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On mining anomalous patterns in road traffic streams

Published: 17 December 2011 Publication History

Abstract

Large number of taxicabs in major metropolitan cities are now equipped with a GPS device. Since taxis are on the road nearly twenty four hours a day (with drivers changing shifts), they can now act as reliable sensors to monitor the behavior of traffic. In this paper we use GPS data from taxis to monitor the emergence of unexpected behavior in the Beijing metropolitan area. We adapt likelihood ratio tests (LRT) which have previously been mostly used in epidemiological studies to describe traffic patterns. To the best of our knowledge the use of LRT in traffic domain is not only novel but results in very accurate and rapid detection of anomalous behavior.

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Cited By

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  • (2020)Discovering Graph Functional DependenciesACM Transactions on Database Systems10.1145/339719845:3(1-42)Online publication date: 11-Sep-2020
  • (2020)Scalable Spatial Scan Statistics for TrajectoriesACM Transactions on Knowledge Discovery from Data10.1145/339404614:6(1-24)Online publication date: 28-Sep-2020
  • (2019)Accurate Detection of Road Network Anomaly by Understanding Crowd's Driving Strategies from Human MobilityACM Transactions on Spatial Algorithms and Systems10.1145/33259135:2(1-17)Online publication date: 8-Aug-2019
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Published In

cover image Guide Proceedings
ADMA'11: Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part II
December 2011
416 pages
ISBN:9783642258558
  • Editors:
  • Jie Tang,
  • Irwin King,
  • Ling Chen,
  • Jianyong Wang

Sponsors

  • Tsinghua University: Tsinghua University
  • IBMR: IBM Research
  • China Samsung Telecom R&D Center: China Samsung Telecom R&D Center

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 17 December 2011

Author Tags

  1. emerging
  2. persistent
  3. spatio-temporal outlier
  4. upper-bounding

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Cited By

View all
  • (2020)Discovering Graph Functional DependenciesACM Transactions on Database Systems10.1145/339719845:3(1-42)Online publication date: 11-Sep-2020
  • (2020)Scalable Spatial Scan Statistics for TrajectoriesACM Transactions on Knowledge Discovery from Data10.1145/339404614:6(1-24)Online publication date: 28-Sep-2020
  • (2019)Accurate Detection of Road Network Anomaly by Understanding Crowd's Driving Strategies from Human MobilityACM Transactions on Spatial Algorithms and Systems10.1145/33259135:2(1-17)Online publication date: 8-Aug-2019
  • (2019)BuScopeProceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services10.1145/3307334.3326091(41-53)Online publication date: 12-Jun-2019
  • (2019)Spatiotemporal traffic network analysisKnowledge and Information Systems10.1007/s10115-018-1225-760:1(25-61)Online publication date: 1-Jul-2019
  • (2018)Outlier Detection in Urban Traffic DataProceedings of the 8th International Conference on Web Intelligence, Mining and Semantics10.1145/3227609.3227692(1-12)Online publication date: 25-Jun-2018
  • (2018)Detecting Urban Anomalies Using Multiple Spatio-Temporal Data SourcesProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/31917862:1(1-18)Online publication date: 26-Mar-2018
  • (2017)Driver behaviour detection and vehicle rating using multi-UAV coordinated vehicular networksJournal of Computer and System Sciences10.1016/j.jcss.2016.10.00386:C(3-32)Online publication date: 1-Jun-2017
  • (2016)Identifying Region-Wide Functions Using Urban Taxicab TrajectoriesACM Transactions on Embedded Computing Systems10.1145/282150715:2(1-19)Online publication date: 11-Mar-2016
  • (2015)Detecting collective anomalies from multiple spatio-temporal datasets across different domainsProceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems10.1145/2820783.2820813(1-10)Online publication date: 3-Nov-2015
  • Show More Cited By

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