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A behavioral modeling approach to bicycle level of service

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  • Griswold, Julia B.
  • Yu, Mengqiao
  • Filingeri, Victoria
  • Grembek, Offer
  • Walker, Joan L.

Abstract

Bicycle level of service (LOS) measures are essential tools for transportation agencies to monitor and prioritize improvements to infrastructure for cyclists. While it is apparent that different types of cyclists have varying preferences for the facilities on which they ride, in current research and practice, measures are used that are either insufficiently quantitative and empirical or lack cyclist segmentation. In this study, we conducted a detailed survey on cyclist habits, preferences, and user experience, capturing responses to videos of a bicycle traveling on road segments in the San Francisco Bay Area. The survey provided rich behavioral data, which invited both quantitative and qualitative exploration. We compared facility preferences from the survey to scores from two common measures, NCHRP bicycle level of service (NCHRP BLOS), and level of traffic stress (LTS); and we examined the responses to open-ended questions to gain insights about heterogeneity of preferences among cyclists. Finally, we applied behavioral analysis tools as a proof of concept for a new bicycle level of service measure that accounts for the segmentation of cyclist types via a latent class choice model. Combining statistics and behavioral analysis, we can improve the quality of bicycle level of service measures to make decisions driven by empirically measured cyclist preferences.

Suggested Citation

  • Griswold, Julia B. & Yu, Mengqiao & Filingeri, Victoria & Grembek, Offer & Walker, Joan L., 2018. "A behavioral modeling approach to bicycle level of service," Transportation Research Part A: Policy and Practice, Elsevier, vol. 116(C), pages 166-177.
    • Handle: RePEc:eee:transa:v:116:y:2018:i:c:p:166-177
    DOI: 10.1016/j.tra.2018.06.006
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    References listed on IDEAS

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    1. Joan Walker & Jieping Li, 2007. "Latent lifestyle preferences and household location decisions," Journal of Geographical Systems, Springer, vol. 9(1), pages 77-101, April.
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    Cited by:

    1. 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.
    2. Alvaro Rodriguez-Valencia & Jose Agustin Vallejo-Borda & German A. Barrero & Hernan Alberto Ortiz-Ramirez, 2022. "Towards an enriched framework of service evaluation for pedestrian and bicyclist infrastructure: acknowledging the power of users’ perceptions," Transportation, Springer, vol. 49(3), pages 791-814, June.
    3. Kim, Sung Hoo & Mokhtarian, Patricia L., 2023. "Finite mixture (or latent class) modeling in transportation: Trends, usage, potential, and future directions," Transportation Research Part B: Methodological, Elsevier, vol. 172(C), pages 134-173.
    4. Zhu, Siying & Zhu, Feng, 2019. "Cycling comfort evaluation with instrumented probe bicycle," Transportation Research Part A: Policy and Practice, Elsevier, vol. 129(C), pages 217-231.
    5. Qiyao Yang & Jun Cai & Tao Feng & Zhengying Liu & Harry Timmermans, 2021. "Bikeway Provision and Bicycle Commuting: City-Level Empirical Findings from the US," Sustainability, MDPI, vol. 13(6), pages 1-15, March.
    6. Fitch, Dillon & Carlen, Jane & Handy, Susan, 2020. "Making Bicycling Comfortable: Identifying Minimum Infrastructure Needs by Population Segments Using a Video Survey," Institute of Transportation Studies, Working Paper Series qt7jn8h79x, Institute of Transportation Studies, UC Davis.
    7. Wei Wang & Zhentian Sun & Liya Wang & Shanshan Yu & Jun Chen, 2020. "Evaluation Model for the Level of Service of Shared-Use Paths Based on Traffic Conflicts," Sustainability, MDPI, vol. 12(18), pages 1-19, September.
    8. Fitch, Dillon T. & Carlen, Jane & Handy, Susan L., 2022. "What makes bicyclists comfortable? Insights from a visual preference survey of casual and prospective bicyclists," Transportation Research Part A: Policy and Practice, Elsevier, vol. 155(C), pages 434-449.
    9. Khashayar Kazemzadeh & Aliaksei Laureshyn & Lena Winslott Hiselius & Enrico Ronchi, 2020. "Expanding the Scope of the Bicycle Level-of-Service Concept: A Review of the Literature," Sustainability, MDPI, vol. 12(7), pages 1-30, April.
    10. Cabral, Laura & Kim, Amy M., 2020. "An empirical reappraisal of the four types of cyclists," Transportation Research Part A: Policy and Practice, Elsevier, vol. 137(C), pages 206-221.
    11. Xiaofei Ye & Yi Zhu & Tao Wang & Xingchen Yan & Jun Chen & Bin Ran, 2022. "Level of Service Model of the Non-Motorized Vehicle Crossing the Signalized Intersection Based on Riders’ Perception Data," IJERPH, MDPI, vol. 19(8), pages 1-17, April.
    12. Liang, Xiao & Zhang, Tianyu & Xie, Meiquan & Jia, Xudong, 2021. "Analyzing bicycle level of service using virtual reality and deep learning technologies," Transportation Research Part A: Policy and Practice, Elsevier, vol. 153(C), pages 115-129.
    13. Gabriella Mazzulla & Maria Grazia Bellizzi & Laura Eboli & Carmen Forciniti, 2021. "Cycling for a Sustainable Touristic Mobility: A Preliminary Study in an Urban Area of Italy," IJERPH, MDPI, vol. 18(24), pages 1-12, December.
    14. S. Van Cranenburgh & S. Wang & A. Vij & F. Pereira & J. Walker, 2021. "Choice modelling in the age of machine learning -- discussion paper," Papers 2101.11948, arXiv.org, revised Nov 2021.

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