Prediction of traffic accident severity and extraction of decision rules for extraordinarily serious accident in mountainous high-speed tunnels
In order to effectively avoid the traffic accidents in tunnel section with serious casualties,the historical traffic acci-dent data from 2013 to 2023 in China were selected for statistical analysis,and 14 influencing factors were selected combined with the severity and spatiotemporal distribution of accidents.The random forest model was used to construct a prediction model for the severity of traffic accidents in mountainous high-speed tunnel section,and the prediction accuracy of this model was compared with those of the ordered Logit model and BP neural network model.The decision rules for"extraordinarily seri-ous accident"in random forest were extracted based on the importance of rules.The results show that the random forest model has better prediction results for the severity of accidents,and the decision rules reveal the combination of influencing factors when the casualties are severe.The research results can provide reference for improving the influence mechanism of accident severity.
NETL
NSTL
万方数据
