SCAG ACTIVITY-BASED TRAVEL DEMAND MODEL SimAGENT

1. SCAG ACTIVITY-BASED TRAVEL DEMAND MODEL (SimAGENT) Kostas Goulias University of California Santa Barbara 1

2. SimAGENT for SCAGSimulator of Activities, Greenhouse Emissions, Networks, and Travel Introduction & Definitions Examples Policy Analysis Needs Simagent Phase 1 Simagent Phase 2 Project Tasks and Management 2

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5. Conventional (zonal) Models (spatial structure representation) 5

6. Conventional (zonal) Models(travel behavior representation) 6

7. The 4-step Model 7

8. Improved 4-step 8

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11. A Person’s Daily Travel Pattern (conventional model) 11

12. A Person’s Daily Travel Pattern (activity based model) 12

13. Two Other Household Members Travel Pattern (activity based model) 13

14. All Household Members’ Travel Pattern (activity based model) 14

15. On the zonal system (activity based model) 15

16. Some Key Aspects of Activity Based Models Trips are linked for each person in a day Timing and durations are included Entire daily travel patterns are linked Car use is associated to needs (take child to school, drive together to shop & dine and back ) Removing a trip or activity has a domino effect on everything else that is not “fixed” 16

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18. Examples of policy analysis needs 18

19. Congestion Pricing Toll strategies/pricing Impose a toll and predict elasticity of demand (-0.1 to -0.4) Conventional models Predict shifts in departure & arrival time Observed elasticity lower than predicted Why? Time offset (freeing capacity taken by others) Value of time very different among segments Entire activity-travel schedule modified by pricing Activity-based models could address these issues Predict who reacts to policy at the individual level Predict activity scheduling and task allocation changes within households 19

20. HOV/HOT Conventional models HOV as a mode (time and cost) Overestimate the number of users The problem is lack of accounting for intra-household interactions and carpool formation Activity-based models Include hh-member interactions Include a car assignment to person model/routine 20

21. Parking Conventional models Parking duration not modeled Parking lot = destination of trip Summary demand by period of day Activity based models Explicit estimation of parking duration Operate at fine temporal resolutions Can keep track of cars in households 21

22. Transit fare Conventional models Zone to zone base fares Examine changes in ridership and correlate with fare changes Activity based models Transit paths can be developed The impact of waiting times and costs examined in terms of overall change in scheduling Too much work? Should we calibrate this to potential for change? 22

23. Shorter days and weeks Conventional models Not sensitive to work duration Impose change in trip generation and see what happens Activity-based models Activities, travel, and duration of activities are tied together Changes in work duration and days of the week are explicitly modeled (increases in after work periods, available extra day to do other things and so forth) 23

24. Demographic shifts Conventional models Very few segments Operate at OD level Activity based models Operate at the individual and household levels Include full-time vs. part time workers Include children by age groups Include many additional segmentations because of synthetic population generation Key to this region ethnicity! 24

25. Car ownership and type Conventional models Absent Number of cars per household Activity-based models Explicit car ownership and assignment to persons Type can be incorporated (including fuel type) 25

26. Emissions inventory Conventional models Vehicle activity is handled by post-processing Does not account for within household vehicle assignment and does not produce a vehicle trace -> loss of vehicle use profiles Activity-based models Details about who uses each vehicle and when/where Some produce traces of vehicles during the day New generation emissions models may be more compatible (?!) with this approach Some explore dynamic traffic assignment but not final word yet! 26

27. Land use & development Conventional models Build scenarios and data fed into 4-step Zone to zone travel time and costs (accessibility indicators) used in land use Can be done in an feedback fashion for lagged time Activity based models Offer opportunity for true integration Land use driven by location desires (and developer desires) Travel models use more detailed land use data We hope for parcel detail but many issues are not solved yet 27

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29. SimAGENT Vision Comply with the California Transportation Commission (CTC) 2008 guidelines for RTPs Create an activity-based model that can address wide range of policies, including: Economic analysis: location-based welfare, wages, and exports Equity analysis: change in welfare by household income class Evaluate the energy use and GHGs produced by households and workers in building space Comprehensively evaluate economic development impacts Evaluate time-of-day roadway tolls 29

30. Phase 1: Adapt CEMDAP-DFW to SCAG SimAGENT 30

31. Phase 1: Adapt CEMDAP-DFW to SCAG SimAGENT 31

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33. Phase 1 Comparison of Model Scale SCAG 2003 Validated Model 4192 zones (4,109 internal + 40 cordon, 12 airport, 31 port) Used also for air quality and GHG (CO2) emission estimation with EMFAC Highway network includes freeway system (mixed-flow lane, auxiliary lane, HOV lane, toll lane, truck lane, etc.), arterials, major collectors, and some minor collectors AM peak period (6:00 AM to 9:00 AM) PM peak period (3:00 PM to 7:00 PM) Mid day period (9:00 AM to 3:00 PM) Night period (7:00 PM to 6:00 AM) No Dynamic Traffic Assignment Traditional feed forward land use and assignment Dallas – Fort Worth CEMDAP Study 4,874 Zones (4,813 Internal + 61 External), 18,566 network nodes 22,185 roadway links (26,799 lane miles) + 9,600 zone connector links 63 HOV links (37 lane miles) Highway network used with CEMDAP includes freeways, HOV lanes, major arterials, minor arterials, collectors, ramps, frontages, etc. Morning off peak (3:00 AM to 6:29 AM) AM peak (6:30 AM to 8:59 AM) Mid day off peak (9:00 AM to 3:59 PM) PM peak (4:00 PM to 6:29 PM) Evening off peak (6:30 PM to 2:59 AM) Also tested Dynamic Traffic Assignment Includes key aspects of the integrated model 33

34. PHASE 2: Development of Advanced Version of SimAGENT (2011) Increase spatial detail Accessibility reflected in major interactions Expected to have: Sensitivity to an expanded repertoire of policies Integrated land use influences on travel behavior Enhanced feedback among model components Enhanced reflection of behavioral interactions Integrated interfaces with land use, traffic assignment, and EMFAC and/or MOVES 34

38. PROJECT TASKS AND MANAGEMENT 38

39. Project Tasks 39

40. Tasks-Schedule-Budget 40

41. Project Deliverables 41

42. Project Deliverables 42

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