A robust map matching method by considering memorized multiple matching candidates

Abstract

Map matching is to track the positions of vehicles on the road network based on the positions provided by GPS (Global Positioning System) devices. Balancing localization accuracy and computation efficiency is a key problem in map matching. Existing methods mainly use Hidden Markov Model (HMM) or historical transportation data to learn the transitional probabilities among road segments. 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, and the transitional model needs to be learned offline. To address these problems, this paper presents Multiple Candidate Matching (MCM) to improve the robustness of map matching. MCM doesn't need to pre-train the transitional model nor the historical transportation information. MCM memorizes multiple historical matching candidates in the map matching process. It votes among historical matchings and current matchings, but generates limited number of road candidates in real-time to restrict the computation complexity. MCM for both online map matching and offline map matching are presented and their properties are analyzed theoretically and experimentally. Numerical experiments in large-scale data sets show that MCM is very promising in terms of accuracy, computational efficiency, and robustness. The matching break and miss-of-match problems can be resolved effectively when compared with the state-of-the-art map matching methods. Codes are outsourced at https://github.com/lindalee-inlab/MCM.