High-Frequency Trajectory Map Matching Algorithm Based on Road Network Topology

Accurately mapping the raw global position system (GPS) trajectories to the road network is the basis for studying the application of trajectory data. This study proposes a novel off-line map matching algorithm based on road network topology, to address the problems of low execution efficiency and poor matching accuracy of selective look-ahead map matching (SLAMM) algorithm. First, the noise points of the trajectory data are removed by data preprocessing. Second, the algorithm searches for critical samples in the trajectory data and segments the data accordingly. Then, the adjacent road segments around the transition node corresponding to the critical sample are selected as candidate arcs. Finally, the segmented trajectory data are matched to the road network by constructing an error ellipse. The algorithm fully considers the topology of the road network and the characteristics of high-frequency trajectory data. The experimental results, using Beijing trajectory data to perform matching on an actual road network environment, show that the proposed algorithm is more efficient and robust than other map matching algorithms for high-frequency trajectories.

Automated summary

Accurately mapping GPS trajectories to the road network is crucial for studying trajectory data applications. This study proposes a novel offline map matching algorithm based on road network topology to address the low efficiency and poor accuracy issues of the selective look-ahead map matching algorithm. The algorithm removes noise points, segments the trajectory data, selects candidate arcs, and matches the segmented data to the road network using error ellipses. Experimental results demonstrate that this algorithm is more efficient and robust for high-frequency trajectories compared to other map matching algorithms.