Macro-level hazardous material transportation safety analysis in China using a Bayesian negative binomial model combined with conditional autoregression prior

Abstract

Abstract Traffic safety for hazardous material (hazmat) transportation has not been studied well at a macro level in recent years. A Bayesian negative binomial conditional autoregressive safety model was used within Chinese provinces and cities. A total of 1,229 hazmat transportation crashes in China were collected from the years 2015 to 2017. The frequency of hazmat transportation crashes and the frequency of severe crashes including fatalities and serious injuries were studied in relation to socioeconomic factors, road classification, and the scale of hazmat transportation. The results show that higher crash frequencies are associated with a greater gross domestic product index, increasing road densities, and number of hazmat transportation vehicles and hazmat drivers per vehicle. The frequency of severe crashes tends to be higher in provinces with greater populations, increasing road densities, mileage of low-grade roads, and number of companies. The urban road mileage and number of hazmat loaders are negatively associated with the total number of hazmat crashes and severe crashes. Additionally, the hospital density also has a negative correlation with the frequency of severe traffic crashes. These results could help hazmat transportation managers and planners determine the risk factors of hazmat crashes on a macro level and develop appropriate measures for improving hazmat transportation safety.