Dangerous Driving Behavior Recognition and Prevention Using an Autoregressive Time-Series Model

作     者：Hongxin Chen Shuo Feng Xin Pei Zuo Zhang Danya Yao 

作者机构：Department of AutomationTsinghua UniversityBeijing 100084China 

出 版 物：《Tsinghua Science and Technology》 (清华大学学报（自然科学版（英文版）)

年 卷 期：2017年第22卷第6期

页      面：682-690页

核心收录：

学科分类：08[工学] 082303[工学-交通运输规划与管理] 082302[工学-交通信息工程及控制] 0823[工学-交通运输工程] 

主　　题：time headway driving behavior traffic safety autoregressive time series model remaining life driving warning strategy 

摘      要：Time headway is an important index used in characterizing dangerous driving behaviors. This research focuses on the decreasing tendency of time headway and investigates its association with crash occurrence. An autoregressive（AR） time-series model is improved and adopted to describe the dynamic variations of average daily time headway. Based on the model, a simple approach for dangerous driving behavior recognition is proposed with the aim of significantly decreasing headway. The effectivity of the proposed approach is validated by means of empirical data collected from a medium-sized city in northern China. Finally, a practical early-warning strategy focused on both the remaining life and low headway is proposed to remind drivers to pay attention to their driving behaviors and the possible occurrence of crash-related risks.