Severity analysis of road transport accidents of hazardous materials with machine learning

Abstract

Objective

The aim of this study was to explore a suitable method for analyzing road transport accidents that involve hazardous materials and to explore the main factors that influence the occurrence of accidents of varying severity.

Methods

The 2015–2019 reported crash data from the Ministry of Transport of the People's Republic of China were obtained, and road transport crashes involving hazardous materials were extracted as the analysis data. The dataset was classified into three injury severity categories: property damage only (PDO), injured (INJ), and fatal (FAT). A statistical model and three machine learning-based models were developed: a random parameters logit model (RPLM), multilayer perceptron (MLP), decision tree C5.0 (C5.0) and support vector machine (SVM). The four models were trained/estimated using the training/estimation dataset, and the best model was used to model accidents of the three different severity levels. The main factors that influence the occurrence of accidents at each crash severity level were obtained.

Results

C5.0 had the best modeling performance. The direct accident form (DAF), indirect accident form (IAF) and road segment (RS) were determined to be the critical determinants of PDO accidents. The DAF, IAF, road type, RS and time had a substantial effect on INJ accidents. The DAF, IAF, hazardous material type (HMT) and road surface condition were important factors in the occurrence of FAT accidents.

Conclusions

Different data have unique characteristics, and the best modeling and analysis method should be chosen accordingly. The safety of road transport of hazardous materials in China is poor, and the losses caused by accidents are substantial. Strengthening the monitoring of travel speed and travel time; improving driver safety awareness, driving skills and the ability to mitigate emergencies; improving the configuration of vehicle safety equipment and the linkage with the control center and rescue center; improving the environmental differences between inside a tunnel and outside a tunnel; reducing the design of long downhill and steep slope sections; reducing the transport plan in unsafe environments; and improving the ability of road management to mitigate bad environments can be effective measures to reduce the severity of road transport accidents involving hazardous materials.

Keywords:

• Hazardous materials
• road transportation
• accident severities
• influencing factors
• machine learning

Additional information

Funding

We acknowledge the general assistance from the Fundamental Research Funds for the Central Universities (300102228206) and National Key Research and Development (2017YFC0804800).