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Development of destination choice models for pedestrian travel

Listed author(s):
  • Clifton, Kelly J.
  • Singleton, Patrick A.
  • Muhs, Christopher D.
  • Schneider, Robert J.
Registered author(s):

    Most research on walking behavior has focused on mode choice or walk trip frequency. In contrast, this study is one of the first to analyze and model the destination choice behaviors of pedestrians within an entire region. Using about 4500 walk trips from a 2011 household travel survey in the Portland, Oregon, region, we estimated multinomial logit pedestrian destination choice models for six trip purposes. Independent variables included terms for impedance (walk trip distance), size (employment by type, households), supportive pedestrian environments (parks, a pedestrian index of the environment variable called PIE), barriers to walking (terrain, industrial-type employment), and traveler characteristics. Unique to this study was the use of small-scale destination zone alternatives. Distance was a significant deterrent to pedestrian destination choice, and people in carless or childless households were less sensitive to distance for some purposes. Employment (especially retail) was a strong attractor: doubling the number of jobs nearly doubled the odds of choosing a destination for home-based shopping walk trips. More attractive pedestrian environments were also positively associated with pedestrian destination choice after controlling for other factors. These results shed light on determinants of pedestrian destination choice behaviors, and sensitivities in the models highlight potential policy-levers to increase walking activity. In addition, the destination choice models can be applied in practice within existing regional travel demand models or as pedestrian planning tools to evaluate land use and transportation policy and investment scenarios.

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    Article provided by Elsevier in its journal Transportation Research Part A: Policy and Practice.

    Volume (Year): 94 (2016)
    Issue (Month): C ()
    Pages: 255-265

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    Handle: RePEc:eee:transa:v:94:y:2016:i:c:p:255-265
    DOI: 10.1016/j.tra.2016.09.017
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    1. de Palma, Andre & Picard, Nathalie & Waddell, Paul, 2007. "Discrete choice models with capacity constraints: An empirical analysis of the housing market of the greater Paris region," Journal of Urban Economics, Elsevier, vol. 62(2), pages 204-230, September.
    2. Liang Ma & Jennifer Dill & Cynthia Mohr, 2014. "The objective versus the perceived environment: what matters for bicycling?," Transportation, Springer, vol. 41(6), pages 1135-1152, November.
    3. Schneider, Robert J. & Arnold, Lindsay S. & Ragland, David R., 2009. "A Pilot Model for Estimating Pedestrian Intersection Crossing Volumes," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt3nr8h66j, Institute of Transportation Studies, UC Berkeley.
    4. Anas, Alex, 1983. "Discrete choice theory, information theory and the multinomial logit and gravity models," Transportation Research Part B: Methodological, Elsevier, vol. 17(1), pages 13-23, February.
    5. Schneider, Robert J., 2013. "Theory of routine mode choice decisions: An operational framework to increase sustainable transportation," Transport Policy, Elsevier, vol. 25(C), pages 128-137.
    6. Khan, Mobashwir & M. Kockelman, Kara & Xiong, Xiaoxia, 2014. "Models for anticipating non-motorized travel choices, and the role of the built environment," Transport Policy, Elsevier, vol. 35(C), pages 117-126.
    7. Lemp, Jason D. & Kockelman, Kara M., 2012. "Strategic sampling for large choice sets in estimation and application," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(3), pages 602-613.
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