Department of Transportation Systems Engineering, Hanyang University at Ansan, Sa1-dong, Sangnok-gu, Ansan-city, Kyunggi-do 426-791, Republic of Korea.
Accid Anal Prev. 2010 Nov;42(6):1888-93. doi: 10.1016/j.aap.2010.05.009.
Recent advancement in traffic surveillance systems has allowed for obtaining more detailed vehicular movement such as individual vehicle trajectory data. Understanding the characteristics of interactions between leading vehicle and following in the traffic flow stream is a backbone for designing and evaluating more sophisticated traffic and vehicle control strategies. This study proposes a methodology for estimating rear-end crash potential, as a probabilistic measure, in real time based on the analysis of vehicular movements. The methodology presented in this study consists of two components. The first estimates the probability that a vehicle's trajectory belonging to either 'changing lane' or 'going straight'. A binary logistic regression (BLR) is used to model the lane-changing decision of the subject vehicle. The other component derives crash probability by an exponential decay function using time-to-collision (TTC) between the subject vehicle and the front vehicle. Also, an aggregated measure, crash risk index (CRI) is used in the analysis to accumulate rear-end crash potential for each subject vehicle. The result of this study can be used in developing traffic control and information systems, in particular, for crash prevention.
近年来，交通监控系统的发展使得获取更详细的车辆运动信息成为可能，例如车辆的个体轨迹数据。了解交通流中前车与后车之间的相互作用特征是设计和评估更复杂的交通和车辆控制策略的基础。本研究提出了一种基于车辆运动分析实时估计追尾碰撞可能性的方法，该方法作为一种概率度量。本研究提出的方法由两个部分组成。第一部分估计车辆轨迹属于“变道”或“直道”的概率。使用二元逻辑回归（BLR）模型来模拟目标车辆的变道决策。另一部分则通过时间碰撞（TTC）的指数衰减函数来推导碰撞概率，其中 TTC 是目标车辆与前车之间的时间。此外，在分析中还使用了综合指标——碰撞风险指数（CRI），以累积每个目标车辆的追尾碰撞潜在风险。该研究结果可用于开发交通控制和信息系统，特别是用于预防碰撞。
