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Anomalous Vehicle Recognition in Smart Urban Traffic Monitoring as an Edge Service

Author

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  • Ning Chen

    (Department of Electrical and Computer Engineering, Binghamton University, State University of New York, Binghamton, NY 13905, USA)

  • Yu Chen

    (Department of Electrical and Computer Engineering, Binghamton University, State University of New York, Binghamton, NY 13905, USA)

Abstract

The past decades witnessed an unprecedented urbanization and the proliferation of modern information and communication technologies (ICT), which makes the concept of Smart City feasible. Among various intelligent components, smart urban transportation monitoring is an essential part of smoothly operational smart cities. Although there is fast development of Smart Cities and the growth of Internet of Things (IoT), real-time anomalous behavior detection in Intelligent Transportation Systems (ITS) is still challenging. Because of multiple advanced features including flexibility, safety, and ease of manipulation, quadcopter drones have been widely adopted in many areas, from service improvement to urban surveillance, and data collection for scientific research. In this paper, a Smart Urban traffic Monitoring (SurMon) scheme is proposed employing drones following an edge computing paradigm. A dynamic video stream processing scheme is proposed to meet the requirements of real-time information processing and decision-making at the edge. Specifically, we propose to identify anomalous vehicle behaviors in real time by creatively applying the multidimensional Singular Spectrum Analysis (mSSA) technique in space to detect the different vehicle behaviors on roads. Multiple features of vehicle behaviors are fed into channels of the mSSA procedure. Instead of trying to create and define a database of normal activity patterns of vehicles on the road, the anomaly detection is reformatted as an outlier identifying problem. Then, a cascaded Capsules Network is designed to predict whether the behavior is a violation. An extensive experimental study has been conducted and the results have validated the feasibility and effectiveness of the SurMon scheme.

Suggested Citation

  • Ning Chen & Yu Chen, 2022. "Anomalous Vehicle Recognition in Smart Urban Traffic Monitoring as an Edge Service," Future Internet, MDPI, vol. 14(2), pages 1-22, February.
    • Handle: RePEc:gam:jftint:v:14:y:2022:i:2:p:54-:d:746310
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    References listed on IDEAS

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    1. Ullah, Fahim & Qayyum, Siddra & Thaheem, Muhammad Jamaluddin & Al-Turjman, Fadi & Sepasgozar, Samad M.E., 2021. "Risk management in sustainable smart cities governance: A TOE framework," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
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    Cited by:

    1. Yousef-Awwad Daraghmi & Mamoun Abu Helou & Eman-Yasser Daraghmi & Waheeb Abu-ulbeh, 2022. "IoT-Based System for Improving Vehicular Safety by Continuous Traffic Violation Monitoring," Future Internet, MDPI, vol. 14(11), pages 1-17, November.

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