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Investigating Contextual Variability in Mode Choice in Chicago Using a Hierarchical Mixed Logit Model

Author

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  • Liang Long

    (Cambridge Systematics, Inc., 4800 Hampden Lane, Suite 800, Bethesda, Maryland, 20814, USA, llong@camsys.com)

  • Jie Lin

    (Department of Civil and Materials Engineering, University of Illinois at Chicago, 842 W. Taylor Street (M/C 246), Chicago, Illinois, 60607, USA, janelin@uic.edu)

  • Kimon Proussaloglou

    (Cambridge Systematics, Inc., 4800 Hampden Lane, Suite 800, Bethesda, Maryland, 20814, USA, kproussaloglou@camsys.com)

Abstract

In this paper, a hierarchical random-coefficient mixed logit model is applied to quantify variability in commuters’ mode choice in the Chicago metropolitan area, especially concerning the contextual variability by the traits of census tract of residence. It is found that individual mode choice behaviour varies considerably across residential locations. Moreover, the contextual effects are found to modify the marginal utility of mode choice. Especially, in-vehicle travel time and gasoline cost are significant covariates of census tract traits (such as percentage of blue-collar residents, ethnicity). Furthermore, random variation is present even after both contextual and individual traits are controlled for, suggesting intrinsic randomness in individual mode choice. The hierarchical structure of quantifying contextual variability proves to be a useful tool in capturing intrinsic heterogeneity in mode choice. The study findings have important implications for integrated land use and transport planning especially at the geographical levels below that of the region.

Suggested Citation

  • Liang Long & Jie Lin & Kimon Proussaloglou, 2010. "Investigating Contextual Variability in Mode Choice in Chicago Using a Hierarchical Mixed Logit Model," Urban Studies, Urban Studies Journal Limited, vol. 47(11), pages 2445-2459, October.
    • Handle: RePEc:sae:urbstu:v:47:y:2010:i:11:p:2445-2459
    DOI: 10.1177/0042098009357965
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    References listed on IDEAS

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    Cited by:

    1. Donna, Javier D., 2018. "Measuring Long-Run Price Elasticities in Urban Travel Demand," MPRA Paper 90260, University Library of Munich, Germany.
    2. Schneider, Robert James, 2011. "Understanding Sustainable Transportation Choices: Shifting Routine Automobile Travel to Walking and Bicycling," University of California Transportation Center, Working Papers qt06v2g6dh, University of California Transportation Center.
    3. 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.

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