A Trajectory Collaboration Based Map Matching Approach for Low-Sampling-Rate GPS Trajectories
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 ma...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-04-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/7/2057 |
| _version_ | 1827719181710131200 |
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| author | Wentao Bian Ge Cui Xin Wang |
| author_facet | Wentao Bian Ge Cui Xin Wang |
| author_sort | Wentao Bian |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T20:38:33Z |
| format | Article |
| id | doaj.art-30dff9e1924147e6aa2cfbd89305260c |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T20:38:33Z |
| publishDate | 2020-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-30dff9e1924147e6aa2cfbd89305260c2023-11-19T20:49:53ZengMDPI AGSensors1424-82202020-04-01207205710.3390/s20072057A Trajectory Collaboration Based Map Matching Approach for Low-Sampling-Rate GPS TrajectoriesWentao Bian0Ge Cui1Xin Wang2School of Information Science and Technology, Northwest University, Xi’an 710127, ChinaIntelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan 430070, ChinaSchool of Information Science and Technology, Northwest University, Xi’an 710127, ChinaGPS (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.https://www.mdpi.com/1424-8220/20/7/2057map matchinglow-sampling-rate GPS trajectoriestrajectory collaborationtrajectory clustering |
| spellingShingle | Wentao Bian Ge Cui Xin Wang A Trajectory Collaboration Based Map Matching Approach for Low-Sampling-Rate GPS Trajectories Sensors map matching low-sampling-rate GPS trajectories trajectory collaboration trajectory clustering |
| title | A Trajectory Collaboration Based Map Matching Approach for Low-Sampling-Rate GPS Trajectories |
| title_full | A Trajectory Collaboration Based Map Matching Approach for Low-Sampling-Rate GPS Trajectories |
| title_fullStr | A Trajectory Collaboration Based Map Matching Approach for Low-Sampling-Rate GPS Trajectories |
| title_full_unstemmed | A Trajectory Collaboration Based Map Matching Approach for Low-Sampling-Rate GPS Trajectories |
| title_short | A Trajectory Collaboration Based Map Matching Approach for Low-Sampling-Rate GPS Trajectories |
| title_sort | trajectory collaboration based map matching approach for low sampling rate gps trajectories |
| topic | map matching low-sampling-rate GPS trajectories trajectory collaboration trajectory clustering |
| url | https://www.mdpi.com/1424-8220/20/7/2057 |
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