Trajectory based Monitoring of Saturation Flow Rate with Video Calibration: a Case Study
Trajectory based Monitoring of Saturation Flow Rate with Video Calibration: a Case Study
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DOI：
10.1109/itsc.2019.8917023
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发表时间：
2019-10
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影响因子：
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通讯作者：
Fourati Walid;Aleksandar Trifunović;Morten Flesser;B. Friedrich
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文献类型：
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作者：
Fourati Walid;Aleksandar Trifunović;Morten Flesser;B. Friedrich
本文介绍了一个案例研究的综合实践为导向的方法，连续监测交叉口的能力，从数据流的车辆轨迹后，一次校准与信息自动收集的视频图像，通过对象识别和跟踪。这种方法将允许快速适应即将到来的交通流物理变化，连接和自动化车辆，以及有效容量知识的时间和空间传播，填补目前依赖于高速公路容量手册或准时测量的实践中的空白。来自探头轨迹的历史数据用于提取信号控制方法的共同特征，例如周期时间、绿色开始和停止线位置。将计算机视觉机器学习应用于信号控制方法的短视频记录，我们跟踪车辆并提取特定特征，例如队列密度（相当于空间车头时距）以及车辆速度曲线与车辆大小之间的关系。这些参数有助于从新观察到的探头轨迹中提取饱和流速值。从视频中提取的相同信息用作比较基础，尽管时间帧不同。
this paper presents a case study of an integrated practice-oriented methodology to continuously monitor intersections' capacity from a data stream of vehicle trajectories after a one-time calibration with information automatically collected from video images through object recognition and tracking. Such an approach would allow a quick adaptation to upcoming changes in traffic flow physics with connected and automated vehicles, as well as the spread in time and space of the knowledge of effective capacities, filling the current gap in the practice that relies on highway capacity manuals or punctual measurements. Historical data from probe trajectories are used to extract common features of the signalized approach such as cycle time, green start and stop line position. Applying computer vision machine learning on short video recordings of the signalized approach we track vehicles and extract specific features such as queue density (equivalently spatial headway) and the relationship between vehicles speed profiles and vehicle sizes. These parameters help extract saturation flow rate values from newly observed probe trajectories. The same information extracted from videos serve as a comparison basis in spite of different time frame.