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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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    1. Saber Talari & Miadreza Shafie-khah & Pierluigi Siano & Vincenzo Loia & Aurelio Tommasetti & João P. S. Catalão, 2017. "A Review of Smart Cities Based on the Internet of Things Concept," Energies, MDPI, vol. 10(4), pages 1-23, March.
    2. World Bank Group, 2017. "Internet of Things," World Bank Publications - Reports 28661, The World Bank Group.
    3. Guifang Liu & Huaiqian Bao & Baokun Han, 2018. "A Stacked Autoencoder-Based Deep Neural Network for Achieving Gearbox Fault Diagnosis," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-10, July.
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    Cited by:

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    2. Abderahman Rejeb & John G. Keogh & Horst Treiblmaier, 2019. "Leveraging the Internet of Things and Blockchain Technology in Supply Chain Management," Future Internet, MDPI, vol. 11(7), pages 1-22, July.
    3. Nadine Bachmann & Shailesh Tripathi & Manuel Brunner & Herbert Jodlbauer, 2022. "The Contribution of Data-Driven Technologies in Achieving the Sustainable Development Goals," Sustainability, MDPI, vol. 14(5), pages 1-33, February.
    4. Khadijeh Alibabaei & Eduardo Assunção & Pedro D. Gaspar & Vasco N. G. J. Soares & João M. L. P. Caldeira, 2022. "Real-Time Detection of Vine Trunk for Robot Localization Using Deep Learning Models Developed for Edge TPU Devices," Future Internet, MDPI, vol. 14(7), pages 1-16, June.
    5. Konstantinos Ntafloukas & Liliana Pasquale & Beatriz Martinez-Pastor & Daniel P. McCrum, 2023. "A Vulnerability Assessment Approach for Transportation Networks Subjected to Cyber–Physical Attacks," Future Internet, MDPI, vol. 15(3), pages 1-23, February.
    6. Zhang, Wuxia & Wu, Yupeng & Calautit, John Kaiser, 2022. "A review on occupancy prediction through machine learning for enhancing energy efficiency, air quality and thermal comfort in the built environment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
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