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dc.contributor.authorQu, X
dc.contributor.authorYu, Y
dc.contributor.authorZhou, M
dc.contributor.authorLin, CT
dc.contributor.authorWang, X
dc.date.accessioned2019-11-25T03:31:49Z
dc.date.available2019-11-25T03:31:49Z
dc.date.issued2020
dc.identifier.issn0306-2619
dc.identifier.doi10.1016/j.apenergy.2019.114030
dc.identifier.urihttp://hdl.handle.net/10072/389236
dc.description.abstractIt has been well recognized that human driver’s limits, heterogeneity, and selfishness substantially compromise the performance of our urban transport systems. In recent years, in order to deal with these deficiencies, our urban transport systems have been transforming with the blossom of key vehicle technology innovations, most notably, connected and automated vehicles. In this paper, we develop a car following model for electric, connected and automated vehicles based on reinforcement learning with the aim to dampen traffic oscillations (stop-and-go traffic waves) caused by human drivers and improve electric energy consumption. Compared to classical modelling approaches, the proposed reinforcement learning based model significantly reduces the modelling constraints and has the capability of self-learning and self-correction. Experiment results demonstrate that the proposed model is able to improve travel efficiency by reducing the negative impact of traffic oscillations, and it can also reduce the average electric energy consumption.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherElsevier
dc.publisher.placeUnited Kingdom
dc.relation.ispartofpagefrom114030: 1
dc.relation.ispartofpageto114030: 11
dc.relation.ispartofjournalApplied Energy
dc.relation.ispartofvolume257
dc.subject.fieldofresearchApplied Economics
dc.subject.fieldofresearchEngineering
dc.subject.fieldofresearchEconomics
dc.subject.fieldofresearchcode1402
dc.subject.fieldofresearchcode09
dc.subject.fieldofresearchcode14
dc.titleJointly dampening traffic oscillations and improving energy consumption with electric, connected and automated vehicles: A reinforcement learning based approach
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationQu, X; Yu, Y; Zhou, M; Lin, CT; Wang, X, Jointly dampening traffic oscillations and improving energy consumption with electric, connected and automated vehicles: A reinforcement learning based approach, Applied Energy, 2020, 257, pp. 114030: 1-114030: 11
dc.date.updated2019-11-21T22:38:08Z
gro.hasfulltextNo Full Text
gro.griffith.authorQu, Xiaobo


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