交通运输研究2024，Vol.10Issue(3)：66-74,9.DOI:10.16503/j.cnki.2095-9931.2024.03.008
基于XGBoost改进模型的高速公路事故多发点鉴别及预测
Identification and Prediction of Highway Accident-Prone Spots Based on Improved XGBoost Model
摘要
Abstract
In order to accurately and quickly predict the accident-prone sections of highways,obtain the characteristic samples of accident spatio-temporal data,clarify the spatio-temporal evolution pat-terns and correlation mechanisms of accidents,and identify the location and the spatio-temporal evolu-tion patterns of accident-prone points based on spatio-temporal hotspot analysis results,this paper con-structed GA-XGBoost accident-prone point prediction model.Firstly,based on the sample data,the spatio-temporal cube was constructed under annual scale and daily scale respectively,and hotspot analysis was carried out.According to the spatio-temporal hotspot analysis results,the locations of ac-cident-prone point of the sample highway and their spatio-temporal evolution pattern were obtained.After comparative analysis and relevance test,seven characteristics were selected to predict whether the accident was located in the accident-prone location,including accident occurrence time,mileage,event type,processing time,number of affected lanes,whether it was in the vicinity of the confluence,and whether it was a holiday.Then,four algorithms,including CNN-LSTM,CNN-LSTM-ATT,Ran-dom Forest,and XGBoost model,were used to predict the accident-prone points respectively,and the results showed that the XGBoost model had the highest prediction accuracy compared to the other three algorithms.Subsequently,the XGBoost model was optimized with GA(Genetic Algorithm),and a GA-XGBoost combination model was constructed,which improved the prediction accuracy by 0.06 and F1 score by 0.07,and the precision by 0.08.This indicated that compared to existing algorithms,the GA-XGBoost model could more accurately predict whether a road section was located in an acci-dent-prone area,and clarify the spatio-temporal feature of accidents in accident-prone areas.Finally,the prediction results were interpreted by SHAP value analysis,and it was found that the samples lo-cated near the confluence,with incident types of rollover,breakdown,during National Day holidays and with 2 affected lanes were more likely to be at an accident-prone point compared to those not lo-cated near the confluence and with other accident types.Based on this,preventive measures could be taken in traffic safety and emergency management to improve the efficiency and emergency response capabilities of traffic management,so as to creat a safe and efficient traffic environment.关键词
事故多发点/时空特征/事故鉴别与预测/XGBoost/遗传算法/时空立方体模型/SHAP解释Key words
accident-prone spot/spatio-temporal feature/accident identification and prediction/XGBoost/GA(Genetic Algorithm)/spatio-temporal cube model/SHAP interpretation分类
交通工程引用本文复制引用
马飞虎,张玉玲,宁玮,谢天长,王海玲..基于XGBoost改进模型的高速公路事故多发点鉴别及预测[J].交通运输研究,2024,10(3):66-74,9.基金项目
国家重点研发计划项目(2021YFE0105600) （2021YFE0105600）
国家自然科学基金面上项目(51978263) （51978263）
江西省自然科学基金重点项目(20192ACBL20008) （20192ACBL20008）
