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交通事件是引发快速路交通延误的主要因素，迅速、有效的检测事件是快速路交通管理与控制的重要组成部分。本文利用CUSUM理论提出了基于浮动车数据的城市快速路交通事件自动检测算法；通过采集北京市快速路上的真实事件信息以及浮动车探测数据，对不同采样间隔条件下的算法性能进行了实际验证，并与传统的基于浮动车数据的UCB算法验证结果进行了对比分析。结果表明：所提算法在采样间隔为1分钟条件下的检测性能要优于采样间隔为5分钟条件下的检测性能；相对于UCB算法而言，所提算法的检测性能大大提高，在同一误报率条件下检测率可提高10%左右。

Traffic Incident Detection Algorithm for Urban Expressways Based on Probe Vehicle Data

Traffic incidents are major contributors to traffic delay. Expedient and reliable incident detection is an important part of expressway traffic management and control in urban areas. This paper proposes an automated incident detection algorithm for urban expressways based on probe vehicle data by the CUSUM algorithm based approach. The presented algorithm is validated using the actual incident information and the probe vehicle data collected in Beijing at different time intervals. Its performance is evaluated and further compared with that of an existing probe-vehicle-based incident detection algorithm, the UCB algorithm. The results show that the performance of the proposed algorithm under 1-minute time interval is better than that under 5-minute time interval. It is also observed that the performance of the proposed algorithm is better than that of the UCB algorithm. Under the same false alarm rate, the detection rate of the proposed algorithm is improved by 10%.