Anomaly detection in smart city traffic based on time series analysis

Mohammad Bawaneh, Vilmos Simon

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

Abstract

Anomaly detection in city traffic is playing a key role in intelligent transportation systems. Anomalies can be caused by different factors, such as accidents, extreme weather conditions or rush hours. In this paper, we propose a method which can detect anomalies in city traffic by analyzing the historical dataset collected from smart city sensors. The proposed Occupancy based anomaly detection algorithm (OBADA) is analyzing occupancy data of the roads, by searching for subsequence of major changes in values in the occupancy's time series which reflects an inordinate behavior. This was done by transforming the time series with a derivative estimation model, into a symbolic representation sequence. To detect the anomalies in the symbolic sequence, the modified z-score method was used. We have also introduced an enhancement by proposing a majority voting technique (OBADA_MV). The OBADA algorithm was evaluated using a historical dataset generated by the Simulation of Urban Mobility (SUMO) framework. By studying four different congestion scenarios, the results have shown that our algorithm can identify anomalies with more than 95% accuracy. OBADA was evaluated by comparing with other methods as well. The results have shown that OBADA anomalies Detection Rate (DR) is 100% and False Alarm Rate (FAR) is 0% which outperformed other methods, but this requires a higher time for detection.

Original languageEnglish
Title of host publication2019 27th International Conference on Software, Telecommunications and Computer Networks, SoftCOM 2019
EditorsDinko Begusic, Nikola Rozic, Josko Radic, Matko Saric
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9789532900880
DOIs
Publication statusPublished - Sep 2019
Event27th International Conference on Software, Telecommunications and Computer Networks, SoftCOM 2019 - Split, Croatia
Duration: Sep 19 2019Sep 21 2019

Publication series

Name2019 27th International Conference on Software, Telecommunications and Computer Networks, SoftCOM 2019

Conference

Conference27th International Conference on Software, Telecommunications and Computer Networks, SoftCOM 2019
CountryCroatia
CitySplit
Period9/19/199/21/19

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Keywords

  • Anomaly Detection
  • Big Data
  • Data Analysis
  • Intelligent Transportation Systems
  • Time Series

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Hardware and Architecture
  • Signal Processing
  • Software
  • Computer Networks and Communications

Cite this

Bawaneh, M., & Simon, V. (2019). Anomaly detection in smart city traffic based on time series analysis. In D. Begusic, N. Rozic, J. Radic, & M. Saric (Eds.), 2019 27th International Conference on Software, Telecommunications and Computer Networks, SoftCOM 2019 [8903822] (2019 27th International Conference on Software, Telecommunications and Computer Networks, SoftCOM 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/SOFTCOM.2019.8903822