ABSTRACT

Offline map matching identifies corresponding roads to a GPS trajectory represented by a series of recorded geographic coordinates (GPS points) to the road network. This paper defines matching error as cost on the corresponding road-link to matched GPS points and formulates the offline map matching problem as a shortest path problem with resource constraints. By regarding matched points on one link as a type of resource consumed, the resource constraint indicates that the number of matched GPS points equals the total number of points in the given trajectory. We propose an offline map matching algorithm based on shortest paths by calculating the matching error on each link and extending the classic label-setting shortest path algorithm to find the path with the minimum total matching error for all GPS points. We use real-world taxi trajectories to compare our algorithm with three state-of-the-art map matching algorithms. Our algorithm outperforms all benchmark algorithms in terms of both matching accuracy and computational efficiency. Our algorithm achieves greater matched length (5.36 to 12.27% larger) and lower mis-matched length (3.72 to 75.30% smaller) at a very high matching speed (60.59 points per second on average over thirteen sampling intervals).

摘要

离线地图匹配可识别与 GPS 轨迹对应的道路，该轨迹由一系列记录到道路网络的地理坐标（GPS 点）表示。本文将匹配误差定义为匹配 GPS 点的相应道路链路上的成本，并将离线地图匹配问题表述为具有资源约束的最短路径问题。通过将一条链路上的匹配点视为消耗的一种资源，资源约束表明匹配的 GPS 点数等于给定轨迹中点的总数。我们提出了一种基于最短路径的离线地图匹配算法，通过计算每个链路上的匹配误差，并扩展经典的标签设置最短路径算法，以找到所有 GPS 点总匹配误差最小的路径。我们使用真实世界的出租车轨迹将我们的算法与三种最先进的地图匹配算法进行比较。我们的算法在匹配精度和计算效率方面都优于所有基准算法。我们的算法以非常高的匹配速度（在 13 个采样间隔内平均每秒 60.59 个点）实现了更大的匹配长度（大 5.36 到 12.27%）和更低的错配长度（小 3.72 到 75.30%）。