A Trajectory Collaboration Based Map Matching Approach for Low-Sampling-Rate GPS Trajectories

Wentao Bian; Ge Cui; Xin Wang

doi:10.3390/s20072057

A Trajectory Collaboration Based Map Matching Approach for Low-Sampling-Rate GPS Trajectories

Sensors (Basel). 2020 Apr 6;20(7):2057. doi: 10.3390/s20072057.

Authors

Wentao Bian¹, Ge Cui^{2

3}, Xin Wang^{1

3}

Affiliations

¹ School of Information Science and Technology, Northwest University, Xi'an 710127, China.
² Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan 430070, China.
³ Department of Geomatics Engineering, University of Calgary, Calgary, AB T2N1N4, Canada.

Abstract

GPS (Global Positioning System) trajectories with low sampling rates are prevalent in many applications. However, current map matching methods do not perform well for low-sampling-rate GPS trajectories due to the large uncertainty between consecutive GPS points. In this paper, a collaborative map matching method (CMM) is proposed for low-sampling-rate GPS trajectories. CMM processes GPS trajectories in batches. First, it groups similar GPS trajectories into clusters and then supplements the missing information by resampling. A collaborative GPS trajectory is then extracted for each cluster and matched to the road network, based on longest common subsequence (LCSS) distance. Experiments are conducted on a real GPS trajectory dataset and a simulated GPS trajectory dataset. The results show that the proposed CMM outperforms the baseline methods in both, effectiveness and efficiency.

Keywords: low-sampling-rate GPS trajectories; map matching; trajectory clustering; trajectory collaboration.

Abstract

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