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Do road network patterns and points of interest influence bicycle safety? Evidence from dockless bike sharing in China and policy implications for traffic safety planning

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  • Li, Jia
  • Li, Chengqian
  • Zhao, Xiaohua
  • Wang, Xuesong

Abstract

Dockless bike sharing, also known as the shared bicycle industry, is booming, especially in China. Since cyclists are more vulnerable than motor vehicle drivers in traffic crashes, it is necessary to investigate shared bicycle traffic safety. Road network patterns and the proportion of different points of interest (POIs) are two critical macro-level factors influencing bicycle crashes. Therefore, it is necessary to consider bicycle traffic safety in road networks and land use policy in road traffic planning. This study investigated risk exposure, demographic data, proportion of different POIs, land use, road network features, and bicycle crashes in 124 census tracts in Beijing's Sixth Ring Road area. The betweenness centrality was calculated for the census tracts to classify the road network patterns. A negative binomial conditional autoregressive (NB-CAR) model was developed for bicycle total crashes, and bivariate negative binomial CAR (BNB-CAR) models were developed for bicycle single-vehicle (SV) and multi-vehicle (MV) crashes, property damage only (PDO) and injury crashes. The results show the following. 1) The BNB-CAR model had a better fit than the NB-CAR model. 2) The census tracts with parallel, mixed, and loops & lollipops patterns were associated with higher bicycle crash frequency than those with a grid pattern. The difference in the bicycle SV crash frequency between the mixed and loop & lollipop patterns was larger than that in the bicycle MV crash frequency. 3) Census tracts with higher proportions of POIs for subway and bus stations (T-POI) were associated with fewer bicycle crashes. 4) Census tracts with higher arterial proportions were associated with more injury crashes. This study provides a theoretical basis for formulating road network and land-use policies to ensure road traffic safety.

Suggested Citation

  • Li, Jia & Li, Chengqian & Zhao, Xiaohua & Wang, Xuesong, 2024. "Do road network patterns and points of interest influence bicycle safety? Evidence from dockless bike sharing in China and policy implications for traffic safety planning," Transport Policy, Elsevier, vol. 149(C), pages 21-35.
    • Handle: RePEc:eee:trapol:v:149:y:2024:i:c:p:21-35
    DOI: 10.1016/j.tranpol.2024.01.021
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    1. J. Buhl & J. Gautrais & R. Solé & P. Kuntz & S. Valverde & J. Deneubourg & G. Theraulaz, 2004. "Efficiency and robustness in ant networks of galleries," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 42(1), pages 123-129, November.
    2. 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).
    3. Ma, Xinwei & Ji, Yanjie & Yuan, Yufei & Van Oort, Niels & Jin, Yuchuan & Hoogendoorn, Serge, 2020. "A comparison in travel patterns and determinants of user demand between docked and dockless bike-sharing systems using multi-sourced data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 139(C), pages 148-173.
    4. Julian Besag & Jeremy York & Annie Mollié, 1991. "Bayesian image restoration, with two applications in spatial statistics," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 43(1), pages 1-20, March.
    5. 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.
    6. Shin, Eun Jin, 2023. "Decomposing neighborhood disparities in bicycle crashes: A Gelbach decomposition analysis," Transport Policy, Elsevier, vol. 131(C), pages 156-172.
    7. Yan, Ying & Zhang, Ying & Yang, Xiangli & Hu, Jin & Tang, Jinjun & Guo, Zhongyin, 2020. "Crash prediction based on random effect negative binomial model considering data heterogeneity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 547(C).
    8. VANDENBULCKE, Grégory & THOMAS, Isabelle & INT PANIS, Luc, 2014. "Predicting cycling accident risk in Brussels: a spatial case-control approach," LIDAM Reprints CORE 2535, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    9. Hou, Qinzhong & Meng, Xianghai & Leng, Junqiang & Yu, Lu, 2018. "Application of a random effects negative binomial model to examine crash frequency for freeways in China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 937-944.
    10. Manfred M. Fischer & Peter Nijkamp (ed.), 2014. "Handbook of Regional Science," Springer Books, Springer, edition 127, number 978-3-642-23430-9, October.
    11. Xing, Yingying & Wang, Ke & Lu, Jian John, 2020. "Exploring travel patterns and trip purposes of dockless bike-sharing by analyzing massive bike-sharing data in Shanghai, China," Journal of Transport Geography, Elsevier, vol. 87(C).
    12. Lord, Dominique & Mannering, Fred, 2010. "The statistical analysis of crash-frequency data: A review and assessment of methodological alternatives," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(5), pages 291-305, June.
    13. Smith, Brian J., 2007. "boa: An R Package for MCMC Output Convergence Assessment and Posterior Inference," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 21(i11).
    14. Wang, Xuesong & You, Shikai & Wang, Ling, 2017. "Classifying road network patterns using multinomial logit model," Journal of Transport Geography, Elsevier, vol. 58(C), pages 104-112.
    15. Jie Bao & Chengcheng Xu & Pan Liu & Wei Wang, 2017. "Exploring Bikesharing Travel Patterns and Trip Purposes Using Smart Card Data and Online Point of Interests," Networks and Spatial Economics, Springer, vol. 17(4), pages 1231-1253, December.
    16. Shakil Rifaat & Richard Tay & Alexandre de Barros, 2012. "Urban Street Pattern and Pedestrian Traffic Safety," Journal of Urban Design, Taylor & Francis Journals, vol. 17(3), pages 337-352.
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