IDEAS home Printed from https://ideas.repec.org/a/eee/transa/v94y2016icp255-265.html
   My bibliography  Save this article

Development of destination choice models for pedestrian travel

Author

Listed:
  • Clifton, Kelly J.
  • Singleton, Patrick A.
  • Muhs, Christopher D.
  • Schneider, Robert J.

Abstract

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.

Suggested Citation

  • Clifton, Kelly J. & Singleton, Patrick A. & Muhs, Christopher D. & Schneider, Robert J., 2016. "Development of destination choice models for pedestrian travel," Transportation Research Part A: Policy and Practice, Elsevier, vol. 94(C), pages 255-265.
    • Handle: RePEc:eee:transa:v:94:y:2016:i:c:p:255-265
    DOI: 10.1016/j.tra.2016.09.017
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0965856416308412
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.tra.2016.09.017?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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. Reid Ewing & Robert Cervero, 2010. "Travel and the Built Environment," Journal of the American Planning Association, Taylor & Francis Journals, vol. 76(3), pages 265-294.
    3. 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.
    4. Clifton, Kelly J. & Singleton, Patrick A. & Muhs, Christopher D. & Schneider, Robert J., 2016. "Representing pedestrian activity in travel demand models: Framework and application," Journal of Transport Geography, Elsevier, vol. 52(C), pages 111-122.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Bojing Liao & Pauline E. W. van den Berg & Pieter J. V. van Wesemael & Theo A. Arentze, 2020. "How Does Walkability Change Behavior? A Comparison between Different Age Groups in the Netherlands," IJERPH, MDPI, vol. 17(2), pages 1-14, January.
    2. Clifton, Kelly J. & Singleton, Patrick A. & Muhs, Christopher D. & Schneider, Robert J., 2016. "Representing pedestrian activity in travel demand models: Framework and application," Journal of Transport Geography, Elsevier, vol. 52(C), pages 111-122.
    3. Berjisian, Elmira & Habibian, Meeghat, 2019. "Developing a pedestrian destination choice model using the stratified importance sampling method," Journal of Transport Geography, Elsevier, vol. 77(C), pages 39-47.
    4. Mingzhu Song & Kaiping Wang & Yi Zhang & Meng Li & He Qi & Yi Zhang, 2020. "Impact Evaluation of Bike-Sharing on Bicycling Accessibility," Sustainability, MDPI, vol. 12(15), pages 1-16, July.
    5. Gehrke, Steven R. & Wang, Liming, 2020. "Operationalizing the neighborhood effects of the built environment on travel behavior," Journal of Transport Geography, Elsevier, vol. 82(C).
    6. Gen Hayauchi & Ryo Ariyoshi & Takayuki Morikawa & Fumihiko Nakamura, 2023. "Assessment of the Improvement of Public Transport in Hillside Cities Considering the Impact of Topography on Walking Choices," Sustainability, MDPI, vol. 15(12), pages 1-12, June.
    7. Raoul S. Liévanos & Amy Lubitow & Julius Alexander McGee, 2019. "Misrecognition in a Sustainability Capital: Race, Representation, and Transportation Survey Response Rates in the Portland Metropolitan Area," Sustainability, MDPI, vol. 11(16), pages 1-33, August.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Berjisian, Elmira & Habibian, Meeghat, 2019. "Developing a pedestrian destination choice model using the stratified importance sampling method," Journal of Transport Geography, Elsevier, vol. 77(C), pages 39-47.
    2. Keunhyun Park & Dong-Ah Choi & Guang Tian & Reid Ewing, 2019. "Not Parking Lots but Parks: A Joint Association of Parks and Transit Stations with Travel Behavior," IJERPH, MDPI, vol. 16(4), pages 1-9, February.
    3. Gustav Bösehans & Ian Walker, 2020. "Do supra-modal traveller types exist? A travel behaviour market segmentation using Goal framing theory," Transportation, Springer, vol. 47(1), pages 243-273, February.
    4. Clifton, Kelly J. & Singleton, Patrick A. & Muhs, Christopher D. & Schneider, Robert J., 2016. "Representing pedestrian activity in travel demand models: Framework and application," Journal of Transport Geography, Elsevier, vol. 52(C), pages 111-122.
