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

Mohammad Peyman, Pedro J. Copado, Rafael D. Tordecilla, Leandro do C. Martins, Fatos Xhafa and Angel Juan
Additional contact information
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
Rafael D. Tordecilla: IN3—Computer Science Department, Universitat Oberta de Catalunya, 08018 Barcelona, Spain
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

Energies, 2021, vol. 14, issue 19, 1-26

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.

Keywords: fog; edge computing; Internet of Things; intelligent transportation systems; smart cities; machine learning; agile optimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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