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dc.contributor.authorShaikh, Palwasha
dc.contributor.authorEl-Abd, Mohammed
dc.contributor.authorKhanafer, Mounib
dc.date.accessioned2021-01-18T07:35:42Z
dc.date.available2021-01-18T07:35:42Z
dc.date.issued2020-08-18
dc.identifier.urihttps://dspace.auk.edu.kw/handle/11675/6686
dc.description.abstractThe rapid development of urban cities coupled with the rise in population has led to an exponentially growing number of vehicles on the roads for the latter to commute. This is adding to the already overbearing problem of traffic congestion. Short term, costly and short-sighted solutions of road infrastructure expansions are no longer suitable. One effective method of road resource allocation is focusing on the widely used traffic signal controllers' timing schedules. Searching for a suitable or an optimal schedule for the prior via brute force to ease traffic congestion might not be the most elegant or feasible solution. Nature-inspired algorithms including evolutionary and swarm intelligence algorithms are gaining a lot of momentum. Many of these algorithms have been used in the last two decades to address different applications in the smart city era including traffic signal control (TSC). This paper conducts a comprehensive literature review on applications of evolutionary and swarm intelligence algorithms to TSC. Surveyed work is categorized based on the set of decision variables, optimization objective(s), problem modeling and solution encoding. The paper, based on gaps identified by the conducted review, identifies promising future research directions and discusses where the future research is headed.
dc.relation.journalIEEE Transactions on Intelligent Transportation Systems (T-ITS)
dc.titleA Review on Swarm Intelligence and Evolutionary Algorithms for Solving the Traffic Signal Control Problem
dc.typeJournal Article
dc.typePeer-Reviewed
dcterms.bibliographicCitationShaikh P. W., El-Abd M., Khanafer M. and Gao K. (2020). A Review on Swarm Intelligence and Evolutionary Algorithms for Solving the Traffic Signal Control Problem. IEEE Transactions on Intelligent Transportation Systems, doi: 10.1109/TITS.2020.3014296.
dc.identifier.urlhttps://ieeexplore.ieee.org/document/9170901


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