A Systematic Review of Artificial Intelligence in Transportation Systems: Methods, Applications, and Transport–Energy Integration for Sustainable Mobility
DOI:
https://doi.org/10.26740/vubeta.v3i3.52572Keywords:
Artificial Intelligence, Sustainable Transport, Autonomous Mobility, Deep Learning, Smart InfrastructureAbstract
Artificial Intelligence (AI) is transforming the transportation systems with autonomous, efficient, and sustainable mobility. The swift transformation of it into a data-driven intelligence rather than a rule-based one has opened new possibilities of interconnecting transportation systems with electrical power networks. This study is a systematic review of AI applications in autonomous and sustainable transportation systems. The major academic databases, such as Scopus, IEEE Xplore, Web of Science, ScienceDirect, and SpringerLink, were searched to retrieve literature that was published in 2015-2025. Peer-reviewed journal and conference papers were screened using a structured screening process, which led to the selection of 35 studies to be included in qualitative synthesis. The review framework was based on thematic analysis in the fields of evolution, applications, methodologies, and energy integration. Results indicate that machine learning and deep learning are the most prevalent in the current transportation AI studies, especially in traffic prediction, autonomous navigation, and infrastructure monitoring. The most developed application areas are traffic management and autonomous vehicle systems, and energy-aware mobility, especially electric vehicle charging optimisation and vehicle-to-grid coordination, is developing quickly but is less advanced. Major obstacles are the data quality constraints, the challenges of model interpretability, the constraints of infrastructure, cybersecurity threats, and disjointed regulatory frameworks, particularly in developing areas. The review confirms the evident shift towards combined transportation-energy intelligence, where AI can facilitate coordinated mobility and power system optimisation. The future directions are federated learning, digital twin systems, explainable AI, and multi-modal intelligent transport systems, which facilitate scalable and sustainable mobility development.
Author Biographies
Najeem Olawale Adelakun, Works and Services Department, Federal College of Education, Iwo, Osun State, Nigeria
Engr. Najeem Olawale Adelakun is a Principal Engineer at the Federal College of Education, Iwo, Osun State, Nigeria. He is a PhD scholar in Electrical Electronics Engineering and Artificial Intelligence, currently pursuing both programmes concurrently at different institutions, reflecting a rare blend of intellectual depth and strategic academic positioning. A seasoned researcher with a disciplined and forward-leaning mindset, he has authored and co-authored over 100 scholarly publications spanning reputable journals, books, and international conferences, attracting more than 470 citations. His work reflects both depth and consistency, grounded in the enduring principles of engineering while embracing emerging technological frontiers. His core research interests include Electrical Power Systems, Artificial Intelligence, Machine Learning, and Renewable Energy Technologies, with a strong inclination toward sustainable and intelligent infrastructure development. He can be reached via email: [email protected]
Matthew Babatunde Olajide, Department of Electrical/Electronic Engineering, Olabisi Onabanjo University, Ago-Iwoye, Nigeria
Engr. Dr. Matthew Babatunde Olajide is a PhD holder in Electrical Engineering and serves as a Senior Lecturer in the Department of Electrical/Electronics Engineering at Olabisi Onabanjo University, Ago-Iwoye, Nigeria. He is a committed researcher and academic, with numerous publications in reputable journals and conferences, reflecting his dedication to advancing knowledge in his field. His research focuses on telecommunications systems, signal processing, and modern communication technologies, with particular interest in innovations that improve network efficiency and connectivity. Dr. Olajide is passionate about teaching and mentoring students, equipping them with the skills and insights needed to excel in engineering practice. He actively contributes to academic and professional development, bridging theoretical research with practical applications, and supporting sustainable technological growth in Nigeria and beyond. He can be reached via email: [email protected]
Samuel Adeniyi Omolola, Department of Electrical/Electronic Engineering, Federal University of Technology Ilaro, Ogun State, Nigeria
Engr. Samuel Adeniyi Omolola is a Principal Lecturer in the Department of Electrical/Electronic Engineering at Federal University of Technology Ilaro, Ogun State, Nigeria. He is a seasoned academic and researcher with numerous publications in reputable journals, reflecting his dedication to advancing knowledge in electrical electronics engineering. His research focuses on electrical power systems, renewable energy technologies, power system stability, smart grids, and energy management, emphasizing practical and innovative approaches. He is committed to excellence in teaching, mentoring students, and fostering research that addresses real-world engineering challenges. Through his work, he bridges theoretical insights with practical applications, contributing to technological development and the training of skilled engineers capable of driving progress in Nigeria’s energy and engineering sectors. He can be reached via email: [email protected]
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