Combination of arriving to and departure from main acitivity c Main Destination Dest 1 Dest 2 Dest 3 Dest 100 Dest 1219

11. Tour Main Mode Revealed-preference: NTHS & Rail Corridor survey Stated-preference: New SP survey & NTA survey Taxi Driver Pass. Bus Rail Employer Transport P&R, K&R, Walk P&R, K&R, Walk, Bus No stops Before After Before and After “Before Stop” Type / “After Stop” Type Work Education Shopping Other No stop

12. “Before Stop” Arrival time / “After” Stop” Departure time “Before Stop” Destination / “After” Stop” Destination Dest 1 Dest 2 Dest 3 Dest 100 Dest 1219 “Before Stop” Mode / “After Stop” Mode Same mode Other as in the Tour Taxi Driver Pass. Bus Rail

13. 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

14. Model Application Program LOS Data Auto Ownership model Zonal data NTHS Census Activity-based Models: Tours / Destinations / Stops / Modes Representative Population De-compose Tours External trips Truck trips Bus trips Network Assignment O-D Trip Tables by Mode and by Time of Day Segment time of Day and mode

15. 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

16. Potential Response to Congestion Pricing

17. What reactions to Congestion Pricingcan different models capture?

18. 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)

19. What reactions to Parking Policiescan different models capture?

20. Land Use and Growth Management Policies • Land development incentives around fixed transportation infrastructure • Concentrated vs. dispersed development • Transit Oriented Development

21. What reactions to Land Use and Growth Management can different models capture?

22. Highway and Transit Improvements • Increase transit investment in different corridors • Traffic management • HOV lanes/Busways • Road development

23. What reactions to Highway and Transit Improvements can different models capture?

24. 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