    5. Ding, Chuan & Wang, Donggen & Liu, Chao & Zhang, Yi & Yang, Jiawen, 2017. "Exploring the influence of built environment on travel mode choice considering the mediating effects of car ownership and travel distance," Transportation Research Part A: Policy and Practice, Elsevier, vol. 100(C), pages 65-80.
    6. Singleton, Patrick A. & Park, Keunhyun & Lee, Doo Hong, 2021. "Varying influences of the built environment on daily and hourly pedestrian crossing volumes at signalized intersections estimated from traffic signal controller event data," Journal of Transport Geography, Elsevier, vol. 93(C).
    7. Boukarta Soufiane & Berezowska-Azzag Ewa, 2020. "Exploring the Role of Socio-Economic and Built Environment Driving Factors in Shaping the Commuting Modal Share: A Path-Analysis-Based Approach," Quaestiones Geographicae, Sciendo, vol. 39(4), pages 87-107, December.
    8. Juwon Chung & Seung-Nam Kim & Hyungkyoo Kim, 2019. "The Impact of PM 10 Levels on Pedestrian Volume: Findings from Streets in Seoul, South Korea," IJERPH, MDPI, vol. 16(23), pages 1-23, December.
    9. Prato, Carlo G. & Kaplan, Sigal & Patrier, Alexandre & Rasmussen, Thomas K., 2019. "Integrating police reports with geographic information system resources for uncovering patterns of pedestrian crashes in Denmark," Journal of Transport Geography, Elsevier, vol. 74(C), pages 10-23.
    10. Arefeh Nasri & Lei Zhang, 2019. "How Urban Form Characteristics at Both Trip Ends Influence Mode Choice: Evidence from TOD vs. Non-TOD Zones of the Washington, D.C. Metropolitan Area," Sustainability, MDPI, vol. 11(12), pages 1-16, June.
    11. Arellana, Julián & Saltarín, María & Larrañaga, Ana Margarita & González, Virginia I. & Henao, César Augusto, 2020. "Developing an urban bikeability index for different types of cyclists as a tool to prioritise bicycle infrastructure investments," Transportation Research Part A: Policy and Practice, Elsevier, vol. 139(C), pages 310-334.
    12. Nichols, Brice G. & Kockelman, Kara M., 2014. "Life-cycle energy implications of different residential settings: Recognizing buildings, travel, and public infrastructure," Energy Policy, Elsevier, vol. 68(C), pages 232-242.
    13. Wang, Xize & Lindsey, Greg & Hankey, Steve & Hoff, Kris, 2014. "Estimating Mixed-Mode Urban Trail Traffic Using Negative Binomial Regression Models," SocArXiv evpfq, Center for Open Science.
    14. Umer Mansoor & Mohammad Tamim Kashifi & Fazal Rehman Safi & Syed Masiur Rahman, 2022. "A review of factors and benefits of non-motorized transport: a way forward for developing countries," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(2), pages 1560-1582, February.
    15. Boukarta Soufiane & Berezowska-Azzag Ewa, 2022. "The Influence of Built Environment and Socio-Economic Factors on Commuting Energy Demand: A Path Analysis-Based Approach," Quaestiones Geographicae, Sciendo, vol. 41(4), pages 19-39, December.
    16. Vincent Viguié, 2015. "Cross-commuting and housing prices in a polycentric modeling of cities," Working Papers 2015.09, FAERE - French Association of Environmental and Resource Economists.
    17. Zuo, Ting & Wei, Heng & Liu, Hao & Yang, Y. Jeffrey, 2019. "Bi-level optimization approach for configuring population and employment distributions with minimized vehicle travel demand," Journal of Transport Geography, Elsevier, vol. 74(C), pages 161-172.
    18. Chakrabarti, Sandip, 2017. "How can public transit get people out of their cars? An analysis of transit mode choice for commute trips in Los Angeles," Transport Policy, Elsevier, vol. 54(C), pages 80-89.
    19. Liu, Jixiang & Wang, Bo & Xiao, Longzhu, 2021. "Non-linear associations between built environment and active travel for working and shopping: An extreme gradient boosting approach," Journal of Transport Geography, Elsevier, vol. 92(C).
    20. Sun, Bindong & Liu, Jiahang & Yin, Chun & Cao, Jason, 2022. "Residential and workplace neighborhood environments and life satisfaction: Exploring chain-mediation effects of activity and place satisfaction," Journal of Transport Geography, Elsevier, vol. 104(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:transa:v:94:y:2016:i:c:p:255-265. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/547/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.