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Using repeated cross-sectional travel surveys to enhance forecasting robustness: Accounting for changing mode preferences

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  • Habib, Khandker M. Nurul
  • Swait, Joffre
  • Salem, Sarah

Abstract

Transportation system capacity and performance, urban form and socio-demographics define the influences and constraints conditioning the preferences of urban residents for different transport modes. Changes in characteristics of urban areas are likely to lead to changes in preferences for alternative modes of transport over time; as a consequence, statistical models to forecast mode choice need to be sensitive to both purposeful changes to urban systems as well as exogenous shocks. We make use of the 1996, 2001 and 2006 household surveys conducted in the Greater Toronto and Hamilton Area to study mode preference evolution and model forecasting performance. These repeated cross-sectional household surveys provide an opportunity to investigate aggregate structural changes in commuting mode preferences over time, in a manner sensitive to changes in the urban area. We focus on commuting mode choices because these trips are prime determinants of peak period congestion and peak spreading. We then address how to combine the three cross-sections econometrically in a robust way that allows for use of a single mode choice model across the entire period. Using independent data from 2012, we are able to compare the individual year and combined models in terms of forecasting performance to demonstrate the combined model’s more robust forecasting performance into the future.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:transa:v:67:y:2014:i:c:p:110-126
    DOI: 10.1016/j.tra.2014.06.004
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    Cited by:

    1. Habib, Khandker Nurul, 2017. "Improving choice model parameter estimates by jointly modelling the SP choices with corresponding elicited certainty ratings," Transportation Research Part A: Policy and Practice, Elsevier, vol. 95(C), pages 305-319.
    2. 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).
    3. Rezaei, Ali & Patterson, Zachary, 2018. "Preference stability in household location choice: Using cross-sectional data from three censuses," Research in Transportation Economics, Elsevier, vol. 67(C), pages 44-53.
    4. Timmer, Sebastian & Merfeld, Katrin & Henkel, Sven, 2023. "Exploring motivations for multimodal commuting: A hierarchical means-end chain analysis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 176(C).
    5. Michael Iacono & David Levinson, 2015. "Cohort Effects and Their Influence on Car Ownership," Working Papers 000138, University of Minnesota: Nexus Research Group.
    6. Sarah Salem & Khandker M. Nurul Habib, 2019. "Use of repeated cross-sectional travel surveys for developing meta models of activity-travel scheduling processes," Transportation, Springer, vol. 46(2), pages 395-423, April.
    7. 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.
    8. Giancarlos Parady & Kay W. Axhausen, 2024. "Size matters: the use and misuse of statistical significance in discrete choice models in the transportation academic literature," Transportation, Springer, vol. 51(6), pages 2393-2425, December.
    9. Habib, Khandker Nurul, 2023. "Rational inattention in discrete choice models: Estimable specifications of RI-multinomial logit (RI-MNL) and RI-nested logit (RI-NL) models," Transportation Research Part B: Methodological, Elsevier, vol. 172(C), pages 53-70.

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