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. 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

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

ValidationVARIABLE SACMET SACSIM

Auto Ownership (vs. Census) 

# 0 - Auto HH / RAD 1.11 1.00

(RMSE) 61% 38%

Vehicle Assignment (Yr.2000 Counts) 

Daily Link Volumes 0.97 0.94

(RMSE) 33% 34%

AM (3hrs) 0.97 0.91

(RMSE) 33% 36%

Midday (5 hrs) 0.91 0.91

(RMSE) 24% 31%

PM (3 hrs) 0.99 1.11

(RMSE) 25% 34%

Evening (13 hrs) 0.77 0.92

(RMSE) 38% 34%

Transit Assignment (vs. 2005 O.B. Survey…)

  tba tba

Census Worker FlowsSACMET

0

5,000

10,000

15,000

20,000

Census

SA

CM

ET

Census Worker FlowsSACSIM

0

5,000

10,000

15,000

20,000

Census

SA

CS

IM

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…

Sample home

Central City

Shopping Center

Office

Trip 7

Trip 1

Trip 2

Trip 5

Trip 6School

Trip 8

Trip 9

Soccer field Trip 10

Trip 11

Office

Trip 3

Trip 4

Trip Shortening…

Sample home

Old Job Location

Shopping Center

Old Job Location

Trip 7

Trip 1

Trip 2Trip 5

Trip 6School

Trip 8

Trip 9

Soccer field Trip 10

Trip 11

Old Appt.location

Trip 3

Trip 4

New Job

New JobNew Appt.Location

Mode Shift…

Sample home

Central City

Shopping Center

Office

Trip 7

Trip 5

Trip 1

Trip 4

Trip 6School

Trip 8

Soccer field Trip 9

Trip 10

Office

Trip 2

Trip 3

Trip 11

Trip 12

Perf. Measures (cont’d)

PERF. MEASURESACMET(w/o 4Ds) SACSIM

VMT / HH

2005 50 46 to 48

2035 45 41 to 45

Change - 10% -5% to -10%

Transit Shares (of HH-Generated) 

HBW Trips    

2005 3.4% 2.7%

2035 4.4% 4.7 to 5.8%

Change 29% +74 to +111%

All Trips    

2005 1.1% 1.0%

2035 1.6% 1.9 to 2.6%

Change 45% +73% to +163%

Non-Motorized Shares (of HH-Generated) 

2005 6.0% 6.8%

2035 6.0% 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 / HHby Density w/in ¼ Mi. of

HH

0

5

10

15

20

25

30

35

40

45

50

55

60

<=4.0 4+ to 10 10+ to 20 20+ to 40 >40

Total Density {(jobs+du's)/acre}

Veh

icle

Miles

Tra

vele

d P

er H

ouse

hold

SACSIM

VMT / HHby Density w/in ¼ Mi. of

HH

0

5

10

15

20

25

30

35

40

45

50

55

60

<=4.0 4+ to 10 10+ to 20 20+ to 40 >40

Total Density {(jobs+du's)/acre}

Veh

icle

Miles

Tra

vele

d P

er H

ouse

hold

Survey SACSIM

VMT / HHby Density w/in ¼ Mi. of

HH

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

<=4.0 4+ to 10 10+ to20

20+ to40

>40

Total Density {(jobs+du's)/acre}

% o

f Tri

ps

by

Wal

k,B

ike,

Tra

nsi

t

SACSIM

c

VMT / HHby Density w/in ¼ Mi. of

HH

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

<=4.0 4+ to 10 10+ to20

20+ to40

>40

Total Density {(jobs+du's)/acre}

% o

f Tri

ps

by

Wal

k,B

ike,

Tra

nsi

t

Survey SACSIM

c