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Bayesian network-based risk assessment for hazmat transportation on the Middle Route of the South-to-North Water Transfer Project in China

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Abstract

A Bayesian network-based risk assessment (BN-RA) model was developed to assess the risk of hazmat transportation by identifying, modeling, and quantitatively calculating the risk on the Middle Route of the South-to-North Water Transfer Project (MRSNWTP) in China. First, we selected seven parameters from five categories of impact factors (i.e., human, vehicle, tank, weather, and road environment) as quintessential risk factors for accidents. Second, we used the developed BN-RA model to predict the probability of accidents. Third, using bidirectional inference in the BN approach, we analyzed and ranked the importance of the effects of these factors. The developed model was subsequently applied to assess the risks of major bridges crossing canals with different pavement grades and traffic flow levels both at present and in the future for the Beijing-Shijiazhuang Section of the MRSNWTP. The results indicated the following: (1) Although the overall potential risk of hazmat transportation accidents on all bridges in the Beijing-Shijiazhuang Section was fairly low (e.g., 0.08 %), the impacts cannot be ignored because of the potential for huge losses. (2) According to the analysis of many factors that may affect accidents, the driving patterns of drivers exerted the strongest influence on the probability of an accident, followed by vehicle conditions and lighting conditions. (3) If a vehicle were to fail, the highest probability (0.17 %) of an accident would arise if it were traveling on a road with no street lighting and poor road conditions at night. (4) Assuming that a vehicle was in good condition, the highest probability (0.12 %) of an accident arised when the vehicle suddenly encountered poor road conditions with no lights on a foggy night. (5) The predicted probabilities of accidents on Bridge TCWRR (short for the Tang County West Ring Road Bridge) in the short (i.e., the year 2017), medium (i.e., the year 2022) and long terms (i.e., the year 2027) were 3.25 × 10−4, 5.37 × 10−4, and 8.89 × 10−4, respectively. For Bridge DNR (short for the Dian Bridge on the North Road), these values were 8.64 × 10−6, 1.02 × 10−5, and 1.21 × 10−5, respectively. Based on the risk assessment results, to lower the accident probability and avoid the serious consequences resulting from hazmat transportation accidents, we developed an appropriate emergency response program to reduce potential hazards. This research resolved the problems of randomness and uncertainty associated with hazmat transportation in the MRSNWTP and can provide a reference for the effective prevention of hazmat transportation accidents and scientific decision-making in risk management.

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Acknowledgments

This study was supported by the National Science and Technology Support Program (No. 2011BAC12B02), and the Fund for Innovative Research Group of the National Natural Science Foundation of China (No. 51421065). We would like to extend special thanks to editors and anonymous reviewers for all their detailed comments and valuable suggestions in greatly improving the quality of this paper.

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Authors and Affiliations

  1. State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing, 100875, China

    Xuan Wang, Jie Zhu, Yanpeng Cai & Zhifeng Yang

  2. Key Laboratory for Water and Sediment Sciences of the Ministry of Education, School of Environment, Beijing Normal University, Beijing, 100875, China

    Xuan Wang, Jie Zhu, Chunhui Li & Zhifeng Yang

  3. Shidu Town People’s Government, Beijing, 102411, China

    Fangbing Ma

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  1. Xuan Wang
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  2. Jie Zhu
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  3. Fangbing Ma
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  4. Chunhui Li
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  5. Yanpeng Cai
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Correspondence to Xuan Wang.

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Wang, X., Zhu, J., Ma, F. et al. Bayesian network-based risk assessment for hazmat transportation on the Middle Route of the South-to-North Water Transfer Project in China. Stoch Environ Res Risk Assess 30, 841–857 (2016). https://doi.org/10.1007/s00477-015-1113-6

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