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Divergent Effects of Factors on Crash Severity under Autonomous and Conventional Driving Modes Using a Hierarchical Bayesian Approach

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  • Weixi Ren

    (Key Laboratory of Road and Traffic Engineering of the Ministry of Education, College of Transportation Engineering, Tongji University, 4800 Cao’an Highway, Shanghai 201804, China
    Engineering Research Center of Road Traffic Safety and Environment, Ministry of Education, Tongji University, 4800 Cao’an Highway, Shanghai 201800, China)

  • Bo Yu

    (Key Laboratory of Road and Traffic Engineering of the Ministry of Education, College of Transportation Engineering, Tongji University, 4800 Cao’an Highway, Shanghai 201804, China
    Engineering Research Center of Road Traffic Safety and Environment, Ministry of Education, Tongji University, 4800 Cao’an Highway, Shanghai 201800, China)

  • Yuren Chen

    (Key Laboratory of Road and Traffic Engineering of the Ministry of Education, College of Transportation Engineering, Tongji University, 4800 Cao’an Highway, Shanghai 201804, China
    Engineering Research Center of Road Traffic Safety and Environment, Ministry of Education, Tongji University, 4800 Cao’an Highway, Shanghai 201800, China)

  • Kun Gao

    (Department of Architecture and Civil Engineering, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden)

Abstract

Influencing factors on crash severity involved with autonomous vehicles (AVs) have been paid increasing attention. However, there is a lack of comparative analyses of those factors between AVs and human-driven vehicles. To fill this research gap, the study aims to explore the divergent effects of factors on crash severity under autonomous and conventional (i.e., human-driven) driving modes. This study obtained 180 publicly available autonomous vehicle crash data, and 39 explanatory variables were extracted from three categories, including environment, roads, and vehicles. Then, a hierarchical Bayesian approach was applied to analyze the impacting factors on crash severity (i.e., injury or no injury) under both driving modes with considering unobserved heterogeneities. The results showed that some influencing factors affected both driving modes, but their degrees were different. For example, daily visitors’ flowrate had a greater impact on the crash severity under the conventional driving mode. More influencing factors only had significant impacts on one of the driving modes. For example, in the autonomous driving mode, mixed land use increased the severity of crashes, while daytime had the opposite effects. This study could contribute to specifying more appropriate policies to reduce the crash severity of both autonomous and human-driven vehicles especially in mixed traffic conditions.

Suggested Citation

  • Weixi Ren & Bo Yu & Yuren Chen & Kun Gao, 2022. "Divergent Effects of Factors on Crash Severity under Autonomous and Conventional Driving Modes Using a Hierarchical Bayesian Approach," IJERPH, MDPI, vol. 19(18), pages 1-22, September.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:18:p:11358-:d:911260
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    References listed on IDEAS

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    1. Agnieszka Dudziak & Monika Stoma & Andrzej Kuranc & Jacek Caban, 2021. "Assessment of Social Acceptance for Autonomous Vehicles in Southeastern Poland," Energies, MDPI, vol. 14(18), pages 1-16, September.
    2. Assemi, Behrang & Hickman, Mark, 2018. "Relationship between heavy vehicle periodic inspections, crash contributing factors and crash severity," Transportation Research Part A: Policy and Practice, Elsevier, vol. 113(C), pages 441-459.
    3. Shen, Yu & Zhang, Hongmou & Zhao, Jinhua, 2018. "Integrating shared autonomous vehicle in public transportation system: A supply-side simulation of the first-mile service in Singapore," Transportation Research Part A: Policy and Practice, Elsevier, vol. 113(C), pages 125-136.
    4. Pan, Yingjiu & Chen, Shuyan & Li, Tiezhu & Niu, Shifeng & Tang, Kun, 2019. "Exploring spatial variation of the bus stop influence zone with multi-source data: A case study in Zhenjiang, China," Journal of Transport Geography, Elsevier, vol. 76(C), pages 166-177.
    5. Faes, C. & Ormerod, J. T. & Wand, M. P., 2011. "Variational Bayesian Inference for Parametric and Nonparametric Regression With Missing Data," Journal of the American Statistical Association, American Statistical Association, vol. 106(495), pages 959-971.
    6. Straub, Edward R. & Schaefer, Kristin E., 2019. "It takes two to Tango: Automated vehicles and human beings do the dance of driving – Four social considerations for policy," Transportation Research Part A: Policy and Practice, Elsevier, vol. 122(C), pages 173-183.
    7. Retting, R.A. & Kyrychenko, S.Y., 2002. "Reductions in injury crashes associated with red light camera enforcement in Oxnard, California," American Journal of Public Health, American Public Health Association, vol. 92(11), pages 1822-1825.
    8. Azim Shariff & Jean-François Bonnefon & Iyad Rahwan, 2017. "Psychological roadblocks to the adoption of self-driving vehicles," Nature Human Behaviour, Nature, vol. 1(10), pages 694-696, October.
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