A Hidden Markov Model-Based Map Matching Algorithm for Low Sampling Rate Trajectory Data

Trajectory data of floating cars form an important data source for the studies on transportation, while map matching is always one essential step for them. Most map matching algorithms perform better with trajectory data of high sampling rates than with those of low sampling rates, but the latter can be commonly accessed because of their low cost. In this article, we proposed a map matching algorithm based on then hidden Markov Model. In this algorithm, we concerned both position and direction information for calculating observation and transition probabilities and solved the labelling problem with the Viterbi algorithm by maximizing the state sequence probabilities. We carried out a case study with the GPS trajectory data of floating cars and road network data of Wuhan. The results show that this algorithm can effectively match trajectory data of low sampling rates with the road network with good topology, and the correct rate can reach up to 86% within an acceptable time cost. In particular, it performs well even in some error-prone scenarios, such as two-way multiple parallel lanes, intersections, overpasses and roundabouts. Furthermore, we also discussed factors that might affect the accuracy and efficiency of this algorithm, particularly investigating the effect of topology correctness of the road network.