Interactive Effect on Traffic Accident Severity Considering Built Environment
To explore the mechanism of various factors influencing traffic accidents under the impact of the built environment,this paper proposes a method that integrates the ADASYN(Adaptive Synthetic Sampling)balancing algorithm with the CatBoost model to study road traffic accidents in Shenyang from 2015 to 2020,and to analyze the interactive effects of accident causation.Firstly,by employing geographic information matching,the study supplemented 14 built environment factors around the accident locations with to construct a multi-source dataset.Secondly,by comparing four classic machine learning models—namely,CatBoost,Random Forest,XGBoost,and LightGBM—the study selected the model with the strongest generalization ability.Subsequently,the SHAP(Shapley Additive Explanation)attribution method was used to interpret the optimal model to reveal the effect of individual risk factors and their importance ranking.Finally,based on single-factor analysis,the study explored the interactive effects between the built environment and accident characteristics.The research indicates that the same features have different impacts on the mechanism of accidents in both single-factor and dual-factor interaction analyses.In single-factor analysis,two factors,season and mode of transportation,have a significant positive impact on fatal accidents;whereas five factors,including trunk road density,expressway density,industrial land proportion,site morphology,and physical road separation,have a significant negative impact on fatal accidents.In dual-factor interaction analysis,high trunk road density interacting with autumn and winter seasons,and low industrial land proportion interacting with spring,have a positive impact on fatal accidents;while a high industrial land proportion interacting with pedestrian traffic has a negative impact.The findings of this study offer precise insights into the factors influencing the severity of traffic accidents,providing a theoretical foundation for optimizing and developing urban transportation systems.
NETL
NSTL
万方数据
维普
