Trajectory Clustering in an Intersection by GDTW

GPS trajectory data in intersections are series data with different lengths. Dynamic time wrapping (DTW) is good to measure the similarity between series with different lengths, however, traditional DTW could not deal with the inclusive relationship well between series. We propose a unified generali...

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Main Authors: Lei Gao, Lu Wei, Jian Yang, Jinhong Li
Format: Article
Language:English
Published: Wiley 2022-01-01
Series:Journal of Advanced Transportation
Online Access:http://dx.doi.org/10.1155/2022/5978704
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author Lei Gao
Lu Wei
Jian Yang
Jinhong Li
author_facet Lei Gao
Lu Wei
Jian Yang
Jinhong Li
author_sort Lei Gao
collection DOAJ
description GPS trajectory data in intersections are series data with different lengths. Dynamic time wrapping (DTW) is good to measure the similarity between series with different lengths, however, traditional DTW could not deal with the inclusive relationship well between series. We propose a unified generalized DTW algorithm (GDTW) by extending the boundary constraint and continuity constraint of DTW and using the weighted local distance to normalize the cumulative distance. Based on the density peak clustering algorithm DPCA using asymmetric GDTW to measure the similarity of two trajectories, we propose an improved DPCA algorithm (ADPC) to adopt this asymmetric similarity measurement. In experiments using the proposed method, the number of clusters is reduced.
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issn 2042-3195
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series Journal of Advanced Transportation
spelling doaj-art-b9effa4931ff4872a51129bac2d3ea2d2025-08-20T03:22:58ZengWileyJournal of Advanced Transportation2042-31952022-01-01202210.1155/2022/5978704Trajectory Clustering in an Intersection by GDTWLei Gao0Lu Wei1Jian Yang2Jinhong Li3Beijing Key Lab of Urban Road Traffic Intelligent TechnologyBeijing Key Lab of Urban Road Traffic Intelligent TechnologyBeijing Key Lab of Urban Road Traffic Intelligent TechnologyBeijing Key Lab of Urban Road Traffic Intelligent TechnologyGPS trajectory data in intersections are series data with different lengths. Dynamic time wrapping (DTW) is good to measure the similarity between series with different lengths, however, traditional DTW could not deal with the inclusive relationship well between series. We propose a unified generalized DTW algorithm (GDTW) by extending the boundary constraint and continuity constraint of DTW and using the weighted local distance to normalize the cumulative distance. Based on the density peak clustering algorithm DPCA using asymmetric GDTW to measure the similarity of two trajectories, we propose an improved DPCA algorithm (ADPC) to adopt this asymmetric similarity measurement. In experiments using the proposed method, the number of clusters is reduced.http://dx.doi.org/10.1155/2022/5978704
spellingShingle Lei Gao
Lu Wei
Jian Yang
Jinhong Li
Trajectory Clustering in an Intersection by GDTW
Journal of Advanced Transportation
title Trajectory Clustering in an Intersection by GDTW
title_full Trajectory Clustering in an Intersection by GDTW
title_fullStr Trajectory Clustering in an Intersection by GDTW
title_full_unstemmed Trajectory Clustering in an Intersection by GDTW
title_short Trajectory Clustering in an Intersection by GDTW
title_sort trajectory clustering in an intersection by gdtw
url http://dx.doi.org/10.1155/2022/5978704
work_keys_str_mv AT leigao trajectoryclusteringinanintersectionbygdtw
AT luwei trajectoryclusteringinanintersectionbygdtw
AT jianyang trajectoryclusteringinanintersectionbygdtw
AT jinhongli trajectoryclusteringinanintersectionbygdtw