Calibrating Large Scale Vehicle Trajectory Data
An accurate and sufficient vehicle trajectory dataset is the basis to many trajectory-based data mining tasks and applications. However, vehicle trajectories sampled by GPS devices are usually at a relatively low sampling rate and contain notable location errors. To address these two problems in GPS...
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sg-smu-ink.larc-10042017-03-03T07:49:55Z Calibrating Large Scale Vehicle Trajectory Data Liu, Siyuan Liu, Ce Luo, Qiong Ni, Lionel Krishnan, Ramayya An accurate and sufficient vehicle trajectory dataset is the basis to many trajectory-based data mining tasks and applications. However, vehicle trajectories sampled by GPS devices are usually at a relatively low sampling rate and contain notable location errors. To address these two problems in GPS trajectory data, we propose WI-matching, the first vehicle trajectory calibration framework to take advantage of road networks topology and geometry information and trajectory historical information in large scale. WI-matching consists of a Weighting based map matching algorithm and a trajectory interpolation based matching algorithm. In our WImatching framework, we first integrate the vehicle GPS data with digital road networks data, to identify the roads where a vehicle traveled and the vehicle locations along the roads. Then our weighting-based map matching algorithm considers (1) the geometric and topological information of the road networks and (2) the spatiotemporal trajectory information to efficiently and effectively calibrate the GPS data points. Finally, our interpolation algorithm identifies paths between consecutive GPS points, and adds points with estimated vehicle status (location and time stamp) along the paths to construct sufficient vehicle trajectories. We have evaluated our algorithms on a large-scale real life data set in comparison with the state of the art. Our extensive and empirical results indicate that our WI-matching achieves a high accuracy as well as a high efficiency on real-world data which beats the state of the art. 2012-07-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/larc/5 https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=1004&context=larc http://creativecommons.org/licenses/by-nc-nd/4.0/ LARC Research Publications eng Institutional Knowledge at Singapore Management University Calibration vehicle trajectory map matching Computer Sciences |
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Calibration vehicle trajectory map matching Computer Sciences Liu, Siyuan Liu, Ce Luo, Qiong Ni, Lionel Krishnan, Ramayya Calibrating Large Scale Vehicle Trajectory Data |
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An accurate and sufficient vehicle trajectory dataset is the basis to many trajectory-based data mining tasks and applications. However, vehicle trajectories sampled by GPS devices are usually at a relatively low sampling rate and contain notable location errors. To address these two problems in GPS trajectory data, we propose WI-matching, the first vehicle trajectory calibration framework to take advantage of road networks topology and geometry information and trajectory historical information in large scale. WI-matching consists of a Weighting based map matching algorithm and a trajectory interpolation based matching algorithm. In our WImatching framework, we first integrate the vehicle GPS data with digital road networks data, to identify the roads where a vehicle traveled and the vehicle locations along the roads. Then our weighting-based map matching algorithm considers (1) the geometric and topological information of the road networks and (2) the spatiotemporal trajectory information to efficiently and effectively calibrate the GPS data points. Finally, our interpolation algorithm identifies paths between consecutive GPS points, and adds points with estimated vehicle status (location and time stamp) along the paths to construct sufficient vehicle trajectories. We have evaluated our algorithms on a large-scale real life data set in comparison with the state of the art. Our extensive and empirical results indicate that our WI-matching achieves a high accuracy as well as a high efficiency on real-world data which beats the state of the art. |
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Liu, Siyuan Liu, Ce Luo, Qiong Ni, Lionel Krishnan, Ramayya |
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Liu, Siyuan Liu, Ce Luo, Qiong Ni, Lionel Krishnan, Ramayya |
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Liu, Siyuan |
title |
Calibrating Large Scale Vehicle Trajectory Data |
title_short |
Calibrating Large Scale Vehicle Trajectory Data |
title_full |
Calibrating Large Scale Vehicle Trajectory Data |
title_fullStr |
Calibrating Large Scale Vehicle Trajectory Data |
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Calibrating Large Scale Vehicle Trajectory Data |
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calibrating large scale vehicle trajectory data |
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Institutional Knowledge at Singapore Management University |
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2012 |
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https://ink.library.smu.edu.sg/larc/5 https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=1004&context=larc |
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