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A Review of Machine Learning and IoT in Smart Transportation

Author

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  • Fotios Zantalis

    (TelSiP Research Laboratory, Department of Electrical and Electronic Engineering, School of Engineering, University of West Attica, University Campus 2, 250 Thivon Str., Egaleo, GR-12241 Athens, Greece
    These authors contributed equally to this work.)

  • Grigorios Koulouras

    (TelSiP Research Laboratory, Department of Electrical and Electronic Engineering, School of Engineering, University of West Attica, University Campus 2, 250 Thivon Str., Egaleo, GR-12241 Athens, Greece
    Hellenic Telecommunications and Post Commission, 60 Kifissias Avenue, Maroussi, GR-15125 Athens, Greece
    These authors contributed equally to this work.)

  • Sotiris Karabetsos

    (TelSiP Research Laboratory, Department of Electrical and Electronic Engineering, School of Engineering, University of West Attica, University Campus 2, 250 Thivon Str., Egaleo, GR-12241 Athens, Greece
    These authors contributed equally to this work.)

  • Dionisis Kandris

    (microSENSES Research Laboratory, Department of Electrical and Electronic Engineering, School of Engineering, University of West Attica, University Campus 2, 250 Thivon Str., Egaleo, GR-12241 Athens, Greece
    These authors contributed equally to this work.)

Abstract

With the rise of the Internet of Things (IoT), applications have become smarter and connected devices give rise to their exploitation in all aspects of a modern city. As the volume of the collected data increases, Machine Learning (ML) techniques are applied to further enhance the intelligence and the capabilities of an application. The field of smart transportation has attracted many researchers and it has been approached with both ML and IoT techniques. In this review, smart transportation is considered to be an umbrella term that covers route optimization, parking, street lights, accident prevention/detection, road anomalies, and infrastructure applications. The purpose of this paper is to make a self-contained review of ML techniques and IoT applications in Intelligent Transportation Systems (ITS) and obtain a clear view of the trends in the aforementioned fields and spot possible coverage needs. From the reviewed articles it becomes profound that there is a possible lack of ML coverage for the Smart Lighting Systems and Smart Parking applications. Additionally, route optimization, parking, and accident/detection tend to be the most popular ITS applications among researchers.

Suggested Citation

  • Fotios Zantalis & Grigorios Koulouras & Sotiris Karabetsos & Dionisis Kandris, 2019. "A Review of Machine Learning and IoT in Smart Transportation," Future Internet, MDPI, vol. 11(4), pages 1-23, April.
    • Handle: RePEc:gam:jftint:v:11:y:2019:i:4:p:94-:d:221586
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    References listed on IDEAS

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    12. Yuting Chen & Pengjun Zhao & Qi Chen, 2026. "Forecasting the commuting generation using metropolis-informed GCN and the topological commuter portrait," Transportation, Springer, vol. 53(1), pages 483-510, February.
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