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Using Deep Learning to Detect Anomalies in Traffic Flow

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Intelligent Information and Database Systems (ACIIDS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13757))

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Abstract

Uncertainty is an ever present challenge in data analysis. In particular, it is important to detect, as precisely as possible, unforeseen phenomena. In this paper we study the usefulness of two deep learning based methods (CNN auto-encoder and BiLSTM auto-encoder) to detect anomalies in situations that can be defined in terms of time series. In order to evaluate our approaches, we consider traffic flow data and perform experiments in two orthogonal scenarios: a guided scenario (training only with data considered as ‘normal’ after a naïve labelling) and a basic scenario. Our results show that if we train the models using only the considered ‘normal’ data, the obtained models do not achieve good results because none of them are able to detect all type of abnormal data correctly. In contrast, both models can detect all type of time series anomalies when we consider the basic scenario.

This work has been supported by the Spanish MINECO/FEDER projects FAME (RTI2018-093608-B-C31) and AwESOMe (PID2021-122215NB-C31) and the Region of Madrid project FORTE-CM (S2018/TCS-4314) co-funded by EIE Funds of the European Union.

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Author information

Authors and Affiliations

  1. Universidad Complutense de Madrid, Madrid, Spain

    Manuel Méndez & Manuel Núñez

  2. SANO-Centre for Computational Medicine, Krakow, Poland

    Alfredo Ibias

Authors
  1. Manuel Méndez
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  2. Alfredo Ibias
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  3. Manuel Núñez
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Corresponding author

Correspondence to Manuel Núñez .

Editor information

Editors and Affiliations

  1. Wrocław University of Science and Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. Vietnam National University, Ho Chi Minh City, Ho Chi Minh City, Vietnam

    Tien Khoa Tran

  3. Al-Farabi Kazakh National University, Almaty, Kazakhstan

    Ualsher Tukayev

  4. National University of Kaohsiung, Kaohsiung, Taiwan

    Tzung-Pei Hong

  5. Wrocław University of Science and Technology, Wrocław, Poland

    Bogdan Trawiński

  6. University of Newcastle, Newcastle, NSW, Australia

    Edward Szczerbicki

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Méndez, M., Ibias, A., Núñez, M. (2022). Using Deep Learning to Detect Anomalies in Traffic Flow. In: Nguyen, N.T., Tran, T.K., Tukayev, U., Hong, TP., Trawiński, B., Szczerbicki, E. (eds) Intelligent Information and Database Systems. ACIIDS 2022. Lecture Notes in Computer Science(), vol 13757. Springer, Cham. https://doi.org/10.1007/978-3-031-21743-2_24

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  • DOI: https://doi.org/10.1007/978-3-031-21743-2_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-21742-5

  • Online ISBN: 978-3-031-21743-2

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