Glocal map-matching algorithm for high-frequency and large-scale GPS data

Abstract

The global positioning system (GPS) trajectory data are extensively utilized in various fields, such as driving behavior analysis, vehicle navigation systems, and traffic management. GPS sensors installed in numerous driving recorders and smartphones facilitate data collection on a large-scale in a high-frequency manner. Therefore, map-matching algorithms are indispensable to identify the GPS trajectories on a road network. Although the local map-matching algorithm reduces computation time, it lacks sufficient accuracy. Conversely, the global map-matching algorithm enhances matching accuracy; however, the computations are time consuming in the case of large-scale data. Therefore, this study proposes a method to improve the accuracy of the local map-matching algorithm without affecting its efficiency. The proposed method first executes the incremental map-matching algorithm. It then identifies the mismatching links in the results based on the connectivity of the links. Finally, the shortest path algorithm and the longest common subsequence are used to correct these error links. An elderly driver’s driving recorder data were used to conduct the experiment to compare the proposed method with four state-of-the-art map-matching algorithms in terms of accuracy and efficiency. The experimental results indicate that the proposed method can significantly increase the accuracy and efficiency of the map-matching process when considering high-frequency and large-scale data. Particularly, compared with the two-benchmark global map-matching algorithms, the proposed method can reduce the error rate of map-matching by nearly half, only consuming 18% and 58% of the computation time of the two global algorithms, respectively.