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Edge Computing and IoT Analytics for Agile Optimization in Intelligent Transportation Systems

Author

Listed:
  • Mohammad Peyman

    (IN3—Computer Science Department, Universitat Oberta de Catalunya, 08018 Barcelona, Spain)

  • Pedro J. Copado

    (IN3—Computer Science Department, Universitat Oberta de Catalunya, 08018 Barcelona, Spain
    Department of Data Analytics & Business Intelligence, Euncet Business School, 08018 Barcelona, Spain)

  • Rafael D. Tordecilla

    (IN3—Computer Science Department, Universitat Oberta de Catalunya, 08018 Barcelona, Spain
    School of Engineering, Universidad de La Sabana, Chia 250001, Colombia)

  • Leandro do C. Martins

    (IN3—Computer Science Department, Universitat Oberta de Catalunya, 08018 Barcelona, Spain)

  • Fatos Xhafa

    (Computer Science Department, Universitat Politècnica de Catalunya, 08034 Barcelona, Spain)

  • Angel A. Juan

    (IN3—Computer Science Department, Universitat Oberta de Catalunya, 08018 Barcelona, Spain
    Department of Data Analytics & Business Intelligence, Euncet Business School, 08018 Barcelona, Spain)

Abstract

With the emergence of fog and edge computing, new possibilities arise regarding the data-driven management of citizens’ mobility in smart cities. Internet of Things (IoT) analytics refers to the use of these technologies, data, and analytical models to describe the current status of the city traffic, to predict its evolution over the coming hours, and to make decisions that increase the efficiency of the transportation system. It involves many challenges such as how to deal and manage real and huge amounts of data, and improving security, privacy, scalability, reliability, and quality of services in the cloud and vehicular network. In this paper, we review the state of the art of IoT in intelligent transportation systems (ITS), identify challenges posed by cloud, fog, and edge computing in ITS, and develop a methodology based on agile optimization algorithms for solving a dynamic ride-sharing problem (DRSP) in the context of edge/fog computing. These algorithms allow us to process, in real time, the data gathered from IoT systems in order to optimize automatic decisions in the city transportation system, including: optimizing the vehicle routing, recommending customized transportation modes to the citizens, generating efficient ride-sharing and car-sharing strategies, create optimal charging station for electric vehicles and different services within urban and interurban areas. A numerical example considering a DRSP is provided, in which the potential of employing edge/fog computing, open data, and agile algorithms is illustrated.

Suggested Citation

  • Mohammad Peyman & Pedro J. Copado & Rafael D. Tordecilla & Leandro do C. Martins & Fatos Xhafa & Angel A. Juan, 2021. "Edge Computing and IoT Analytics for Agile Optimization in Intelligent Transportation Systems," Energies, MDPI, vol. 14(19), pages 1-26, October.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:19:p:6309-:d:649116
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    References listed on IDEAS

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    1. Javier Panadero & Angel A. Juan & Christopher Bayliss & Christine Currie, 2020. "Maximising reward from a team of surveillance drones: a simheuristic approach to the stochastic team orienteering problem," European Journal of Industrial Engineering, Inderscience Enterprises Ltd, vol. 14(4), pages 485-516.
    2. Daniele Ferone & Aljoscha Gruler & Paola Festa & Angel A. Juan, 2019. "Enhancing and extending the classical GRASP framework with biased randomisation and simulation," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 70(8), pages 1362-1375, August.
    3. Ali Gohar & Gianfranco Nencioni, 2021. "The Role of 5G Technologies in a Smart City: The Case for Intelligent Transportation System," Sustainability, MDPI, vol. 13(9), pages 1-24, May.
    4. Daniel J. Fagnant & Kara M. Kockelman, 2018. "Dynamic ride-sharing and fleet sizing for a system of shared autonomous vehicles in Austin, Texas," Transportation, Springer, vol. 45(1), pages 143-158, January.
    5. Agatz, Niels A.H. & Erera, Alan L. & Savelsbergh, Martin W.P. & Wang, Xing, 2011. "Dynamic ride-sharing: A simulation study in metro Atlanta," Transportation Research Part B: Methodological, Elsevier, vol. 45(9), pages 1450-1464.
    6. Elarbi Badidi & Zineb Mahrez & Essaid Sabir, 2020. "Fog Computing for Smart Cities’ Big Data Management and Analytics: A Review," Future Internet, MDPI, vol. 12(11), pages 1-28, October.
    7. Juan, Angel A. & Faulin, Javier & Grasman, Scott E. & Rabe, Markus & Figueira, Gonçalo, 2015. "A review of simheuristics: Extending metaheuristics to deal with stochastic combinatorial optimization problems," Operations Research Perspectives, Elsevier, vol. 2(C), pages 62-72.
    8. Christian Fikar & Angel A. Juan & Enoc Martinez & Patrick Hirsch, 2016. "A discrete-event driven metaheuristic for dynamic home service routing with synchronised trip sharing," European Journal of Industrial Engineering, Inderscience Enterprises Ltd, vol. 10(3), pages 323-340.
    9. Aljoscha Gruler & Carlos L. Quintero-Araújo & Laura Calvet & Angel A. Juan, 2017. "Waste collection under uncertainty: a simheuristic based on variable neighbourhood search," European Journal of Industrial Engineering, Inderscience Enterprises Ltd, vol. 11(2), pages 228-255.
    10. Junjun Wei & Kejun Long & Jian Gu & Qingling Ju & Piao Zhu, 2020. "Optimizing Bus Line Based on Metro-Bus Integration," Sustainability, MDPI, vol. 12(4), pages 1-14, February.
    11. Shah, Nirav & Kumar, Subodha & Bastani, Farokh & Yen, I-Ling, 2012. "Optimization models for assessing the peak capacity utilization of intelligent transportation systems," European Journal of Operational Research, Elsevier, vol. 216(1), pages 239-251.
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    Cited by:

    1. Satheesh Abimannan & El-Sayed M. El-Alfy & Shahid Hussain & Yue-Shan Chang & Saurabh Shukla & Dhivyadharsini Satheesh & John G. Breslin, 2023. "Towards Federated Learning and Multi-Access Edge Computing for Air Quality Monitoring: Literature Review and Assessment," Sustainability, MDPI, vol. 15(18), pages 1-34, September.
    2. Daniela Mazza & Daniele Tarchi & Angel A. Juan, 2022. "Advanced Technologies in Smart Cities," Energies, MDPI, vol. 15(13), pages 1-3, June.

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