Abstract: Map matching is the process of recognizing the movement track of moving objects (mainly vehicles,pedes-trians) in the road network according to the discrete sampling location data (GPS coordinates).It is a necessary processing step for many relevant applications such as GPS trajectory data analysis and position analysis.This paper proposed an improved map matching algorithm based on interactive voting to solve the problems that the existing map matching algorithms have low accuracy and efficiency for low sampling trajectory data.The main contributions of the proposed algorithm are as follows.Firstly,the proposed algorithm considers not only the spatial distances between sampling points,road topology and road segment speed limits,but also the real-time moving direction and speed of each GPS point to improve the matching accuracy.Secondly,a filter based on driving direction and speed limits is introduced to filter out noisy candidates,thus improving the efficiency of the algorithm.To evaluate the performance of the proposed algorithm,two real-world datasets were used to compare the proposed algorithm with the existing IVMM algorithm and the AIVMM algorithm.Experimental results show that the proposed algorithm outperforms the existing two algorithms in terms of matching accuracy and efficiency.

Key words: Constraints, Direction, Low sampling rate, Map matching