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Evaluating the safety effects of bicycle lanes in New York City

Author

Listed:
  • Chen, L.
  • Chen, C.
  • Srinivasan, R.
  • McKnight, C.E.
  • Ewing, R.
  • Roe, M.

Abstract

Objectives: We evaluated the effects of on-street bicycle lanes installed prior to 2007 on different categories of crashes (total crashes, bicyclist crashes, pedestrian crashes, multiple-vehicle crashes, and injurious or fatal crashes) occurring on roadway segments and at intersections in New York City. Methods: We used generalized estimating equation methodology to compare changes in police-reported crashes in a treatment group and a comparison group before and after installation of bicycle lanes. Our study approach allowed us to control confounding factors, such as built environment characteristics, that cannot typically be controlled when a comparison group is used. Results: Installation of bicycle lanes did not lead to an increase in crashes, despite the probable increase in the number of bicyclists. The most likely explanations for the lack of increase in crashes are reduced vehicular speeds and fewer conflicts between vehicles and bicyclists after installation of these lanes. Conclusions: Our results indicate that characteristics of the built environment have a direct impact on crashes and that they should thus be controlled in studies evaluating traffic countermeasures such as bicycle lanes. To prevent crashes at intersections,we recommend installation of "bike boxes" and markings that indicate the path of bicycle lanes across intersections.

Suggested Citation

  • Chen, L. & Chen, C. & Srinivasan, R. & McKnight, C.E. & Ewing, R. & Roe, M., 2012. "Evaluating the safety effects of bicycle lanes in New York City," American Journal of Public Health, American Public Health Association, vol. 102(6), pages 1120-1127.
    • Handle: RePEc:aph:ajpbhl:10.2105/ajph.2011.300319_5
    DOI: 10.2105/AJPH.2011.300319
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    Cited by:

    1. Xie, Kun & Ozbay, Kaan & Yang, Di & Xu, Chuan & Yang, Hong, 2021. "Modeling bicycle crash costs using big data: A grid-cell-based Tobit model with random parameters," Journal of Transport Geography, Elsevier, vol. 91(C).
    2. Luís Rita & Miguel Peliteiro & Tudor-Codrin Bostan & Tiago Tamagusko & Adelino Ferreira, 2023. "Using Deep Learning and Google Street View Imagery to Assess and Improve Cyclist Safety in London," Sustainability, MDPI, vol. 15(13), pages 1-26, June.
    3. Hwachyi Wang & S. K. Jason Chang & Hans De Backer & Dirk Lauwers & Philippe De Maeyer, 2019. "Integrating Spatial and Temporal Approaches for Explaining Bicycle Crashes in High-Risk Areas in Antwerp (Belgium)," Sustainability, MDPI, vol. 11(13), pages 1-28, July.
    4. Federico Rupi & Kevin J. Krizek, 2019. "Visual Eye Gaze While Cycling: Analyzing Eye Tracking at Signalized Intersections in Urban Conditions," Sustainability, MDPI, vol. 11(21), pages 1-14, November.
    5. Brown, Anne E., 2017. "Car-less or car-free? Socioeconomic and mobility differences among zero-car households," Transport Policy, Elsevier, vol. 60(C), pages 152-159.
    6. Gabriele Prati & Víctor Marín Puchades & Marco De Angelis & Federico Fraboni & Luca Pietrantoni, 2018. "Factors contributing to bicycle–motorised vehicle collisions: a systematic literature review," Transport Reviews, Taylor & Francis Journals, vol. 38(2), pages 184-208, March.
    7. Mohamed Bayoumi Kamel & Tarek Sayed, 2021. "The impact of bike network indicators on bike kilometers traveled and bike safety: A network theory approach," Environment and Planning B, , vol. 48(7), pages 2055-2072, September.
    8. Wang, Hwachyi & De Backer, Hans & Lauwers, Dirk & Chang, S.K.Jason, 2019. "A spatio-temporal mapping to assess bicycle collision risks on high-risk areas (Bridges) - A case study from Taipei (Taiwan)," Journal of Transport Geography, Elsevier, vol. 75(C), pages 94-109.
    9. Shin, Eun Jin, 2023. "Decomposing neighborhood disparities in bicycle crashes: A Gelbach decomposition analysis," Transport Policy, Elsevier, vol. 131(C), pages 156-172.
    10. Marcel E Moran, 2021. "What’s your angle? Analyzing angled parking via satellite imagery to aid bike-network planning," Environment and Planning B, , vol. 48(7), pages 1912-1925, September.
    11. Kyriakos Ketikidis & Apostolos Papagiannakis & Socrates Basbas, 2023. "Identifying and Modeling the Factors That Affect Bicycle Users’ Satisfaction," Sustainability, MDPI, vol. 15(18), pages 1-20, September.
    12. Sanders, Rebecca L, 2013. "Examining the Cycle: How Perceived and Actual Bicycling Risk Influence Cylcing Frequency, Roadway Design Preferences, and Support for Cycling Among Bay Area Residents," University of California Transportation Center, Working Papers qt1tf5v738, University of California Transportation Center.
    13. Sanders, Rebecca Lauren, 2013. "Examining the Cycle: How Perceived and Actual Bicycling Risk Influence Cycling Frequency, Roadway Design Preferences, and Support for Cycling Among Bay Area Residents," University of California Transportation Center, Working Papers qt6ct7x8hp, University of California Transportation Center.
    14. Anne C. Lusk & Walter C. Willett & Vivien Morris & Christopher Byner & Yanping Li, 2019. "Bicycle Facilities Safest from Crime and Crashes: Perceptions of Residents Familiar with Higher Crime/Lower Income Neighborhoods in Boston," IJERPH, MDPI, vol. 16(3), pages 1-21, February.

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