Comparison of an ABTM and a 4-Step Model as a Tool for Transportation Planning

1. Comparison of an ABTM and a 4-Step Model as a Tool for Transportation Planning TRB Transportation Planning Application Conference May 8, 2007

2. Acknowledgments • ABTM Model (Daysim) Designers, Architects • John Bowman, Ph.D • Mark Bradley • Application and Shell Program Developers • John Gibb, DKS Associates • Parcel Data Production Process • Steve Hossack, SACOG

3. Overview • Background on Models • Validation • Performance Measures

4. Sacramento Facts • 2.1 million people • Nearly 1 million jobs • State capitol • Unique geography: • To West: SF Bay Delta (San Francisco=90 miles) • To East: Sierra Nevada Mountains • To North, South: Sacramento, San Joaquin Valleys • Rivers!

5. Sacramento Facts (cont’d) • Growing • 20,000 dwellings / year since Yr. 2000 • 50,000 people / year since Yr. 2000 • Since 1997: 3 new cities formed, more on the way… • SACOG • MPO for part or all of 6 counties + cities within • Board=31 elected officials from 28 jurisdictions • Current work transit share • 3% for region • 20% for jobs in CBD • +/- 1% for jobs elsewhere

6. SACOG Models: SACMET • SACMET = Traditional 4-step model • HH’s cross classified (P x W x I) • 4 home-based purposes • 2 non-home-based (but still household-generated) purposes • 7 modes incl. bike, walk • Commercial vehicle “purpose” • Mode/destination choice for HBW • Gravity distribution for else • Fixed time-of-travel factors • Conventional assignments • Runs = 6 hours on good PC

7. SACOG Models: SACSIM • SACSIM = ABTM • Synthetic population (controls = P x W x I, Age, …) • 7 activity types (work, school, escort, shop, pers.bus., meal, soc/rec.) • 7 modes incl. bike, walk • Long term choice (auto ownership, work location) • Day pattern (#’s, types of tours, 0/1 stops per tour, etc) • “Short term” choice models (i/m stops and locations, tour/trip mode, times of travel, etc.)

8. SACOG Models: SACSIM (cont’d) • Population, employment and some transport variables input at “parcel/point” level of detail (650k non-empty parcels) • Proximity measures = combination TAZ-to-TAZ skims + parcel-to-parcel orthogonal distances • Shorter trips more parcel-to-parcel, longer trips more TAZ-to-TAZ

9. SACOG Models: SACSIM (cont’d) • Major SACSIM operational components • DAYSIM = stand-alone ABTM program, handles household-generated, I-I travel only • TP+ application handles rest: • I-X, X-I, X-X • Commercial vehicles • Airport passenger • Skims going into DAYSIM • Reads DAYSIM outputs, creates assignable (TAZ-to-TAZ) trip tables • Iteration / conversion looping and sampling • Runs = 12 – 20 hours on good PC

10. Validation

11. Census Worker FlowsSACMET

12. Census Worker FlowsSACSIM

13. Validation (cont’d) • Key differences • Lots more to calibrate/validate w/ SACSIM • Population characteristics • Travel behavior by person type • Time of travel • Observed data feels even more inadequate than before • More “natural” solutions to odd/errant outputs

14. Performance Measures • Household-Generated VMT • The number of vehicle miles a household requires to perform their daily activities • Developed during Blueprint planning process • Decreases in HH VMT for: • Mixed use (shortening trips) • Density (more non-motorized) • Mode shift

15. HH VMT for “Sample” Family…

16. Trip Shortening…

17. Mode Shift…

18. Perf. Measures (cont’d)

19. Given Similarity in Result, Why Bother? • Parcel input data eliminates some TAZ aggregation “bias” • ABTM + synthetic population accounts for demographics more directly • Potential for tying travel more directly to: • Land use • Demographics • EJ analysis

20. VMT / HHby Density w/in ¼ Mi. of HH

21. VMT / HHby Density w/in ¼ Mi. of HH

22. VMT / HHby Density w/in ¼ Mi. of HH

23. VMT / HHby Density w/in ¼ Mi. of HH