Frequent Pattern-Based Map-Matching on Low Sampling Rate Trajectories

Abstract

Map-matching is an important preprocessing task for many location-based services (LBS). It projects each GPS point in trajectory data onto digital maps. The state of art work typically employed the Hidden Markov model (HMM) by shortest path computation. Such shortest path computation may not work very well for very low sampling rate trajectory data, leading to low matching precision and high running time. To solve this problem, this paper, we first identify the frequent patterns from historical trajectory data and next perform the map matching for higher precision and faster running time. Since the identified frequent patterns indicate the mobility behaviours for the majority of trajectories, the map matching thus has chance to satisfy the matching precision with high confidence. Moreover, the proposed FP-forest structure can greatly speedup the lookup of frequent paths and lead to high computation efficiency. Our experiments on real world data set validate that the proposed FP-matching outperforms state of arts in terms of effectiveness and efficiency.