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CPM: Mining Converging Patterns from Moving Object Trajectories in Road Networks

Published: 13 November 2020 Publication History

Abstract

Group pattern mining from spatio-temporal trajectories of moving objects have gained significant attentions due to the prevalence of location-acquisition devices and tracking technologies. In this work, we propose a new group pattern, named converging, which is a group of moving objects that converge from different directions for a certain time period. Examples of convergings may include traffic jams, troop assembly, serious stampedes, and other public congregations. As a proof-of-concept, we implemented a visual analytic system CPM based on road-network constrained trajectories to detect converging events in road networks. A user-friendly interface is designed to help users gain insights into converging events from spatial and temporal aspects. Finally, we demonstrate the effectiveness and efficiency of our system by using a real dataset.

References

[1]
Yu Zheng. Trajectory data mining: An overview. ACM TIST, 6(3):29:1--29:41, 2015.
[2]
Martin Ester, Hans-Peter Kriegel, Jörg Sander, and Xiaowei Xu. A density-based algorithm for discovering clusters in large spatial databases with noise. In KDD 1996, pages 226--231, 1996.
[3]
Dimitris Papadias, Jun Zhang, Nikos Mamoulis, and Yufei Tao. Query processing in spatial network databases. In VLDB 2003, pages 802--813, 2003.
[4]
Bilong Shen, Ying Zhao, and Guoliang Li et. al. V-tree: Efficient knn search on moving objects with road-network constraints. In ICDE 2017, pages 609--620, 2017.
[5]
Zijian Li, Lei Chen, and Yue Wang. G*-tree: An efficient spatial index on road networks. In ICDE 2019, pages 268--279, 2019.

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cover image ACM Conferences
SIGSPATIAL '20: Proceedings of the 28th International Conference on Advances in Geographic Information Systems
November 2020
687 pages
ISBN:9781450380195
DOI:10.1145/3397536
Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 November 2020

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Author Tags

  1. Converging Pattern
  2. Nearest Neighbour Query
  3. Road Network

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  • Refereed limited

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SIGSPATIAL '20
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Overall Acceptance Rate 257 of 1,238 submissions, 21%

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