Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/140590
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dc.contributor.authorRu, X.-
dc.contributor.authorMei, C.-
dc.contributor.authorXia, W.-
dc.contributor.authorShi, P.-
dc.date.issued2023-
dc.identifier.citationScience China Information Sciences, 2023; 66(9):190209-1-190209-2-
dc.identifier.issn1674-733X-
dc.identifier.issn1869-1919-
dc.identifier.urihttps://hdl.handle.net/2440/140590-
dc.descriptionLetter-
dc.description.abstractIn this study, a novel data-driven approach, DED-MFAPC, was proposed for traffic light control of multi-intersection networks. In the future, the fuel economy and emissions of vehicles and the case when the traffic lights and vehicle dynamics are simultaneously optimized are worthy of investigation. Another future direction would be calculating the offset of the signals and the cycle time by the data-driven method.-
dc.description.statementofresponsibilityXinfeng Ru, Chenyi Mei, Weiguo Xia, Peng Shi-
dc.language.isoen-
dc.publisherSpringer-
dc.rights© Science China Press 2023-
dc.source.urihttp://dx.doi.org/10.1007/s11432-022-3825-4-
dc.titleDistributed model-free adaptive predictive control of traffic lights for multiple interconnected intersections-
dc.typeJournal article-
dc.identifier.doi10.1007/s11432-022-3825-4-
dc.relation.granthttp://purl.org/au-research/grants/arc/DP240101140-
pubs.publication-statusPublished-
dc.identifier.orcidShi, P. [0000-0001-6295-0405] [0000-0001-8218-586X] [0000-0002-0864-552X] [0000-0002-1358-2367] [0000-0002-5312-5435]-
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