research-article

Similarity measurement of moving object trajectories

Authors:

Hechen Liu,

Markus SchneiderAuthors Info & Claims

IWGS '12: Proceedings of the 3rd ACM SIGSPATIAL International Workshop on GeoStreaming

Pages 19 - 22

https://doi.org/10.1145/2442968.2442971

Published: 06 November 2012 Publication History

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Abstract

To study the similarity between moving object trajectories is important in many applications, e.g., to find the clusters of moving objects which share the same moving pattern, and infer the future locations of a moving object from its similar trajectories. To define the similarity between moving objects is a challenging task, since not only their locations change but also their speed and semantic features vary. In this paper, we propose a novel approach to measure the similarity between trajectories. The similarity is defined based on both geographic and semantic features of movements. Our approach can be used to detect trajectory clusters and infer future locations of moving objects.

References

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Cited By

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Zhang GHu ZBâce MBulling A(2024)Mouse2Vec: Learning Reusable Semantic Representations of Mouse BehaviourProceedings of the CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642141(1-17)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642141
Song ROzmen MKim HMuller RCelik ZBianchi ACalandrino JTroncoso C(2023)Discovering adversarial driving maneuvers against autonomous vehiclesProceedings of the 32nd USENIX Conference on Security Symposium10.5555/3620237.3620403(2957-2974)Online publication date: 9-Aug-2023
https://dl.acm.org/doi/10.5555/3620237.3620403
Wang CZhao Y(2023)A Novel Approach for Trajectory Partition Privacy in Location-Based Services2023 IEEE 22nd International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)10.1109/TrustCom60117.2023.00200(1464-1469)Online publication date: 1-Nov-2023
https://doi.org/10.1109/TrustCom60117.2023.00200
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Index Terms

Similarity measurement of moving object trajectories
1. Human-centered computing
  1. Visualization
    1. Visualization application domains
      1. Geographic visualization
2. Information systems
  1. Information systems applications
    1. Spatial-temporal systems

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Published In

IWGS '12: Proceedings of the 3rd ACM SIGSPATIAL International Workshop on GeoStreaming

November 2012

131 pages

ISBN:9781450316958

DOI:10.1145/2442968

General Chairs:
Mohamed Ali
Microsoft
,
Farnoush Banaei-Kashani
USC
,
Erik Hoel
ESRI

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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: 06 November 2012

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SIGSPATIAL'12

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SIGSPATIAL

SIGSPATIAL'12: SIGSPATIAL 2012 International Conference on Advances in Geographic Information Systems

November 6, 2012

California, Redondo Beach

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Overall Acceptance Rate 7 of 9 submissions, 78%

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Cited By

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Zhang GHu ZBâce MBulling A(2024)Mouse2Vec: Learning Reusable Semantic Representations of Mouse BehaviourProceedings of the CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642141(1-17)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642141
Song ROzmen MKim HMuller RCelik ZBianchi ACalandrino JTroncoso C(2023)Discovering adversarial driving maneuvers against autonomous vehiclesProceedings of the 32nd USENIX Conference on Security Symposium10.5555/3620237.3620403(2957-2974)Online publication date: 9-Aug-2023
https://dl.acm.org/doi/10.5555/3620237.3620403
Wang CZhao Y(2023)A Novel Approach for Trajectory Partition Privacy in Location-Based Services2023 IEEE 22nd International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)10.1109/TrustCom60117.2023.00200(1464-1469)Online publication date: 1-Nov-2023
https://doi.org/10.1109/TrustCom60117.2023.00200
Reich THulbert DBudka M(2022)A Model Architecture for Public Transport Networks Using a Combination of a Recurrent Neural Network Encoder Library and a Attention MechanismAlgorithms10.3390/a1509032815:9(328)Online publication date: 14-Sep-2022
https://doi.org/10.3390/a15090328
Kaushik STiwari S(2021)EMD-Based Semantic User Similarity Using Past Travel HistoriesJournal of Cases on Information Technology10.4018/JCIT.20220701.oa224:3(1-17)Online publication date: 8-Oct-2021
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Seep JVahrenhold J(2021)K-means for semantically enriched trajectoriesProceedings of the 1st ACM SIGSPATIAL International Workshop on Animal Movement Ecology and Human Mobility10.1145/3486637.3489495(38-47)Online publication date: 2-Nov-2021
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Karami FMalek M(2021)Trajectory similarity measurement: An enhanced maximal travel match methodTransactions in GIS10.1111/tgis.1273325:3(1485-1503)Online publication date: 16-Feb-2021
https://doi.org/10.1111/tgis.12733
Luo QNiu CLiu JChou CHao MLan N(2021)Evaluation of Model-Based Biomimetic Control of Prosthetic Finger Force for GraspIEEE Transactions on Neural Systems and Rehabilitation Engineering10.1109/TNSRE.2021.310630429(1723-1733)Online publication date: 2021
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Mendoza-Silva GTorres-Sospedra JPotorti FMoreira AKnauth SBerkvens RHuerta J(2021)Beyond Euclidean Distance for Error Measurement in Pedestrian Indoor LocationIEEE Transactions on Instrumentation and Measurement10.1109/TIM.2020.302151470(1-11)Online publication date: 2021
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Tao YBoth ASilveira RBuchin KSijben SPurves RLaube PPeng DToohey KDuckham M(2021)A comparative analysis of trajectory similarity measuresGIScience & Remote Sensing10.1080/15481603.2021.190892758:5(643-669)Online publication date: 23-Jun-2021
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