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Open AccessArticle

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

by Wentao Bian 1, Ge Cui 2,3,* and Xin Wang 1,3
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
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(7), 2057; https://doi.org/10.3390/s20072057
Received: 10 February 2020 / Revised: 1 April 2020 / Accepted: 3 April 2020 / Published: 6 April 2020
(This article belongs to the Section State-of-the-Art Sensors Technologies)
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. View Full-Text
Keywords: map matching; low-sampling-rate GPS trajectories; trajectory collaboration; trajectory clustering map matching; low-sampling-rate GPS trajectories; trajectory collaboration; trajectory clustering
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Bian, W.; Cui, G.; Wang, X. A Trajectory Collaboration Based Map Matching Approach for Low-Sampling-Rate GPS Trajectories. Sensors 2020, 20, 2057.

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