Anomaly detection on ITS data via view association

Junaidillah Fadlil, Hsing Kuo Pao, Yuh-Jye Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

We focus on detecting anomalous events in transportation systems. In transportation systems, other than normal road situation, anomalous events happen once in a while such as traffic accidents, ambulance car passing, harsh weather conditions, etc. Identifying the anomalous traffic events is essential because the events can lead to critical conditions where immediate investigation and recovery may be necessary. We propose an anomaly detection method for transportation systems where we create a police report automatically after detecting anomalies. Unlike the traditional police report, in this case, some quantitative analysis shall be done as well to provide experts with an advanced, precise and professional description of the anomalous event. For instance, we can provide the moment, the location as well as how severe the accident occurs in the upstream and downstream routes. We present an anomaly detection approach based on view association given multiple feature views on the transportation data if the views are more or less independent from each other. For each single view, anomalies are detected based on a manifold learning and hierarchical clustering procedures and anomalies from different views are associated and detected as anomalies with high confidence. We study two well-known ITS datasets which include the data from Mobile Century project and the PeMS dataset, and we evaluate the proposed method by comparing the automatically generated report and real report from police during the related period.

Original languageEnglish
Title of host publicationProceedings of the ACM SIGKDD Workshop on Outlier Detection and Description, ODD 2013
Pages22-30
Number of pages9
DOIs
StatePublished - 24 Dec 2013
EventACM SIGKDD Workshop on Outlier Detection and Description, ODD 2013 - Chicago, IL, United States
Duration: 11 Aug 201311 Aug 2013

Publication series

NameProceedings of the ACM SIGKDD Workshop on Outlier Detection and Description, ODD 2013

Conference

ConferenceACM SIGKDD Workshop on Outlier Detection and Description, ODD 2013
CountryUnited States
CityChicago, IL
Period11/08/1311/08/13

Keywords

  • Anomaly detection
  • Association
  • Intelligent transportation system (ITS)
  • Manifold learning
  • Trajectory

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  • Cite this

    Fadlil, J., Pao, H. K., & Lee, Y-J. (2013). Anomaly detection on ITS data via view association. In Proceedings of the ACM SIGKDD Workshop on Outlier Detection and Description, ODD 2013 (pp. 22-30). (Proceedings of the ACM SIGKDD Workshop on Outlier Detection and Description, ODD 2013). https://doi.org/10.1145/2500853.2500859