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Pilot Models for Estimating Bicycle Intersection Volumes

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  • Griswold, Julia B.
  • Medury, Aditya
  • Schneider, Robert J.

Abstract

Bicycle volume data are useful to practitioners and researchers to understand safety, travel behavior, and development impacts. This paper describes the methodology used to develop several simple models of bicycle intersection volumes in Alameda County, California. The models are based on two-hour bicycle counts performed at a sample of 81 intersections in the Spring of 2008 and 2009. Study sites represented areas with a wide range of population density, employment density, proximity to commercial property, neighborhood income, and street network characteristics. The explanatory variables considered for the models included intersection site, land use, transportation system, and socioeconomic characteristics of the areas surrounding each intersection. Four alternative models are presented with adjusted R-square values ranging from 0.39 to 0.60. The models showed that bicycle volumes tended to be higher at intersections surrounded by more commercial retail properties within 1/10 mile, closer to a major university, with a marked bicycle facility on at least one leg of the intersection, surrounded by less hilly terrain within 1/2 mile, and surrounded by a more connected roadway network. The models also showed several important differences between weekday and weekend intersection volumes. The positive association between bicycle volume and proximity to retail or a large university was greater on weekdays than weekends, while bicycle facilities had a stronger positive association and hilly terrain had a weaker negative association with bicycle volume on weekends than weekdays. Further testing and refinement is necessary before accurate count predictions can be made in Alameda County or other communities.

Suggested Citation

  • Griswold, Julia B. & Medury, Aditya & Schneider, Robert J., 2011. "Pilot Models for Estimating Bicycle Intersection Volumes," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt380855q6, Institute of Transportation Studies, UC Berkeley.
  • Handle: RePEc:cdl:itsrrp:qt380855q6
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    1. Md Mintu Miah & Kate Kyung Hyun & Stephen P. Mattingly & Hannan Khan, 2023. "Estimation of daily bicycle traffic using machine and deep learning techniques," Transportation, Springer, vol. 50(5), pages 1631-1684, October.
    2. 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.
    3. Desmond Lartey & Meredith A. Glaser, 2024. "Towards a Sustainable Transport System: Exploring Capacity Building for Active Travel in Africa," Sustainability, MDPI, vol. 16(3), pages 1-20, February.
    4. Hochmair, Hartwig H. & Bardin, Eric & Ahmouda, Ahmed, 2019. "Estimating bicycle trip volume for Miami-Dade county from Strava tracking data," Journal of Transport Geography, Elsevier, vol. 75(C), pages 58-69.
    5. 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.
    6. Senes, Giulio & Rovelli, Roberto & Bertoni, Danilo & Arata, Laura & Fumagalli, Natalia & Toccolini, Alessandro, 2017. "Factors influencing greenways use: Definition of a method for estimation in the Italian context," Journal of Transport Geography, Elsevier, vol. 65(C), pages 175-187.
    7. Ahmed Jaber & János Juhász & Bálint Csonka, 2021. "An Analysis of Factors Affecting the Severity of Cycling Crashes Using Binary Regression Model," Sustainability, MDPI, vol. 13(12), pages 1-12, June.
    8. Salon, Deborah, 2016. "Estimating pedestrian and cyclist activity at the neighborhood scale," Journal of Transport Geography, Elsevier, vol. 55(C), pages 11-21.
    9. Mina Lee & Joseph Y. J. Chow & Gyugeun Yoon & Brian Yueshuai He, 2019. "Forecasting e-scooter substitution of direct and access trips by mode and distance," Papers 1908.08127, arXiv.org, revised Apr 2021.
    10. Jestico, Ben & Nelson, Trisalyn & Winters, Meghan, 2016. "Mapping ridership using crowdsourced cycling data," Journal of Transport Geography, Elsevier, vol. 52(C), pages 90-97.
    11. Munira, Sirajum & Sener, Ipek N., 2020. "A geographically weighted regression model to examine the spatial variation of the socioeconomic and land-use factors associated with Strava bike activity in Austin, Texas," Journal of Transport Geography, Elsevier, vol. 88(C).
    12. Osama, Ahmed & Sayed, Tarek & Bigazzi, Alexander Y., 2017. "Models for estimating zone-level bike kilometers traveled using bike network, land use, and road facility variables," Transportation Research Part A: Policy and Practice, Elsevier, vol. 96(C), pages 14-28.
    13. 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.
    14. Alattar, Mohammad Anwar & Cottrill, Caitlin & Beecroft, Mark, 2021. "Public participation geographic information system (PPGIS) as a method for active travel data acquisition," Journal of Transport Geography, Elsevier, vol. 96(C).

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    Keywords

    Engineering; safeTREC;

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