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Further evidence on the temporal stability of disaggregate travel demand models

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  • McCarthy, Patrick S.
Abstract
Current evidence on the transferability of disaggregate travel demand models is inconclusive. Adding to this body of research, the present analysis focuses upon the temporal characteristics of work trip behavior in the San Francisco Bay Area. Using before and after data sets associated with the BART Impact Travel Study, multinomial logit models of work trip modal choice are estimated. The results indicate that the general form and the coefficient estimates of a pre BART model are transferable in time. Moreover, when updated to reflect BART's presence, the model's predictive success and its implied elasticity measures are generally accurate, relative to those implied by reestimating the entire model on post BART data. Finally, as economic theory would predict, elasticity measures of the service related variables were found to increase over time.

Suggested Citation

  • McCarthy, Patrick S., 1982. "Further evidence on the temporal stability of disaggregate travel demand models," Transportation Research Part B: Methodological, Elsevier, vol. 16(4), pages 263-278, August.
    • Handle: RePEc:eee:transb:v:16:y:1982:i:4:p:263-278
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    Cited by:

    1. Fouquet, Roger & O'Garra, Tanya, 2022. "In pursuit of progressive and effective climate policies: Comparing an air travel carbon tax and a frequent flyer levy," Energy Policy, Elsevier, vol. 171(C).
    2. Fox, James & Daly, Andrew & Hess, Stephane & Miller, Eric, 2014. "Temporal transferability of models of mode-destination choice for the Greater Toronto and Hamilton Area," The Journal of Transport and Land Use, Center for Transportation Studies, University of Minnesota, vol. 7(2), pages 41-62.
    3. Karasmaa, Nina, 2007. "Evaluation of transfer methods for spatial travel demand models," Transportation Research Part A: Policy and Practice, Elsevier, vol. 41(5), pages 411-427, June.
    4. Paul A. Longley & Richard Dunn, 1988. "Graphical Assessment of Housing Market Models," Urban Studies, Urban Studies Journal Limited, vol. 25(1), pages 21-33, February.
    5. Nobuhiro Sanko & Takayuki Morikawa, 2010. "Temporal transferability of updated alternative-specific constants in disaggregate mode choice models," Transportation, Springer, vol. 37(2), pages 203-219, March.
    6. Maria Börjesson, 2014. "Inter-temporal variation in the travel time and travel cost parameters of transport models," Transportation, Springer, vol. 41(2), pages 377-396, March.
    7. Holguín-Veras, José & Ramirez-Rios, Diana & Pérez-Guzmán, Sofía, 2021. "Time-dependent patterns in freight trip generation," Transportation Research Part A: Policy and Practice, Elsevier, vol. 148(C), pages 423-444.
    8. Vij, Akshay & Gorripaty, Sreeta & Walker, Joan L., 2017. "From trend spotting to trend ’splaining: Understanding modal preference shifts in the San Francisco Bay Area," Transportation Research Part A: Policy and Practice, Elsevier, vol. 95(C), pages 238-258.
    9. Habib, Khandker M. Nurul & Swait, Joffre & Salem, Sarah, 2014. "Using repeated cross-sectional travel surveys to enhance forecasting robustness: Accounting for changing mode preferences," Transportation Research Part A: Policy and Practice, Elsevier, vol. 67(C), pages 110-126.
    10. Nobuhiro Sanko, 2014. "Travel demand forecasts improved by using cross-sectional data from multiple time points," Transportation, Springer, vol. 41(4), pages 673-695, July.

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