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Modeling bicycle crash costs using big data: A grid-cell-based Tobit model with random parameters

Author

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  • Xie, Kun
  • Ozbay, Kaan
  • Yang, Di
  • Xu, Chuan
  • Yang, Hong

Abstract

Bicyclists are among the most vulnerable road users in the urban transportation system. It is critical to investigate the contributing factors to bicycle-related crashes and to identify the hotspots for efficient allocation of treatment resources. A grid-cell-based modeling framework was used to incorporate heterogeneous data sources and to explore the overall safety patterns of bicyclists in Manhattan, New York City. A random parameters (RP) Tobit model was developed in the Bayesian framework to correlate transportation, land use, and sociodemographic data with bicycle crash costs. It is worth mentioning that a new algorithm was proposed to estimate bicyclist-involved crash exposure using large-scale bicycle ridership data from 2014 to 2016 obtained from Citi Bike, which is the largest bicycle sharing program in the United States. The proposed RP Tobit model could deal with left-censored crash cost data and was found to outperform the Tobit model by accounting for the unobserved heterogeneity across neighborhoods. Results indicated that bicycle ridership, bicycle rack density, subway ridership, taxi trips, bus stop density, population, and ratio of population under 14 were positively associated with bicycle crash cost, whereas residential ratio and median age had a negative impact on bicycle crash cost. The RP Tobit model was used to estimate the cell-specific potential for safety improvement (PSI) for hotspot ranking. The advantages of using the RP Tobit crash cost model to capture PSI are that injury severity is considered by being converted into unit costs, and varying effects of certain explanatory variables are accounted for by incorporating random parameters. The cell-based hotspot identification method can provide a complete risk map for bicyclists with high resolution. Most locations with high PSIs either had unprotected bicycle lanes or were close to the access points to protected bicycle routes.

Suggested Citation

  • 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).
  • Handle: RePEc:eee:jotrge:v:91:y:2021:i:c:s0966692321000065
    DOI: 10.1016/j.jtrangeo.2021.102953
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    References listed on IDEAS

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    1. 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.
    2. Sturtz, Sibylle & Ligges, Uwe & Gelman, Andrew, 2005. "R2WinBUGS: A Package for Running WinBUGS from R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 12(i03).
    3. 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.
    4. Kun Xie & Kaan Ozbay & Abdullah Kurkcu & Hong Yang, 2017. "Analysis of Traffic Crashes Involving Pedestrians Using Big Data: Investigation of Contributing Factors and Identification of Hotspots," Risk Analysis, John Wiley & Sons, vol. 37(8), pages 1459-1476, August.
    5. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
    Full references (including those not matched with items on IDEAS)

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