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Exploring Risk Factors Contributing to the Severity of Hazardous Material Transportation Accidents in China

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  • Yingying Xing

    (College of Transportation Engineering, Tongji University, Key Laboratory of Road and Traffic Engineering of the State Ministry of Education, Shanghai Key Laboratory of Rail Infrastructure Durability and System Safety, Shanghai 201804, China)

  • Shengdi Chen

    (School of Transport & Communications, Shanghai Maritime University, 1550 Haigang Street, Shanghai 201306, China)

  • Shengxue Zhu

    (Jiangsu key Laboratory of Traffic and Transportation Security, Huaiyin Institute of Technology, Huaian 223003, China)

  • Yi Zhang

    (Department of Transportation Engineering, School of Naval Architecture, Ocean & Civil Engineering, Shanghai Jiaotong University, Shanghai 200240, China)

  • Jian Lu

    (College of Transportation Engineering, Tongji University, Key Laboratory of Road and Traffic Engineering of the State Ministry of Education, Shanghai Key Laboratory of Rail Infrastructure Durability and System Safety, Shanghai 201804, China)

Abstract

With the increasing demand of hazardous material (Hazmat), traffic accidents occurred frequently during Hazmat transportation, which had caused widespread concern in communities. Therefore, a good understanding of Hazmat transportation accident characteristics and contributing factors is of practical importance. In this study, 1721 Hazmat accidents that have occurred during road transportation for the period 2014–2017 in China were examined, and a random-parameters ordered probit model was established to explore the influence of contributing factors on the severity of accidents by accounting for unobserved heterogeneity in the data. Both the injuries and the number of people evacuated were considered as the indicator of accident severity and investigated, respectively. Results show that higher injury severity is likely to be associated with type of Hazmat (compressed gas, explosive, and poison), misoperation, driver fatigue, speeding, tunnel, slope, county road, dry road surface, winter, dark, more than two vehicles, rear end crash, and explosion. As for the correlation between risk factors and the severity of evacuation, type of Hazmat (compressed gas, explosive, and poison), quantity of Hazmat (10–39 t), misoperation, county road, dry road surface, weekdays, dusk, explosion significantly contribute to increasing the severity of evacuation of Hazmat accidents.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:4:p:1344-:d:322672
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    References listed on IDEAS

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    1. Kenneth Train ., 2000. "Halton Sequences for Mixed Logit," Economics Working Papers E00-278, University of California at Berkeley.
    2. Talarico, Luca & Reniers, Genserik & Sörensen, Kenneth & Springael, Johan, 2015. "MISTRAL: A game-theoretical model to allocate security measures in a multi-modal chemical transportation network with adaptive adversaries," Reliability Engineering and System Safety, Elsevier, vol. 138(C), pages 105-114.
    3. Bhat, Chandra R., 2003. "Simulation estimation of mixed discrete choice models using randomized and scrambled Halton sequences," Transportation Research Part B: Methodological, Elsevier, vol. 37(9), pages 837-855, November.
    4. Garrido, Rodrigo A. & Bronfman, Andrés C., 2017. "Equity and social acceptability in multiple hazardous materials routing through urban areas," Transportation Research Part A: Policy and Practice, Elsevier, vol. 102(C), pages 244-260.
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    Cited by:

    1. Wang, Jinpei & Bai, Xuejie & Liu, Yankui, 2023. "Globalized robust bilevel optimization model for hazmat transport network design considering reliability," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
    2. 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.
    3. Fanyu Meng & Pengpeng Xu & Cancan Song & Kun Gao & Zichu Zhou & Lili Yang, 2020. "Influential Factors Associated with Consecutive Crash Severity: A Two-Level Logistic Modeling Approach," IJERPH, MDPI, vol. 17(15), pages 1-16, August.
    4. Shengxue Zhu & Shiwen Zhang & Hong Lang & Chenming Jiang & Yingying Xing, 2022. "The Situation of Hazardous Materials Accidents during Road Transportation in China from 2013 to 2019," IJERPH, MDPI, vol. 19(15), pages 1-15, August.
    5. Shuaiming Chen & Haipeng Shao & Ximing Ji, 2021. "Insights into Factors Affecting Traffic Accident Severity of Novice and Experienced Drivers: A Machine Learning Approach," IJERPH, MDPI, vol. 18(23), pages 1-20, December.
    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.
    7. 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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