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A Pilot Model for Estimating Pedestrian Intersection Crossing Volumes

Author

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  • Schneider, Robert J.
  • Arnold, Lindsay S.
  • Ragland, David R.

Abstract

Better data on pedestrian volumes are needed to improve the safety, comfort, and convenience of pedestrian movement. This requires more carefully-developed methodologies for counting pedestrians as well as improved methods of modeling pedestrian volumes. This paper describes the methodology used to create a simple, pilot model of pedestrian intersection crossing volumes in Alameda County, CA. The model is based on weekly pedestrian volumes at a sample of 50 intersections with a wide variety of surrounding land uses, transportation system attributes, and neighborhood socioeconomic characteristics. Three alternative model structures were considered, and the final recommended model has a good overall fit (adjusted-R2=0.897). Statistically-significant factors in the model include the total population within a 0.5-mile radius, employment within a 0.25-mile radius, number of commercial retail properties within a 0.25- mile radius, and the presence of a regional transit station within a 0.1-mile radius of an intersection. The model has a simple structure, and it can be implemented by practitioners using geographic information systems and a basic spreadsheet program. Since the study is based on a relatively small number of intersections in one urban area, additional research is needed to refine the model and determine its applicability in other areas.

Suggested Citation

  • 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.
    • Handle: RePEc:cdl:itsrrp:qt3nr8h66j
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    Cited by:

    1. Medury, Aditya PhD & Vlachogiannis, Dimitris & Grembek, Offer PhD, 2020. "Assessing the Variation of Curbside Safety at the City Block Level," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt46n9669d, Institute of Transportation Studies, UC Berkeley.
    2. Zhang, Yuanyuan & Bigham, John & Ragland, David & Chen, Xiaohong, 2015. "Investigating the associations between road network structure and non-motorist accidents," Journal of Transport Geography, Elsevier, vol. 42(C), pages 34-47.
    3. Strauss, Jillian & Miranda-Moreno, Luis F., 2013. "Spatial modeling of bicycle activity at signalized intersections," The Journal of Transport and Land Use, Center for Transportation Studies, University of Minnesota, vol. 6(2), pages 47-58.
    4. 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.
    5. 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).
    6. 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.
    7. Pfiester, Laura Mali & Thompson, Russell G. & Zhang, Lele, 2021. "Spatiotemporal exploration of Melbourne pedestrian demand," Journal of Transport Geography, Elsevier, vol. 95(C).
    8. 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.
    9. 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.
    10. Qin Zhang & Rolf Moeckel & Kelly J. Clifton, 2024. "MoPeD meets MITO: a hybrid modeling framework for pedestrian travel demand," Transportation, Springer, vol. 51(4), pages 1327-1347, August.
    11. Sohrabi, Soheil & Paleti, Rajesh & Balan, Lacramioara & Cetin, Mecit, 2020. "Real-time prediction of public bike sharing system demand using generalized extreme value count model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 133(C), pages 325-336.
    12. Yang, Hongtai & Lu, Xiaozhao & Cherry, Christopher & Liu, Xiaohan & Li, Yanlai, 2017. "Spatial variations in active mode trip volume at intersections: a local analysis utilizing geographically weighted regression," Journal of Transport Geography, Elsevier, vol. 64(C), pages 184-194.
    13. 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.
    14. Ryus, Paul & Ferguson, Erin & Laustsen, Kelly M. & Schneider, Robert J. & Proulx, Frank R. & Hull, Tony & Miranda-Moreno, Luis, 2014. "Guidebook on Pedestrian and Bicycle Volume Data Collection," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt11q5p33w, Institute of Transportation Studies, UC Berkeley.
    15. Srinivas S. Pulugurtha & L. Prasanna Srirangam, 2022. "Pedestrian safety at intersections near light rail transit stations," Public Transport, Springer, vol. 14(3), pages 583-608, October.

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    Keywords

    Engineering; safeTREC;

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