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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.

With the increase of hazardous materials (Hazmat) demand and transportation, frequent Hazmat road transportation accidents had arisen the widespread concern in the community. Thus, it is necessary to analyze the risk factors’ implications, which would make the safety of Hazmat transportation evolve from “passive type” to “active type”. In order to explore the influence of risk factors resulting in accidents and predict the occurrence of accidents under the combination of risk factors, 839 accidents that have occurred for the period 2015–2016 were 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 the following. (1) The Bayesian network model can explore the most probable factor or combination leading to the accident, which calculated the posterior probability of each risk factor. For example, the importance of three or more vehicles in an accident leading 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 transporting in autumn. (2) The model can predict the occurrence of accident by setting the influence degrees of specific factor. Such that the probability of rear-end accidents caused by “speeding” is 0.42, and the probability could reach up to 0.97 when the driver is speeding at the low-class roads. Moreover, the complex logical relationship in Hazmat road transportation accidents could be obtained, and 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.

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