Making advanced travel forecasting models affordable through model transferability 14th TRB Conference on Transportation Planning Applications May 5-9,

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Presentation transcript:

Making advanced travel forecasting models affordable through model transferability 14th TRB Conference on Transportation Planning Applications May 5-9, 2013, Columbus, Ohio John L Bowman, Mark Bradley, Joe Castiglione, Supin Yoder

Acknowledgments Sponsored by FHWA under the STEP program Data provided by California DOT Fehr & Peers Florida DOT Fresno County COG Sacramento Area COG San Joaquin County COG San Diego Association of Governments TRB Planning Apps Conf May 2013Bowman, et al2

TRB Planning Apps Conf May 2013Bowman, et al3 Outline Introduction Transferability testing methods Results

TRB Planning Apps Conf May 2013Bowman, et al4 Objective Empirically test and demonstrate the transferability of activity-based (AB) models between regions Why? Reduce AB development costs large household survey estimating entirely new models

Six regions in study TRB Planning Apps Conf May 2013Bowman, et al5 Sacramento San Diego Northern San Joaquin Valley Jacksonville Tampa Fresno

Activity-Based Models: John L Bowman, Ph.D. ( AB Model Framework

Fifteen tested model components TRB Planning Apps Conf May 2013Bowman, et al7 Model TypeNumber of coefficients Usual work location48 Auto ownership24 Person-day tour generation126 Exact number of tours86 Work tour time of day69 Work tour mode (detailed LOS)58 Work tour mode (combined LOS)31 Work-based subtour generation14 School tour mode32 Other tour destination62 Other tour time of day86 Other tour mode41 Intermediate stop generation100 Intermediate stop location66 Trip time of day45 Total888

Seven untested model components TRB Planning Apps Conf May 2013Bowman, et al8 Model Type Usual school location Work tour destination Escort tour mode Work-based subtour mode School tour time of day Work-based subtour time of day Trip mode

Eleven variable types TRB Planning Apps Conf May 2013Bowman, et al9 Variable Type Number of coefficients A-constant192 P-person184 H-household149 D-day pattern76 T-tour/trip199 I-impedance110 U-land use99 W-time window45 C-logsum24 G-size variable35 L-log size multiplier3 Total1116

Transferability testing: two approaches Application-Based Apply model system developed for another region Compare predictions to observed aggregate outcomes Estimation-Based Estimate coefficients for both regions Compare them for statistical differences TRB Planning Apps Conf May 2013Bowman, et al10

Strengths of the estimation-based approach Explicit statistical tests Can address a wide variety of hypotheses Can test transferability of specific variable types and model components TRB Planning Apps Conf May 2013Bowman, et al11

Data issues Data problems can confound transferability test results Inconsistent data Small samples TRB Planning Apps Conf May 2013Bowman, et al12

Estimability questions What estimation sample size is adequate? How does combining samples improve estimability? Which models are more estimable at the regional level? TRB Planning Apps Conf May 2013Bowman, et al13

Transferability Hypothesis 1 Variables that apply to population segments defined by characteristics of individuals and/or their situational context (i.e. segment-specific variables) will tend to be more transferable than variables that are more generic and apply to all individuals. TRB Planning Apps Conf May 2013Bowman, et al14

Transferability Hypothesis 2 Variables that are segment-specific will tend to be more transferable than alternative-specific constants. TRB Planning Apps Conf May 2013Bowman, et al15

Transferability Hypothesis 3 Models that deal with social organization (activity generation and scheduling) will be more transferable than models that deal mainly with spatial organization (mode choice and location choice) TRB Planning Apps Conf May 2013Bowman, et al16

Transferability Hypothesis 4 Models for different regions within the same state will tend to be more transferable than models for regions in different parts of the country TRB Planning Apps Conf May 2013Bowman, et al17

TRB Planning Apps Conf May 2013Bowman, et al18 Outline Introduction Transferability testing methods Results

Testing method overview Prepare data Estimate separate models Estimate comparison models Tabulate and analyze results TRB Planning Apps Conf May 2013Bowman, et al19

Data preparation overview TRB Planning Apps Conf May 2013Bowman, et al20 For each region:

NHTS Sample Sizes RegionNumber of Households Fresno380 Northern San Joaquin Valley660 Sacramento1,310 San Diego6,000 California Total8,350 Jacksonville1,050 Tampa2,500 Florida Total 3,550 Two-state total 11,900 TRB Planning Apps Conf May 2013Bowman, et al21

