From driving trajectories to driving paths: a survey on map-matching Algorithms
From driving trajectories to driving paths: a survey on map-matching Algorithms
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从行驶轨迹到行驶路径：地图匹配算法综述
DOI：
10.1007/s42486-022-00101-w
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发表时间：
2022-05-23
影响因子：
2.1
通讯作者：
Guo, Bin
中科院分区：
文献类型：
--
作者：
Jiang, Linli;Chen, Chaoxiong;Guo, Bin
随着内置全球定位系统（GPS）设备的广泛部署，可以方便地记录大量的驾驶轨迹。受GPS设备精度和行驶环境的影响，原始GPS轨迹会偏离车辆实际行驶的路径，这种不准确的数据对上游应用不利。因此，应用地图匹配来从GPS轨迹数据识别道路网络中的真实驾驶路径。地图匹配领域的研究已经取得了很多成果，但仍存在三个问题：（1）对近年来采用新技术的地图匹配算法缺乏全面的综述;（2）现有的地图匹配算法仍不能同时满足高精度和高效率的要求;（3）缺乏在统一的实验环境下对各种类型的匹配算法进行比较。在本文中，我们回顾了现有的匹配算法，并提出了一个新的分类方法的基础上。建议的分类能更好地揭示它们的属性，便于将来使用。此外，我们进行了四个代表性的算法之间的实验比较，以深入了解不同类别的属性。实验结果表明，一些解决方案，以提高匹配精度和效率的重要性。
With the widespread deployment of built-in Global Positioning System (GPS) devices, numerous volumes of driving trajectories can be recorded conveniently. Affected by GPS equipment precision and driving environments, raw GPS trajectories will deviate from the paths that vehicles really drove on. Such inaccurate data is not beneficial to the upstream applications. Therefore, map matching is applied to identify the true driving paths in the road network from the GPS trajectories data. A lot of studies in the filed of map matching have been proposed, but there still exist three problems: (1) there lacks a comprehensive review on recent map matching algorithms with new techniques; (2) the existing map-matching algorithms still fail to meet the requirements of both high precision and high efficiency simultaneously; (3) there is a lack of comparison between various types of matching algorithms on a unified experimental environment. In this paper, we review the existing matching algorithms and propose a new categorisation based on their methodologies. The proposed categorisation can better reveal their properties and facilitate the future utilization. In addition, we conduct an experimental comparison among four representative algorithms to give a deep insight to the properties of different categories. Experimental results reveal the importance of some solutions to improve matching accuracy and efficiency.