Edge Intelligence for Intelligent Transport Systems: Approaches, challenges, and future directions

Intelligent Transportation Systems (ITS) are entering a new era with the integration of Distributed Edge Intelligence, which brings the power of artificial intelligence to the edge of the network. This survey provides a comprehensive review of the role of Distributed Edge Intelligence in ITS, emphasizing its applications, challenges, and implications. Unlike previous studies that focus on specific technologies such as communication, blockchain, cloud and fog computing, and security, this work highlights the unique integration of Edge Intelligence across various ITS components, including vehicles, infrastructure, and communication systems. The paper systematically examines these integrations, identifies key technical challenges, and offers insights into future research directions. By focusing on the transformative impact of Edge Intelligence, this study aims to complement existing surveys and guide researchers, practitioners, and policymakers in shaping the future of smart, sustainable transportation. Through this, we contribute to advancing ITS technology and fostering innovation in the transportation sector.

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Publication Details

Journal
Expert Systems with Applications
Published
2025-04-02
DOI
https://doi.org/10.1016/j.eswa.2025.127273
Citations
10
Primary Topic
Traffic Prediction and Management Techniques
Type
article
Field-Weighted Citation Impact
10.06

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article

Edge Intelligence for Intelligent Transport Systems: Approaches, challenges, and future directions

Tajedin Derikvand, Ahmed M. Abdelmoniem, Amin Keshavarzi, Arezoo Ghasemi et al.
10 citations
Expert Systems with Applications
Traffic Prediction and Management Techniques
10.06
article

Edge Intelligence for Intelligent Transport Systems: Approaches, challenges, and future directions

Tajedin Derikvand, Ahmed M. Abdelmoniem, Amin Keshavarzi, Arezoo Ghasemi, Omid Reza Nejati
article en
10 citations

Abstract

Intelligent Transportation Systems (ITS) are entering a new era with the integration of Distributed Edge Intelligence, which brings the power of artificial intelligence to the edge of the network. This survey provides a comprehensive review of the role of Distributed Edge Intelligence in ITS, emphasizing its applications, challenges, and implications. Unlike previous studies that focus on specific technologies such as communication, blockchain, cloud and fog computing, and security, this work highlights the unique integration of Edge Intelligence across various ITS components, including vehicles, infrastructure, and communication systems. The paper systematically examines these integrations, identifies key technical challenges, and offers insights into future research directions. By focusing on the transformative impact of Edge Intelligence, this study aims to complement existing surveys and guide researchers, practitioners, and policymakers in shaping the future of smart, sustainable transportation. Through this, we contribute to advancing ITS technology and fostering innovation in the transportation sector.

Expert Systems with ApplicationsVol. 280
Leibniz University Hannover (DE), Queen Mary University of London (GB), Qazvin Islamic Azad University (IR), Technische Informationsbibliothek (TIB) (DE), Islamic Azad University, Marvdasht (IR), L3S Research Center (DE), Assiut University (EG)
UK Research and Innovation, Engineering and Physical Sciences Research Council, Economic and Social Research Council
Openalex Percentile: Top 2%
Traffic Prediction and Management Techniques
10.06
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