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. 1 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

3. 2 Home Work Dinner Tour-based Approach: Two Inter-related Tours Shopping Travel is a derived demand from the demand for activities

4. 3 Space At home At work At store At home At dinner Travel to work Travel to store Travel to home Travel to dinner Travel to home Time H W S D H H Example of a Daily Travel Pattern

5. 4 Trip-based Approach: Five Independent Trips Home-based Work Non-home Based Home-based Shop Home-based Other H W S D H H W S H D

6. 5 Trip-based Models Trips are treated independently Departure times Different household activities Modes of outbound and return trips Locations of subsequent stops in a tour All daily trips are “added up” across all households Difficult to account for induced travel H

7. 6 Questions that the Trip-based Models Answer How many trips are produced by each household? How many trips are attracted to each zone? What is the distribution of trip lengths by purpose? Where are the destinations of different trip purposes? What mode was used for each individual trip and how is the choice affected by cost and level of service? How are trips distributed to the network? H

8. 7 Questions that Tour-based Models Answer What is the purpose of the main activity? What is the type and complexity of the tour? How complex is the tour pattern? Are there any stops? Before or after the main destination? How do tour types differ by segment? Where is the main destination of the tour? Where are the intermediate stop destinations? What mode was used in different parts of the tour? To arrive at the main destination? To visit intermediate stops? What combinations of modes were used for different tours? How do major new investments and level of service improvements affect induced travel ? What factors affect time-of-day travel decisions? H

9. 8 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

10. 9 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

11. 10 The Data A three-day trip diary (NTHS) An extension of the NTHS in communities adjacent to rail 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

12. 11 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

13. 12 The Stated-Preference Survey (continued) 1,194 completed questionnaires 4 trip purpose – work, education, shopping, and other (distributed roughly equally) Age 16 and older 32 percent didn’t have driver license 48 percent drove in their selected outward trip plus 15 percent as car passengers H

14. 13 The Stated-Preference Survey (continued) 98 respondents (8 percent) didn’t use the same mode for the return leg as for the outward leg 72 respondents (6 percent) made an intermediate stop on their way to their main destination with 11 out of them making more than one stop On the way back, 132 respondents made an intermediate stop, with 20 out of them making more than one stop. H

15. 14 The Stated-Preference Survey (continued) 39 percent of the respondents never switched mode 20 percent always switched 41 percent switched sometime In general car drivers don’t switch easily to a public transport mode, but it does happen in 22 percent of the observations (not including taxi in public transport). Car passengers switch mode more easily, and bus users chose mainly bus. H

16. 15 Main Activity Main Destination Work Education Shopping Other No tour Dest 1Dest 2Dest 3 Dest 100 Dest 1219 Automobile Ownership ZeroOneTwo + Time of Day c Combination of arriving to and departure from main acitivity

17. 16 Tour Main Mode Revealed-preference: NTHS & Rail Corridor survey Stated-preference: New SP survey & NTA survey TaxiDriverPass. 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

18. 17 “Before Stop” Mode / “After Stop” Mode TaxiDriverPass. Bus Rail “Before Stop” Destination / “After” Stop” Destination Same mode Other as in the Tour Dest 1Dest 2Dest 3 Dest 100 Dest 1219 “Before Stop” Arrival time / “After” Stop” Departure time

19. 18 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

20. 19 Model Application Program Representative Population Activity-based Models: Tours / Destinations / Stops / Modes Network Assignment Zonal data NTHS Census LOS Data De-compose Tours Segment time of Day and mode External trips Truck trips Bus trips O-D Trip Tables by Mode and by Time of Day Auto Ownership model

21. 20 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

22. 21 Congestion Pricing Method: Time of day model sensitive to level of service and costs by time period. Mode choice model sensitive to costs and level of service. Outputs: Changes in travel mode, departure time period, complexity of tours, and network facility. H

23. 22 Potential Response to Congestion Pricing

24. 23 What reactions to Congestion Pricing can different models capture? New ModelTrip Model Cancel a trip or reduce total number of tripsYes Delay departure time for work-related travelYes Change mode for one or more trips/toursYes Combine trips by increasing number of stopsYes Change mode and departure timeYes Shift most non-work trips to of-peak time periods Yes Change route choices in response to pricingYes Change destinationYes

25. 24 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)

26. 25 Parking Policies Method: Mode and destination models sensitive to parking costs, search time for parking, and parking egress time. Supply/demand model can account for time of day parking policies. Parking model estimates changes in parking search time as a function of supply and demand. Outputs: Changes in destination, mode shares, time of day, and assignment to the network. H

27. 26 What reactions to Parking Policies can different models capture? New ModelTrip 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 Combine trips by increasing number of stops Yes Change mode and departure timeYes Shift most non-work trips to off- peak time periods Yes Change destinationYes

28. 27 Land Use and Growth Management Policies Land development incentives around fixed transportation infrastructure Concentrated vs. dispersed development Transit Oriented Development

29. 28 What reactions to Land Use and Growth Management can different models capture? New ModelTrip 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 Reduce length of travel due to mixed development patterns Yes Account for attractiveness of different zones in study area Yes

30. 29 Highway and Transit Improvements Increase transit investment in different corridors Traffic management HOV lanes/Busways Road development

31. 30 What reactions to Highway and Transit Improvements can different models capture? New ModelTrip 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 Reduce highway travel times and increase auto share Yes

32. 31 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