Emerging trends in machine learning assisted optimization techniques across intelligent transportation systems

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dc.contributor.authorItoro Afolayan, Blessing
dc.contributor.authorGhosh, Arka
dc.contributor.authorFajardo Calderín, Jenny
dc.contributor.authorMasegosa Arredondo, Antonio David
dc.date.accessioned2025-02-24T11:42:50Z
dc.date.available2025-02-24T11:42:50Z
dc.date.issued2024
dc.date.updated2025-02-24T11:42:50Z
dc.description.abstractArtificial intelligence (AI) plays a critical role in Intelligent Transport Systems (ITS) as urban areas grow by processing data for safety enhancements, predictive analysis, and traffic management. This results in better traffic control, lower emissions, and preventative actions to lessen the effects of accidents. Despite these developments, there isn't a thorough academic analysis that covers a variety of optimization strategies for transportation AI models. By presenting an in-depth analysis of AI optimization methods and their uses in ITSs, this work seeks to close this knowledge gap and give academics important new information on possible directions for future research. Model-based optimization approaches, reinforcement learning techniques, model-predictive control techniques, and generative AI techniques are the four areas into which this study divides AI optimization techniques for the sake of structure, clarity, and comparative analysis. Subcategories of optimization techniques and their corresponding applications are explored, and each category is thoroughly addressed. Researchers will be better able to comprehend the state of AI optimization for transportation management today and in the future thanks to this methodical methodology. The most cutting-edge optimization methods created in the last five years are summarized in this review. This work acts as a compass for future research initiatives targeted at developing scalable and adaptable AI solutions for transportation management by identifying common approaches and highlighting research needs.en
dc.description.sponsorshipThis work was supported in part by the Spanish Ministry of Science and Innovation through the REsearch on New Artificial Intelligence techniques to Improve Sustainability, SAfety and resilieNCE of mobility (RENAISSANCE) project under Grant PID2022-140612OB-I00; in part by Basque Government through Research under Grant IT1564-22, Grant KK-2023/00012, and Grant KK-2023/00038; and in part by the Horizon Europe Research and Innovation Program through the project SYNCHROMODE (Advanced Traffic Management Solutions for Synchronized and Resilient Multimodal Transport Services) under Grant 101104171en
dc.identifier.citationAfolayan, B. I., Ghosh, A., Calderin, J. F., & Masegosa, A. D. (2024). Emerging Trends in Machine Learning Assisted Optimization Techniques Across Intelligent Transportation Systems. IEEE Access, 12, 173981-174005. https://doi.org/10.1109/ACCESS.2024.3501775
dc.identifier.doi10.1109/ACCESS.2024.3501775
dc.identifier.eissn2169-3536
dc.identifier.urihttps://hdl.handle.net/20.500.14454/2363
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.rights© 2024 The Authors
dc.subject.otherArtificial intelligence
dc.subject.otherGenerative AI
dc.subject.otherIntelligent transport systems
dc.subject.otherModel predictive control
dc.subject.otherModel-based optimization
dc.subject.otherReinforcement learning
dc.titleEmerging trends in machine learning assisted optimization techniques across intelligent transportation systemsen
dc.typejournal article
dcterms.accessRightsopen access
oaire.citation.endPage174005
oaire.citation.startPage173981
oaire.citation.titleIEEE Access
oaire.citation.volume12
oaire.licenseConditionhttps://creativecommons.org/licenses/by/4.0/
oaire.versionVoR
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