北京交通大学学报2024，Vol.48Issue(6)：12-21,10.DOI:10.11860/j.issn.1673-0291.20240006
轨迹数据驱动的高速公路事故风险研判
Trajectory data-driven risk assessment of freeway accidents
摘要
Abstract
To address the challenges posed by fully enclosed freeway segments,high vehicle speeds and the substantial damage caused by traffic accidents,this study proposes a freeway accident risk as-sessment method that integrates the Random Forest(RF)algorithm for feature selection with the eX-treme Gradient Boosting(XGBoost)algorithm.First,by filtering private vehicle trajectory data from freeway accident segments,a data foundation for accident risk assessment is established under four dif-ferent spatiotemporal conditions(30 km upstream and 30 minutes before the accident,10 km upstream and 15 minutes before the accident,10 km upstream and 10 minutes before the accident,and 10 km upstream and 5 minutes before the accident).Next,a combined accident risk assessment method based on the RF and XGBoost is constructed.It evaluates accident risk after selecting various operational in-dicators for vehicles on the freeway.Finally,the algorithm's performance is assessed using five met-rics:accuracy,precision,recall,balanced F Score(F1),and Area Under Curve(AUC).Results indi-cate that the RF-XGBoost combination algorithm outperforms the Decision Tree(DT),Support Vec-tor Machine(SVM),and traditional XGBoost algorithms in accident risk assessment.Compared to the traditional XGBoost algorithm,the average accuracy of the RF-XGBoost algorithm is increased by 11.1%,the average precision is increased by 8.9%,and the average recall rate is increased by 7.625%.Under the spatiotemporal condition of 10 km upstream and 10 minutes before the accident,the algorithm achieves an accuracy of 80%,demonstrating optimal overall assessment performance.These findings provide theoretical and methodological support for freeway accident risk assessment and dynamic warnings for private vehicles.关键词
交通工程/交通安全/随机森林算法/XGBoost/风险研判Key words
traffic engineering/traffic safety/RF algorithm/XGBoost/risk assessment分类
交通工程引用本文复制引用
董春娇,许博,李鹏辉,庄焱,杨妙言..轨迹数据驱动的高速公路事故风险研判[J].北京交通大学学报,2024,48(6):12-21,10.基金项目
国家重点研发计划(2023YFC3009601)National Key R&D Plan(2023YFC3009601) （2023YFC3009601）
