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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Main Authors: Liu, Siyuan, Liu, Ce, Luo, Qiong, Ni, Lionel, Krishnan, Ramayya
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Language:English
Published: Institutional Knowledge at Singapore Management University 2012
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Online Access: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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spelling 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
institution Singapore Management University
building SMU Libraries
country Singapore
collection InK@SMU
language English
topic Calibration
vehicle trajectory
map matching
Computer Sciences
spellingShingle Calibration
vehicle trajectory
map matching
Computer Sciences
Liu, Siyuan
Liu, Ce
Luo, Qiong
Ni, Lionel
Krishnan, Ramayya
Calibrating Large Scale Vehicle Trajectory Data
description 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.
format text
author Liu, Siyuan
Liu, Ce
Luo, Qiong
Ni, Lionel
Krishnan, Ramayya
author_facet Liu, Siyuan
Liu, Ce
Luo, Qiong
Ni, Lionel
Krishnan, Ramayya
author_sort 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
title_full_unstemmed Calibrating Large Scale Vehicle Trajectory Data
title_sort calibrating large scale vehicle trajectory data
publisher Institutional Knowledge at Singapore Management University
publishDate 2012
url 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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