1. Responses to Gas Prices in Knoxville, TN Vince Bernardin, Jr., Ph.D. Vince Bernardin, Jr., Ph.D. Bernardin, Lochmueller & Associates Mike Conger, P.E. Mike Conger, P.E. Knoxville Regional Transportation Planning Organization

2. Background  Gas price fluctuations prompt the question:  How are changes in gas prices affecting travel?

3. More $$$ = Less VMT  Various studies have attempted to estimate the elasticity of VMT to gas prices -0.07 -0.17  Short term elasticities: -0.07 to -0.17 -0.22 0.33  Long term elasticities: -0.22 to 0.33

4. Components of Travelers Response  Travelers can reduce gas consumption in various ways, some easier than others,  More carpooling  Destinations closer to each other  Destinations closer to home  More transit/walking  Fewer tours (more stops/tour)  Lower activity participation (fewer stops)  Lower vehicle ownership (long term)

5.  Travelers can reduce gas consumption in various ways, some easier than others,  More carpooling  Destinations closer to each other  Destinations closer to home  More transit/walking  Fewer tours (more stops/tour)  Lower activity participation (fewer stops)  Lower vehicle ownership (long term)  Traditional models have represented some of these responses, but neglected others, More carpooling More carpooling X Destinations closer to each other X Destinations closer to home More transit/walking More transit/walking X Fewer tours (more stops/tour) X Lower activity participation (fewer stops) X Lower vehicle ownership (long term) Modeling Travelers Response

6. Challenges  Models have faced two key problems in incorporating additional sensitivity to fuel prices  Data limitations  Structural limitations

7. Data Limitations cross-sectional  Travel models have traditionally been estimated from cross-sectional household survey data  The resulting lack of variation in fuel prices with observed travel behavior has generally precluded the incorporation of fuel prices as a variable

8. Structural Limitations  The traditional four-step model design does not allow the incorporation of many effects mode choice  Changes in mode and car-pooling can be captured in mode choice, but gravity model  The agglomeration of destinations cannot be reflected as the gravity model treats all destination choices as independent cross-classification trip production models  Activity participation and touring rates cannot respond because cross-classification trip production models cannot incorporate fuel price as a variable  Vehicle ownership  Vehicle ownership is generally not modeled at all

9. Overcoming the Challenges  In Knoxville, we are attempting to overcome both challenges 2000- 20012008,  Travel survey data was collected in both 2000- 2001 and again in 2008, yielding data with significant variation in fuel prices hybrid trip-based/tour-based  A new hybrid trip-based/tour-based model design has been adopted which overcomes the structural limitations of the four-step model

10.  Traditional models have represented some of these responses, but neglected others, More carpooling More carpooling X Destinations closer to each other X Destinations closer to home More transit/walking More transit/walking X Fewer tours (more stops/tour) X Lower activity participation (fewer stops) X Lower vehicle ownership (long term)  Traditional models have represented some of these responses, but neglected others, More carpooling More carpooling Destinations closer to each other ?? Destinations closer to each other ?? Destinations closer to home ?? Destinations closer to home ?? More transit/walking More transit/walking X Fewer tours (more stops/tour) Lower activity participation (fewer stops) Lower activity participation (fewer stops) Lower vehicle ownership (long term) Lower vehicle ownership (long term) Modeling Travelers Response

11.  Traditional models have represented some of these responses, but neglected others, More carpooling More carpooling Modeling Travelers Response

12. Carpooling  In the Knoxville model, as in activity-based models, vehicle occupancy is determined by trip mode choice models, distinct from tour mode choice

13. Network TAZ Flow Averaging Traffic Assignment Departure Time Choice Stop Sequence Choice Stop Location Choice Tour Mode Choice Activity / Tour Generation Population Synthesizer Accessibility Travel Times Link Flows Trip Mode Choice VariablesModels Disaggregate Models Aggregate Models Vehicle Availability Choice

14. Carpooling  In the Knoxville model, as in activity-based models, vehicle occupancy is determined by trip mode choice models, distinct from tour mode choice  NL and MNL models of trip mode choice were estimated using the combined 2000-2001 & 2008 datasets 0.128.  The models show a combined elasticity of vehicle occupancy with respect to fuel price of 0.128.

