China Safety Science Journal ›› 2022, Vol. 32 ›› Issue (3): 174-182.doi: 10.16265/j.cnki.issn1003-3033.2022.03.024

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Prediction model for road transport accidents of hazardous chemicals based on Bayesian network

LU Yi1,2(), WU Jiangle1,2, SHAO Shuzhen1, SHI Shiliang1, ZHOU Rongyi1, WANG Wei2   

  1. 1School of Resource & Environment and Safety Engineering, Hunan University of Science and Technology, Xiangtan Hunan 411201, China
    2Shanghai Fire Science and Technology Research Institute of MEM, Shanghai, 200032, China
  • Received:2021-12-20 Revised:2022-02-17 Online:2022-08-23 Published:2022-09-28

Abstract:

In order to accurately predict road transport accidents of hazardous chemicals, firstly, data of 1,727 such transport accidents in China from 2015 to 2020 were collected, and a Bayesian network (BN) was developed with accident influencing factors, accident types, accident emergency treatment time and the degree of casualties as main nodes. Then, a prediction model for the accidents was established in Netica, and its validity was verified according to the mean absolute error (MAE). Finally, through forward causal reasoning and reverse diagnostic reasoning, the posterior probability changes of each variable of target nodes were observed, and accident development trend and evolution process under set conditions were explored. The results show that the model can effectively predict accidents under set conditions. Through positive causal inference, it is concluded that the most likely form of accident at noon is the leakage accident caused by rear-end collision or tank leakage, while based on reverse diagnostic reasoning, it is found that carrying capacity <30 t is a significant condition for flammable liquid leakage accidents to be successfully disposed of within 0 to 3 hours.

Key words: hazardous chemicals, road transportation, Bayesian network (BN), accident prediction, Netica