research-article

Model-driven matching and segmentation of trajectories

Authors:

Swaminathan Sankararaman,

Pankaj K. Agarwal,

Thomas Mølhave,

Arnold P. BoedihardjoAuthors Info & Claims

SIGSPATIAL'13: Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

Pages 234 - 243

https://doi.org/10.1145/2525314.2525360

Published: 05 November 2013 Publication History

Abstract

A fundamental problem in analyzing trajectory data is to identify common patterns between pairs or among groups of trajectories. In this paper, we consider the problem of matching similar portions between a pair of trajectories, each observed as a sequence of points sampled from it. We present new measures of trajectory similarity --- both local and global --- between a pair of trajectories to distinguish between similar and dissimilar portions. We then use this model to perform segmentation of a set of trajectories into fragments, contiguous portions of trajectories shared by many of them.

Our model for similarity is robust under noise and sampling rate variations. The model also yields a score which can be used to rank multiple pairs of trajectories according to similarity, e.g. in clustering applications. We present quadratic time algorithms to compute the similarity between trajectory pairs under our measures together with algorithms to identify fragments in a large set of trajectories efficiently using the similarity model.

Finally, we present an extensive experimental study evaluating the effectiveness of our approach on real datasets, comparing it with earlier approaches. Our experiments show that our model for similarity is highly accurate in distinguishing similar and dissimilar portions as compared to earlier methods even with sparse sampling. Further, our segmentation algorithm is able to identify a small set of fragments capturing the common parts of trajectories in the dataset.

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Cited By

Chen YYu PChen WZheng ZGuo M(2023)Meta-Learning Based Classification for Moving Object Trajectories in Mobile IoTIEEE Transactions on Big Data10.1109/TBDATA.2022.31958619:2(584-596)Online publication date: 1-Apr-2023
https://doi.org/10.1109/TBDATA.2022.3195861
Chen YYu PChen WZheng ZGuo M(2022)Embedding-Based Similarity Computation for Massive Vehicle Trajectory DataIEEE Internet of Things Journal10.1109/JIOT.2021.31073279:6(4650-4660)Online publication date: 15-Mar-2022
https://doi.org/10.1109/JIOT.2021.3107327
Fox KLi X(2022)Approximating the Geometric Edit DistanceAlgorithmica10.1007/s00453-022-00966-484:9(2395-2413)Online publication date: 25-Apr-2022
https://doi.org/10.1007/s00453-022-00966-4
Show More Cited By

Index Terms

Model-driven matching and segmentation of trajectories
1. Information systems
  1. Information systems applications
    1. Data mining
2. Theory of computation
  1. Randomness, geometry and discrete structures

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Published In

cover image ACM Conferences

SIGSPATIAL'13: Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 2013

598 pages

ISBN:9781450325219

DOI:10.1145/2525314

General Chairs:
Craig Knoblock
University of Southern California
,
Markus Schneider
University of Florida
,
Program Chairs:
Peer Kröger
Ludwig-Maximilians-Universität München, Germany
,
John Krumm
Microsoft Research
,
Peter Widmayer
ETH Zürich, Switzerland

Copyright © 2013 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGSPATIAL: ACM Special Interest Group on Spatial Information

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SIGGRAPH: ACM Special Interest Group on Computer Graphics and Interactive Techniques

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 November 2013

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SIGSPATIAL'13

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SIGSPATIAL

SIGSPATIAL'13: 21st SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 5 - 8, 2013

Florida, Orlando

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Overall Acceptance Rate 257 of 1,238 submissions, 21%

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Cited By

Chen YYu PChen WZheng ZGuo M(2023)Meta-Learning Based Classification for Moving Object Trajectories in Mobile IoTIEEE Transactions on Big Data10.1109/TBDATA.2022.31958619:2(584-596)Online publication date: 1-Apr-2023
https://doi.org/10.1109/TBDATA.2022.3195861
Chen YYu PChen WZheng ZGuo M(2022)Embedding-Based Similarity Computation for Massive Vehicle Trajectory DataIEEE Internet of Things Journal10.1109/JIOT.2021.31073279:6(4650-4660)Online publication date: 15-Mar-2022
https://doi.org/10.1109/JIOT.2021.3107327
Fox KLi X(2022)Approximating the Geometric Edit DistanceAlgorithmica10.1007/s00453-022-00966-484:9(2395-2413)Online publication date: 25-Apr-2022
https://doi.org/10.1007/s00453-022-00966-4
Boonchoo TAo XHe Q(2019)Multi-Aspect Embedding for Attribute-Aware TrajectoriesSymmetry10.3390/sym1109114911:9(1149)Online publication date: 10-Sep-2019
https://doi.org/10.3390/sym11091149
Arslan MCruz CGinhac D(2019)Semantic enrichment of spatio-temporal trajectories for worker safety on construction sitesPersonal and Ubiquitous Computing10.1007/s00779-018-01199-5Online publication date: 30-Jan-2019
https://doi.org/10.1007/s00779-018-01199-5
Dan TLuo CLi YZhang C(2019)Trajectory Similarity Join for Spatial Temporal DatabaseDatabase and Expert Systems Applications10.1007/978-3-030-27618-8_23(306-321)Online publication date: 6-Aug-2019
https://doi.org/10.1007/978-3-030-27618-8_23
Agarwal PFox KMunagala KNath APan JTaylor EVan den Bussche JArenas M(2018)Subtrajectory ClusteringProceedings of the 37th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems10.1145/3196959.3196972(75-87)Online publication date: 27-May-2018
https://dl.acm.org/doi/10.1145/3196959.3196972
Li XZhao KCong GJensen CWei W(2018)Deep Representation Learning for Trajectory Similarity Computation2018 IEEE 34th International Conference on Data Engineering (ICDE)10.1109/ICDE.2018.00062(617-628)Online publication date: Apr-2018
https://doi.org/10.1109/ICDE.2018.00062
Schmitt JBaldo F(2018)A Method to Suggest Alternative Routes Based on Analysis of Automobiles' Trajectories2018 XLIV Latin American Computer Conference (CLEI)10.1109/CLEI.2018.00059(436-444)Online publication date: Oct-2018
https://doi.org/10.1109/CLEI.2018.00059
Alewijnse SBuchin KBuchin MSijben SWestenberg M(2018)Model-Based Segmentation and Classification of TrajectoriesAlgorithmica10.1007/s00453-017-0329-x80:8(2422-2452)Online publication date: 1-Aug-2018
https://dl.acm.org/doi/10.1007/s00453-017-0329-x
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