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A two-stage traffic sensor location method for low-volume road incident detection

机译:小流量道路事件检测的两阶段交通传感器定位方法

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Low-volume road always lied at the area with few inhabitants and few traffic monitoring equipment, thus it's difficult to detect traffic incident and take traffic rescue in a timely manner. However, most research focused on high-volume road traffic sensor location for OD matrix estimation, travel time estimation and traffic volume estimation, not aiming to low-volume road for incident detection. Firstly, a two-stage traffic sensor location method was proposed for low-volume road incident detection, with the optimization objective of maximizing incident detection rate. At the first stage, high-resolution camera Automatic Vehicle Identification (AVI) was used to monitor closed road segments, at the second stage, video was used to monitor both the closed road segments and intersection segments. Then, reliability theory was adopted to model the incident detection rate of AVI and video parallel detection system. Next, a real-coded genetic algorithm was proposed to optimize the traffic sensor location problem. Finally, a case study was conducted and the case study result shows that incident detection rate of the first and second stage increase by 36.39% and 7.68% respectively, this demonstrates that the proposed method is effective.
机译:低容量道路总是散布在人口少,交通监控设备少的地区,因此难以及时发现交通事故并及时进行交通救援。但是,大多数研究都集中在用于OD矩阵估计,行进时间估计和交通量估计的大容量道路交通传感器位置上,而不是针对用于事件检测的小容量道路。首先,提出了一种用于交通量小的道路交通事件检测的两阶段交通传感器定位方法,其目标是最大化道路交通事件的发现率。在第一阶段,使用高分辨率摄像机自动车辆识别(AVI)监视封闭的路段,在第二阶段,使用视频监视封闭的路段和交叉路口。然后,采用可靠性理论对AVI和视频并行检测系统的事件检测率进行建模。接下来,提出了一种实码遗传算法来优化交通传感器的位置问题。最后,进行了案例分析,结果表明,第一阶段和第二阶段的事件检测率分别提高了36.39%和7.68%,表明该方法是有效的。

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