MCM: A Robust Map Matching Method by Tracking Multiple Road Candidates

Wanting Li, Yongcai Wang, Deying Li, Xiaojia Xu

Abstract: Map matching is to track the positions of vehicles on the road network based on the positions provided by GPS (Global Positioning System). Balancing localization accuracy and computation efficiency is a key problem in online map matching, for which, existing methods HMM and MHT mainly use Markov assumption which drops early unused data. Although the roads to explore can be remarkably reduced by the Markov assumption, miss-of-match and matching breaks may occur if the GPS data is highly noisy. To address these problems, this paper presents Multiple Candidate Matching (MCM) to improve the robustness of map matching by using historical trajectory data. MCM tracks multiple road candidates in the map matching process while limiting the number of road candidates by excluding the routes whose likelihood are below a threshold. Numerical experiments in large-scale data sets show that MCM is very promising in terms of accuracy, computational efficiency, and robustness. Mismatching problems caused by Markov assumption can be resolved effectively when compared with state-of-the-art online map matching methods.