A Global-Voting Map Matching Algorithm on the Base of Taxi GPS Data

Since the existing floating car map-matching algorithms lead to high error rate when GPS data sampling rate is low, we propose a global-voting map matching algorithm. Based on floating car GPS track data, the algorithm do not only consider the influence to matching process caused by the topological information of road network but also the different spatial distance of GPS track data. In this matching algorithm, we device a new indicator to model the influence of geometric and topological information of road network and define a static matching matrix (SMM) as intermediate results. Based on the SMM, we define a distance weighted function to revise the SMM and build a dynamic matching matrix (DMM), and the function reflects the strength of the influence weighted by the distance between GPS points. After that, referring the DMM, we design an efficient voting algorithm to identify the optimal trajectory as map matching results. In this paper, we apply the algorithm to real Hangzhou taxi data. Results show that this map matching algorithm can make full use of existing information and perform well when GPS data sampling rate is low. Key-Words: floating car, taxi GPS data, low-sampling-rate, global-voting, map matching, topological information, road network