Influence Factors and Coupling Relationship of Traffic Accident Injury Degree Based on a Data-driven Approach

In order to accurately identify the relevant factors affecting the traffic accident injury degree of mountainous expressway (TAIDME), a model named random forest naive bayes-coupling degree model (RFNB-CDM) was constructed. Firstly, 1760 pieces of accident data of mountainous expressway in Yunnan Province from 2016 to 2020 were processed. And 18 factors including accident information, road information, accident motor vehicle information, and driver information were studied as initial features. A RF model was used for feature extraction, and the importance ranking of each factor for the severity of traffic accidents (TASME) of mountainous expressway was obtained. Secondly, the new features are input into a NB model to conduct a single factor analysis on the influencing factors of TAIDME. To improve the shortcomings of the original model that cannot accurately describe the relationship between the influencing factors, this paper introduces the coupling degree model to make an example verification analysis. Eight kinds of factors, i.e., rear-end collision, the period from 18:00 to 6:00 of the next day, the number of accident vehicles, downhill, no street lighting at night, freight, large and medium-sized trucks, and straight uniform are more likely to increase TAIDME. The coupling effect of rear-end collision and straight uniform velocity is more likely to lead to major accidents. Road surface dryness, roadside metal protection, and central green belt isolation can reduce TAIDME, and when roadside metal protection and central green belt isolation are coupled, the TAIDME can be reduced. The conclusion of this study can provide a theoretical basis and decision- making reference for the prevention of traffic accidents and the reduction of the injury degree of mountain highway accidents. © 2022 Science Press. All rights reserved.