An Incremental Map Matching Approach with Speed Estimation Constraints for High Sampling Rate Vehicle Trajectories

Liping Huang, Sida Zhao, Ruikang Luo, Rong Su, Mohit Sindhwani, Siew Kiat Chan, George Rosario Dhinesh

Abstract: Map matching of vehicle trajectories is to identify the correct link in the road network for each positioning point of a trajectory. A sampling period of fewer than 10 seconds for each position point is typically regarded as a high sampling rate trajectory. Existing algorithms are mainly targeted to the low-sampling-rate trajectories, which may ignore much useful information from high-sampling-rate trajectories. Till now, no studies explore the validity of such algorithms when we feed them the high-sampling-rate trajectories, which is the target of this paper. For alleviating the positioning error influence and speeding up the matching process for high sampling rate trajectories, a batch matching strategy is studied to simultaneously match a subsequence of a trajectory. Specifically, we first estimate the mean and median speeds of a trajectory and the current speed of the GPS point. To make sure that the upcoming subsequence to be matched is on the same road segment, we utilize the speed estimations as constraints for determining the subsequence size together with the local features of a road network. Accordingly, an incremental map matching algorithm is further in this study. Experimental results on real-world datasets of high sampling rate vehicle trajectories demonstrate that the proposed algorithm outperforms the algorithm that is designed for the low-sampling-rate trajectory.