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International Conference on Discovery Science

DS 2020: Discovery Science pp 674–688Cite as

Spatiotemporal Traffic Anomaly Detection on Urban Road Network Using Tensor Decomposition Method

Part of the Lecture Notes in Computer Science book series (LNAI,volume 12323)

Abstract

Tensor-based models emerged only recently in modeling and analysis of the spatiotemporal road traffic data. They outperform other data models regarding the property of simultaneously capturing both spatial and temporal components of the observed traffic dataset. In this paper, the nonnegative tensor decomposition method is used to extract traffic patterns in the form of Speed Transition Matrix (STM). The STM is presented as the approach for modeling the large sparse Floating Car Data (FCD). The anomaly of the traffic pattern is estimated using Kullback–Leibler divergence between the observed traffic pattern and the average traffic pattern. Experiments were conducted on the large sparse FCD dataset for the most relevant road segments in the City of Zagreb, which is the capital and largest city in Croatia. Results show that the method was able to detect the most anomalous traffic road segments, and with analysis of the extracted spatial and temporal components, conclusions could be drawn about the causes of the anomalies. Results are validated by using the domain knowledge from the Highway Capacity Manual and achieved a precision score value of more than 90%. Therefore, such valuable traffic information can be used in routing applications and urban traffic planning.

Keywords

  • Road traffic anomaly detection
  • Tensor decomposition methods
  • Speed probability distribution
  • Intelligent transport systems
  • Traffic state estimation

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  • DOI: 10.1007/978-3-030-61527-7_44
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Acknowledgment

This research has been supported by the European Regional Development Fund under the grant KK.01.1.1.01.0009 (DATACROSS). Data used for this research is collected during the SORDITO project (RC.2.2.08-0022). Authors are also very grateful to industrial partner MIREO Inc. Sofia Fernandes acknowledges the support of FCT (Fundação para a Ciência e a Tecnologia) via the PhD scholarship PD/BD/114189/2016.

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Authors and Affiliations

  1. Department of Intelligent Transport Systems, Faculty of Transport and Traffic Sciences, University of Zagreb, Zagreb, Croatia

    Leo Tišljarić & Tonči Carić

  2. LIAAD - INESC TEC, University of Porto, Porto, Portugal

    Sofia Fernandes & João Gama

Authors
  1. Leo Tišljarić
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  2. Sofia Fernandes
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  3. Tonči Carić
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  4. João Gama
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Corresponding author

Correspondence to Leo Tišljarić .

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Editors and Affiliations

  1. University of Bari Aldo Moro, Bari, Italy

    Dr. Annalisa Appice

  2. Aristotle University of Thessaloniki, Thessaloniki, Greece

    Prof. Grigorios Tsoumakas

  3. Open University of Cyprus, Nicosia, Cyprus

    Prof. Dr. Yannis Manolopoulos

  4. Dalhousie University, Halifax, NS, Canada

    Prof. Stan Matwin

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Tišljarić, L., Fernandes, S., Carić, T., Gama, J. (2020). Spatiotemporal Traffic Anomaly Detection on Urban Road Network Using Tensor Decomposition Method. In: Appice, A., Tsoumakas, G., Manolopoulos, Y., Matwin, S. (eds) Discovery Science. DS 2020. Lecture Notes in Computer Science(), vol 12323. Springer, Cham. https://doi.org/10.1007/978-3-030-61527-7_44

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  • DOI: https://doi.org/10.1007/978-3-030-61527-7_44

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