Critical safety management driver identification based upon temporal variation characteristics of driving behavior

Accident; analysis and prevention(2023)

引用 0|浏览5
暂无评分
摘要
•Established a novel driver-level risk modeling framework based on the temporal variation characteristics of driving behavior.•Proposed “traffic entropy” index to regularize heterogeneous driving behavior.•Utilized deep learning models to conduct the temporal variation feature mining for critical safety management driver identification.•Developed a convolutional neural network (CNN) model based on time-series traffic entropy data, achieving an AUC index of 0.754.•Evaluated the influence of risk factors on driver-level risks.
更多
查看译文
关键词
Critical safety management driver identification,Driving behavior temporal variation,Traffic entropy,Driving behavior heterogeneity,Deep learning models
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要