A Hidden Markov Model-Based Map-Matching Approach for Low-Sampling-Rate GPS Trajectories

Abstract

Map matching is the process of matching a series of recorded geographic coordinates (e.g., a GPS trajectory) to a road network. Due to GPS positioning errors and the sampling constraints, the GPS data collected by the GPS devices are not precise, and the location of a user cannot always be correctly shown on the map. Unfortunately, most current map-matching algorithms only consider the distance between the GPS points and the road segments, the topology of the road network, and the speed constraint of the road segment to determine the matching results. In this paper, we propose a spatio-temporal based matching algorithm (STD-matching) for low-sampling-rate GPS trajectories. STD-matching considers the spatial features such as the distance information and topology of the road network, the speed constraints of the road network, and the realtime moving direction which shows the movement of the user. In our experiments, we compare STD-matching with three existing algorithms, the ST-matching algorithm, the stMM algorithm, and the HMM-RCM algorithm, using a real data set. The experiment results show that our STD-matching algorithm outperforms the three existing algorithms in terms of matching accuracy.