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Investigation on Hazardous Material Truck Involved Fatal Crashes Using Cluster Correspondence Analysis

Author

Listed:
  • Ming Sun

    (Road Safety Research Center, Research Institute of Highway Ministry of Transport, No. 8 Xitucheng Road, Haidian District, Beijing 100088, China)

  • Ronggui Zhou

    (Road Safety Research Center, Research Institute of Highway Ministry of Transport, No. 8 Xitucheng Road, Haidian District, Beijing 100088, China)

Abstract

Although hazardous material (HAZMAT) truck-involved crashes are uncommon compared to other types of traffic crashes, these crashes pose considerable threats to the public, property, and environment due to the unique feature of low probability with high consequences. Using ten-year (2010–2019) crash data from the Fatality Analysis Reporting System (FARS) database, this study applies cluster correspondence analysis to identify the underlying patterns and the associations between the risk factors for HAZMAT-truck-involved fatal crashes. A low-dimensional space projects the categorical variables (including the crash, road, driver, vehicle, and environmental characteristics) into different clusters based on the optimal clustering validation criterion. This study reveals that fatal HAZMAT-truck-involved crashes are highly distinguishable concerning collision types (angle and front-to-front crashes, single-vehicle crashes, and front-to-end crashes) and roadway geometric variables, such as two-way undivided roadways, curve alignments, and high-speed (65 mph or more) urban interstate highways. Driver behavior (distraction, asleep or fatigue, and physical impairment), lighting conditions (dark–lighted and dark–not lighted), and adverse weather are also interrelated. The findings from this study will help HAZMAT carriers, transportation management authorities, and policymakers develop potential targeted countermeasures for HAZMAT-truck-involved crash reduction and safety improvement.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:12:p:9369-:d:1167906
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    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. van de Velden, M. & Iodice D' Enza, A. & Palumbo, F., 2014. "Cluster Correspondence Analysis," Econometric Institute Research Papers EI 2014-24, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    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.
    5. 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.
    6. 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.
    Full references (including those not matched with items on IDEAS)

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