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

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

Overview Background on Models Validation Performance Measures

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!

Sacramento Facts (cont’d) Growing –20,000 dwellings / year since Yr –50,000 people / year since Yr –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

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

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.)

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

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

Validation VARIABLESACMETSACSIM Auto Ownership (vs. Census) # 0 - Auto HH / RAD (RMSE)61%38% Vehicle Assignment (Yr.2000 Counts) Daily Link Volumes (RMSE)33%34% AM (3hrs) (RMSE)33%36% Midday (5 hrs)0.91 (RMSE)24%31% PM (3 hrs) (RMSE)25%34% Evening (13 hrs) (RMSE)38%34% Transit Assignment (vs O.B. Survey…) tba

Census Worker Flows SACMET

Census Worker Flows SACSIM

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

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

HH VMT for “Sample” Family…

Trip Shortening…

Mode Shift…

Perf. Measures (cont’d) PERF. MEASURE SACMET (w/o 4Ds)SACSIM VMT / HH to to 45 Change- 10%-5% to -10% Transit Shares (of HH-Generated) HBW Trips %2.7% %4.7 to 5.8% Change29%+74 to +111% All Trips %1.0% %1.9 to 2.6% Change45%+73% to +163% Non-Motorized Shares (of HH-Generated) %6.8% %7.1% Change--+ 4%

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

VMT / HH by Density w/in ¼ Mi. of HH