Abstract
With the increase in the demand for and transportation of hazardous materials (Hazmat), frequent Hazmat road transport accidents, high death tolls and property damage have caused widespread societal concern. Therefore, it is necessary to carry out risk factor analysis of Hazmat transportation; predict the severity of accidents; and develop targeted, extensive and refined preventive measures to guarantee the safety of Hazmat road transportation. Based on the philosophy of graded risk management, this study used a priori algorithms in association rule mining (ARM) technology to analyze Hazmat transport accidents, using road types as classification criteria to find rules that had strong associations with property-damage-only (PDO) accidents and casualty (CAS) accidents under different road types. The results indicated that accidents involving PDO had a strong association with weather (WEA), traffic signals (TS), surface conditions (SC), fatigue (FAT) and vehicle safety status (VSS), and that accidents involving CAS had a strong association with VSS, equipment safety status (ESS), time of day (TOD) and WEA when urban roads were used for Hazmat transportation. Among Hazmat transport incidents on rural roads, the incidence of PDO accidents was associated with intersections (IN), SC, WEA, vehicle type (VT), and segment type (ST), while the occurrence of CAS accidents was associated with qualification (QUA), ESS, TS, VSS, SC, WEA, TOD, and month (MON). Strong associations between the occurrence of PDO accidents and related items, such as IN, SC, WEA and FAT, and the occurrence of CAS accidents and related items, such as ESS, TOD, VSS, WEA and SC, were identified for Hazmat road transport accidents on highways. The accident characteristics exemplified by strongly correlated rules were used as the input to the prediction model. Considering the scarcity of these events, four prediction models were selected to predict the severity of Hazmat accidents on each road type employing four analyses, and the most suitable prediction model was determined based on the evaluation criteria. The results showed that extreme gradient boosting (XGBoost) is preferable for predicting the severity of Hazmat accidents occurring on urban roads and highways, while nearest neighbor classification (NNC) is more suitable for predicting the severity of Hazmat accidents occurring on rural roads.
Highlights
The results showed that extreme gradient boosting (XGBoost) is preferable for predicting the severity of hazardous materials (Hazmat) accidents occurring on urban roads and highways, while nearest neighbor classification (NNC) is more suitable for predicting the severity of Hazmat accidents occurring on rural roads
Exploring the leading causes and predicting the severity of Hazmat road transport accidents on different road types using road types as grading criteria is meaningful for building a community with traffic safety as a priority
The use of association rule mining (ARM) can both compensate for the negative impact of correlation between risk factors as independent variables in accident severity analysis and fill the shortcoming in which machine learning cannot provide a reasonable explanation for the antecedents and consequences of accident occurrences
Summary
China has become the world’s largest producer and seller of chemicals, and the accompanying logistics have increased rapidly with the booming development of production, sales and related activities. Due to the uneven geographical distribution of product supply and product demand in China’s industries, approximately 95% of hazardous materials (Hazmat) in China must be transported off-site [1]. Geographical differences, and nonuniform technical conditions, information systems are not interoperable, and railroads, waterways and other modes of transport are not fully utilized. Most Hazmat must be transported by road. In 2020, China’s total shipments of Hazmat reached 1.7 billion tons, of which approximately 1.2 billion tons, Sustainability 2021, 13, 12773.
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