Map matching can provide useful traffic information by aligning the observed trajectories of vehicles with the road network on a digital map. It has an essential role in many advanced intelligent traffic systems (ITSs). Unfortunately, almost all current map-matching approaches were developed for GPS trajectories generated by probe sensors mounted in a few vehicles and cannot deal with the trajectories of massive vehicle samples recorded by fixed sensors, such as camera detectors. In this paper, we propose a novel map-matching model termed Fixed-MM, which is designed specifically for fixed sensor data. Based on two key observations from real-world data, Fixed-MM considers (1) the utility of each path and (2) the travel time constraint to match the trajectories of fixed sensor data to a specific path. Meanwhile, with the laws derived from the distribution of GPS trajectories, a path generation algorithm was developed to search for candidates. The proposed Fixed-MM was examined with field-test data. The experimental results show that Fixed-MM outperforms two types of classical map-matching algorithms regarding accuracy and efficiency when fixed sensor data are used. The proposed Fixed-MM can identify 68.38% of the links correctly, even when the spatial gap between the sensor pair is increased to five kilometers. The average computation time spent by Fixed-MM on one point is only 0.067 s, and we argue that the proposed method can be used online for many real-time ITS applications.

中文翻译：

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基于效用理论的固定传感器数据地图匹配

通过将观察到的车辆轨迹与数字地图上的道路网络对齐，地图匹配可以提供有用的交通信息。它在许多高级智能交通系统（ITS）中扮演着至关重要的角色。不幸的是，几乎所有当前的地图匹配方法都是针对由安装在少数车辆中的探针传感器生成的GPS轨迹开发的，并且无法处理由固定传感器（例如摄像机检测器）记录的大量车辆样本的轨迹。在本文中，我们提出了一种新颖的地图匹配模型，称为Fixed-MM，该模型专门针对固定传感器数据而设计。基于对现实世界数据的两个关键观察，Fixed-MM考虑了（1）每条路径的效用和（2）行进时间约束，以将固定传感器数据的轨迹匹配到特定路径。同时，根据GPS轨迹分布的定律，开发了一种路径生成算法来搜索候选对象。拟议的固定式MM已通过现场测试数据进行了检查。实验结果表明，在使用固定传感器数据时，Fixed-MM优于两种经典的地图匹配算法，它们在准确性和效率上都很高。即使传感器对之间的空间距离增加到五公里，所提出的固定式MM也可以正确识别68.38％的链路。Fixed-MM在一个点上花费的平均计算时间仅为0.067 s，我们认为该方法可在线用于许多实时ITS应用程序。实验结果表明，在使用固定传感器数据时，Fixed-MM优于两种经典的地图匹配算法，它们在准确性和效率上都非常出色。即使传感器对之间的空间距离增加到五公里，所提出的固定式MM也可以正确识别68.38％的链路。Fixed-MM在一个点上花费的平均计算时间仅为0.067 s，我们认为该方法可在线用于许多实时ITS应用程序。实验结果表明，在使用固定传感器数据时，Fixed-MM优于两种经典的地图匹配算法，它们在准确性和效率上都很高。即使传感器对之间的空间距离增加到五公里，所提出的固定式MM也可以正确识别68.38％的链路。Fixed-MM在一个点上花费的平均计算时间仅为0.067 s，我们认为该方法可在线用于许多实时ITS应用程序。