15.  Traditional models have represented some of these responses, but neglected others, More carpooling More carpooling Destinations closer to each other ?? Destinations closer to each other ?? Destinations closer to home ?? Destinations closer to home ?? Modeling Travelers Response

16. Destination Choices  The new Knoxville model does incorporate trip-chaining effects reflecting the fact that travelers group their stops into convenient tours  However, we were unable to directly estimate the effect of fuel prices on trip-chaining or destination choice due to the limitations of our estimation technique

17. Destination Choice  Analysis of the data using regression did show fuel price effects on destination choice  Trip-based perspective -0.114  Home-based trip length elasticity: -0.114 -0.064  Non-home-based trip length elasticity: -0.064  Tour-based perspective -0.036  Direct travel time from home to stop elasticity: -0.036 0.042  Elasticity of destination accessibility: 0.042

18. Destination Choice  Elasticities from regression analysis may be incorporated in stop location choice models through a heuristic calibration effort  Time & labor intensive process  Contingent on schedule and budget feasibility

19.  Traditional models have represented some of these responses, but neglected others, More carpooling More carpooling Destinations closer to each other ?? Destinations closer to each other ?? Destinations closer to home ?? Destinations closer to home ?? More transit/walking More transit/walking Modeling Travelers Response

20. Mode Shifts  Shifts from driving to bus and walking are primarily reflected in tour mode choice  Nested logit models of combined tour mode and stop location choice were estimated sequentially from household travel & on- board survey data 0.853  Modeled elasticity of bus ridership: 0.853 0.318  Observed elasticity of bus ridership from KATS weekly counts for 2006 vs. 2008: 0.318

21.  Traditional models have represented some of these responses, but neglected others, More carpooling More carpooling Destinations closer to each other ?? Destinations closer to each other ?? Destinations closer to home ?? Destinations closer to home ?? More transit/walking More transit/walking X Fewer tours (more stops/tour) Modeling Travelers Response

22. Tour-making  Conceptually, it seems reasonable that travelers may respond to increased fuel prices by reducing travel costs by combining/eliminating tours  However, the Knoxville data showed no evidence of this sort of behavior

23.  Traditional models have represented some of these responses, but neglected others, More carpooling More carpooling Destinations closer to each other ?? Destinations closer to each other ?? Destinations closer to home ?? Destinations closer to home ?? More transit/walking More transit/walking X Fewer tours (more stops/tour) Lower activity participation (fewer stops) Lower activity participation (fewer stops) Modeling Travelers Response

24. Activity Participation  Travelers can also respond by decreasing their participation in out-of-home activities  This effect was observed in the Knoxville data and incorporated in stop generation  Low income travelers discretionary activities  Low income travelers (< $25k/yr) and discretionary activities were primarily affected -0.155 -0.233  Range of elasticities for various income groups and stop types: -0.155 to -0.233

25.  Traditional models have represented some of these responses, but neglected others, More carpooling More carpooling Destinations closer to each other ?? Destinations closer to each other ?? Destinations closer to home ?? Destinations closer to home ?? More transit/walking More transit/walking X Fewer tours (more stops/tour) Lower activity participation (fewer stops) Lower activity participation (fewer stops) Lower vehicle ownership (long term) Lower vehicle ownership (long term) Modeling Travelers Response

26. Vehicle Ownership  Over the long term, travelers can also respond by owning fewer (or more efficient) vehicles  An ordered response logit model for vehicle ownership choice was estimated -0.067  Elasticity of household vehicles with respect to fuel price: -0.067

27. Ongoing Work  Currently, estimation is complete for the new Knoxville model, but work is on-going to calibrate the component models  Hope to estimate total elasticity of VMT to fuel price as part of model validation

28. Thank You!