1. Systems Analysis Group One ABM for Four Cities: Experience of ABM Estimation on a Pooled Dataset of Multiple Surveys Surabhi Gupta, Peter Vovsha, Gaurav Vyas, Parsons Brinckerhoff Inc. Rebekah Anderson, Greg Giaimo, Ohio Department of Transportation

2. Systems Analysis Group 4 Regions CharacteristicsColumbusClevelandCincinnatiDayton Population1.66 M2.02 M1.99 M0.8 M # Counties758 ( OH, IN, KY)3 Transit modesExpress bus, Local bus Heavy Rail, BRT, Express bus, Local bus Express bus, Local bus Toll roadsNoYesNo

3. Systems Analysis Group 4 Regional Household Travel Surveys CharacteristicsColumbusClevelandCincinnatiDayton MPOMORPCNOACAOKIMVPRC # Households5,5554,2502,0501,950 # Days1331 Survey year19992012-1320102001 TypePrompted recall (PR) GPS + partially PR PR Time of the yearFall/SpringAll year Fall/Spring

4. Systems Analysis Group Motivation Develop the most possible generic ABM for all regions: Transferability as desired feature rather than post-development analysis Bigger and richer dataset for advanced ABM compared to any regional HTS on its own

5. Systems Analysis Group Data Processing Consolidating Survey Data: Household File Person File Trips File Vehicle File Recoding Variables: Common variable codes Unknown for missing variables in a particular region

6. Systems Analysis Group How to handle missing data in estimation? Missing independent variables (e.g., income, age etc) Create dummy for missing category Cannot estimate region-specific coefficients for any attribute missing for the region

7. Systems Analysis Group Using Pooled Dataset for Model Estimation / General Approach NYMTC, April 2, 2014 7 Dependent variable Independent variables Y 1 st Survey 2 nd Survey Y X1 X2 X3 Still possible to estimate Y=f(X1,X2,X3)

8. Systems Analysis Group Using Pooled Dataset for Model Estimation / Placeholders NYMTC, April 2, 2014 8 Dependent variable Independent variables Y 1 st Survey 2 nd Survey Y X1 X2 X3 Estimated model example: Y=a1×X1×δ1 + b1×Z1×(1-δ1) +a2×X2 + b3×Z3×(1-δ2) + a3×X3×δ2 Z1 Z3 Placeholders or approximations Applied model: Y=a1×X1 + a2×X2 + a3×X3

9. Systems Analysis Group How to handle missing data in estimation? Missing dependent variable (e.g., work arrangement model) Choice alternatives specific to region based on available data Component-wise utility function and generic coefficients AlternativeNumber of jobsWork place typeAvailable for 1Single jobFixed work placeCleveland and Cincinnati 2Single jobVariable work place Cleveland and Cincinnati 3Single jobHome Cleveland and Cincinnati 4Multiple jobsFixed work place Cleveland and Cincinnati 5Multiple jobsVariable work place Cleveland and Cincinnati 6Multiple jobsHome Cleveland and Cincinnati 7Single job NAColumbus and Dayton 8Multiple jobsNAColumbus and Dayton

10. Systems Analysis Group Transferability Analysis Every model has a rich set of variables: Household characteristics, person characteristics, activity participation, LOS, accessibilities, time-space constraints Statistical analysis and model estimation/calibration: Generic model – no region-specific coefficients or constants Partially segmented – some coefficients or constants are region-specific Fully segmented – all or most coefficients or constants are region-specific

11. Systems Analysis Group Submodels: Generic or Specific? Sub-Model/ ComponentGeneric or Region Specific Work Arrangement ModelPartially segmented Work Location Choice ModelFully segmented Schooling from Home ModelGeneric School Location Choice ModelFully segmented Commuting Frequency ModelGeneric Person Mobility Attributes ModelGeneric Auto Ownership Model Generic Auto Allocation ModelGeneric Coordinated Daily Activity PatternPartially segmented Mandatory Activity and Tour FrequencyPartially segmented Preferred Mandatory Activity Span ModelGeneric Escorting children to SchoolGeneric

12. Systems Analysis Group Submodels: Generic or Specific? Sub-Model/ ComponentGeneric or Region Specific Joint Tour frequency, party composition and household participation Partially segmented Joint Tour destination with stop frequency and location choice Generic Frequency of Household Maintenance tasksGeneric Allocation of Maintenance Tasks to Household Members Generic Person Frequency of Individual ActivitiesPartially segmented Tour Formation ModelsGeneric Tour Time-of-day Choice ModelGeneric Tour Mode Combination ModelFully segmented

13. Systems Analysis Group Fully- Segmented Models Work and School Location Choice Models Size of region shapes tolerance to commuting distance Relative location of population and employment

14. Systems Analysis Group Partially Segmented Models Work Arrangement Coordinated Daily Activity Pattern Mandatory Activity and Tour Frequency Joint Tour frequency, party composition and household participation Person Frequency of Individual Activities

15. Systems Analysis Group Work Arrangement Model Number of Jobs ( 1, 2+) Region specific constants Work Location Type (Fixed, Variable, Home) Generic Available for only 2 surveys (Cleveland and Cincinnati)

16. Systems Analysis Group Coordinated Daily Activity Pattern Mandatory, Non-Mandatory, Home patterns Differences between Older (Columbus, Dayton) vs. Newer (Cleveland, Cincinnati) Surveys Fall/Spring vs. All year for Mandatory frequency Prompted recall vs. GPS for Non-Mandatory vs. Home

17. Systems Analysis Group Mandatory Activity and Tour Frequency Tour Breaks – going home between work episodes Multiple work tours More probable for Dayton – smaller region size

18. Systems Analysis Group Joint Tour Frequency and Participation Cleveland specific constants More maintenance, eating out and discretionary joint tours Lower frequency of joint tours GPS survey, All year

19. Systems Analysis Group Person Frequency of Individual Activities # of Eating out, visiting, and discretionary activities Region specific constants by purpose & frequency Cleveland – time trends? Cincinnati data was not used due to trip purpose imputation issues

20. Systems Analysis Group Conclusions Overall most of the models generic and transferable Pooled dataset supports more advanced behavioral analysis: Recommend cooperation between MPOs Observed differences across regions partially reflect on survey technology and time trends Moving towards more generic and portable models by having a rich set of variables and more flexible specifications Destination choice and travel time-cost perceptions the most fundamental difference across regions: Residential self-choice Endogenize and equilibrate time and cost coefficients as function of regional travel conditions