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Severity Analysis of Hazardous Material Road Transportation Crashes with a Bayesian Network Using Highway Safety Information System Data

Author

Listed:
  • Ming Sun

    (Road Safety Research Center, Research Institute of Highway Ministry of Transport, Beijing 100088, China)

  • Ronggui Zhou

    (Road Safety Research Center, Research Institute of Highway Ministry of Transport, Beijing 100088, China)

  • Chengwu Jiao

    (Road Safety Research Center, Research Institute of Highway Ministry of Transport, Beijing 100088, China)

  • Xiaoduan Sun

    (Department of Civil Engineering, University of Louisiana at Lafayette, Lafayette, LA 70504, USA)

Abstract

Although crashes involving hazardous materials (HAZMAT) are rare events compared with other types of traffic crashes, they often cause tremendous loss of life and property, as well as severe hazards to the environment and public safety. Using five-year (2013–2017) crash data (N = 1610) from the Highway Safety Information System database, a two-step machine learning-based approach was proposed to investigate and quantify the statistical relationship between three HAZMAT crash severity outcomes (fatal and severe injury, injury, and no injury) and contributing factors, including the driver, road, vehicle, crash, and environmental characteristics. Random forest ranked the importance of risk factors, and then Bayesian networks were developed to provide probabilistic inference. The results show that fatal and severe HAZMAT crashes are closely associated with younger drivers (age less than 25), driver fatigue, violation, distraction, special roadway locations (such as intersections, ramps, and bridges), higher speed limits (over 66 mph), midnight and early morning (12:00–5:59 a.m.), head-on crashes, more than four vehicles, fire/explosion/spill, poor lighting conditions, and adverse weather conditions. The overall prediction accuracy of 85.8% suggests the effectiveness of the proposed method. This study extends machine learning applications in a HAZMAT crash analysis, which would help develop targeted countermeasures and strategies to improve HAZMAT road transportation safety.

Suggested Citation

  • Ming Sun & Ronggui Zhou & Chengwu Jiao & Xiaoduan Sun, 2022. "Severity Analysis of Hazardous Material Road Transportation Crashes with a Bayesian Network Using Highway Safety Information System Data," IJERPH, MDPI, vol. 19(7), pages 1-22, March.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:7:p:4002-:d:781206
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    References listed on IDEAS

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    1. Yingying Xing & Shengdi Chen & Shengxue Zhu & Yi Zhang & Jian Lu, 2020. "Exploring Risk Factors Contributing to the Severity of Hazardous Material Transportation Accidents in China," IJERPH, MDPI, vol. 17(4), pages 1-19, February.
    2. Xiuguang Song & Jianqing Wu & Hongbo Zhang & Rendong Pi, 2020. "Analysis of Crash Severity for Hazard Material Transportation Using Highway Safety Information System Data," SAGE Open, , vol. 10(3), pages 21582440209, July.
    3. Li Zhou & Chun Guo & Yunxiao Cui & Jianjun Wu & Ying Lv & Zhiping Du, 2020. "Characteristics, Cause, and Severity Analysis for Hazmat Transportation Risk Management," IJERPH, MDPI, vol. 17(8), pages 1-24, April.
    4. Changxi Ma & Jibiao Zhou & Dong Yang, 2020. "Causation Analysis of Hazardous Material Road Transportation Accidents Based on the Ordered Logit Regression Model," IJERPH, MDPI, vol. 17(4), pages 1-25, February.
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    Cited by:

    1. Ming Sun & Ronggui Zhou, 2023. "Investigation on Hazardous Material Truck Involved Fatal Crashes Using Cluster Correspondence Analysis," Sustainability, MDPI, vol. 15(12), pages 1-21, June.

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