01697284

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With the increase of hazardous materials (Hazmat) demand and transportation, frequent Hazmat road transportation accidents had arisen the widespread concern in the community. In order to explore the influence of risk factors resulted in accidents and predict the occurrence of accidents under the combination of risk factors, 839 accidents that have occurred for the period 2015–2016 was collected and examined. The Bayesian network structure was established by experts’ knowledge using Dempster-Shafer evidence theory. Parameter learning was conducted by the Expectation-Maximization (EM) algorithm in Genie 2.0. The two main results could be likely to obtain: (1) The Bayesian network model can explore the most probable factor or combination leading to the accident. For example, the importance of three or more vehicles in an accident leads to the severe accident is higher than less vehicles, and in the absence of other evidences, the most probable reasons for “explosion accident” are vehicles carrying flammable liquids; larger quantity Hazmat; vehicle failure and transported in autumn. (2) The model can predict the the occurrence of accident by setting the influence degrees of specific factor. Such as the probability of rear-end accidents caused by “speeding” is 0.42, and the probability could reach up to 0.97 when the driver speeding at the low-class roads. Moreover, the uncertain relation among various risk factors could be expressed. These findings could provide theoretical support for transportation corporations and government department on taking effective measures to reduce the risk of Hazmat road transportation.

This paper was sponsored by TRB committee AT040 Standing Committee on Transportation of Hazardous Materials.

19-01726

English

8p

2019

Transportation Research Board 98th Annual Meeting

Digital/other

Figures; References; Tables

Algorithms; Bayes' theorem; Crash causes; Crash data; Crash risk forecasting; Explosions; Hazardous materials; Probability

Highways; Planning and Forecasting; Safety and Human Factors

Transportation Research Board Annual Meeting 2019 Paper #19-01726

TRIS, TRB, ATRI

Dec 7 2018 9:22AM