Testing method overview Prepare data Estimate separate models Estimate comparison models Tabulate and analyze results TRB Planning Apps Conf May 2013Bowman, et al22

Estimating separate models Generate base model specs Estimate 90 separate models (15 models x 6 regions) Constrain inestimable coefficients TRB Planning Apps Conf May 2013Bowman, et al23

Testing method overview Prepare data Estimate separate models Estimate comparison models Tabulate and analyze results TRB Planning Apps Conf May 2013Bowman, et al24

Estimating comparison models For each of the 15 models : Combine data for all regions Estimate 36 versions of each model 12 base model versions 24 difference model versions TRB Planning Apps Conf May 2013Bowman, et al25

Utility functions TRB Planning Apps Conf May 2013Bowman, et al26

Testing Method Overview Prepare data Estimate separate models Estimate comparison models Tabulate and analyze results TRB Planning Apps Conf May 2013Bowman, et al27

TRB Planning Apps Conf May 2013Bowman, et al28 Outline Introduction Transferability testing methods Results Hypotheses tested Most important conclusion

Transferability hypotheses (Hypothesis 1) Segment-specific variables will tend to be more transferable than variables that are more generic and apply to all individuals. (accepted) (Hypothesis 2) Variables that are segment- specific will tend to be more transferable than alternative-specific constants. (rejected) TRB Planning Apps Conf May 2013Bowman, et al29

TRB Planning Apps Conf May 2013Bowman, et al30

Transferability hypotheses (Hypothesis 3) Models that deal with social organization (activity generation and scheduling) will be more transferable than models that deal mainly with spatial organization (mode choice and location choice) (accepted) TRB Planning Apps Conf May 2013Bowman, et al31

1 TRB Planning Apps Conf May 2013Bowman, et al32

Transferability hypotheses (Hypothesis 4) Models for different regions within the same state will tend to be more transferable than models for regions in different parts of the country California—accepted (weakly) Florida—rejected, Jacksonville more transferable with California than with Tampa TRB Planning Apps Conf May 2013Bowman, et al33

TRB Planning Apps Conf May 2013Bowman, et al34 Outline Introduction Transferability testing methods Results Hypotheses tested Most important conclusions

Most Important Conclusions evidence of broad comparability among all the regions, with one region, Tampa, standing out as less comparable than the others sample sizes of 6,000 households or more provide much better information for estimating coefficients than samples of 2,500 or less. Better to transfer models based on large sample from comparable region than to estimate new models using a much smaller local sample TRB Planning Apps Conf May 2013Bowman, et al35

Comparability—2 state TRB Planning Apps Conf May 2013Bowman, et al36

Comparability within state TRB Planning Apps Conf May 2013Bowman, et al37

Most Important Conclusions evidence of broad comparability among all the regions, with one region, Tampa, standing out as less comparable than the others sample sizes of 6,000 households or more provide much better information for estimating coefficients than samples of 2,500 or less. Better to transfer models based on large sample from comparable region than to estimate new models using a much smaller local sample TRB Planning Apps Conf May 2013Bowman, et al38

Need for large samples TRB Planning Apps Conf May 2013Bowman, et al39

Most Important Conclusions evidence of broad comparability among all the regions, with one region, Tampa, standing out as less comparable than the others sample sizes of 6,000 households or more provide much better information for estimating coefficients than samples of 2,500 or less. Better to transfer models based on large sample from comparable region than to estimate new models using a much smaller local sample TRB Planning Apps Conf May 2013Bowman, et al40

TRB Planning Apps Conf May 2013Bowman, et al41

12 Base model versions 6 region-specific versions 2 2-state versions With region-specific ASCs Without region-specific ASCs 2 FL versions (with & without region ASCs) 2 CA versions (with & without region ASCs) TRB Planning Apps Conf May 2013Bowman, et al42

24 Difference model versions Four versions for each region each with difference variables relative to a base version 2 state base 2 state base with region-specific ASCs 1 state base 1 state base with region-specific ASCs TRB Planning Apps Conf May 2013Bowman, et al43

TRB Planning Apps Conf May 2013Bowman, et al44

TRB Planning Apps Conf May 2013Bowman, et al45