Optimization of Map Matching Algorithm in Various Road Conditions

Abstract

Map matching refers to find the real driving trajectory of the vehicle by its GPS data. To solve this problem, most research focuses on improving the accuracy of GPS positioning. Even so, map matching still has a large deviation in complex road sections, such as interchanges, underground tunnels and other areas with high spatial complexity. At the same time, traditional map matching algorithms, such as hidden Markov model (HMM) and nearest neighbor algorithms, have the disadvantage of long running time. Different from the research on improving the accuracy of positioning, this research focuses on optimizing and creating a new algorithm for map matching. In order to solve the accuracy of map matching under complex road conditions, this paper adds a new attribute of altitude to GPS data for the first time, and greatly improves the accuracy of map matching in complex road conditions by constructing a three-dimensional hidden Markov model (3D HMM). In order to reduce the running time of the algorithm, this paper uses graph convolutional neural network as a bridge, integrates a variety of map matching algorithms and constructs a hybrid algorithm. Through experiments, it is found that the running time and accuracy of this hybrid algorithm are better than other algorithms.