EcoLight plus : a novel multi-modal data fusion for enhanced eco-friendly traffic signal control driven by urban traffic noise prediction

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dc.contributor.authorOunoughi, Chahinez
dc.contributor.authorOunoughi, Doua
dc.contributor.authorBen Yahia, Sadok
dc.date.accessioned2025-01-27T20:55:55Z
dc.date.available2025-01-27T20:55:55Z
dc.date.issued2023
dc.departmentÇanakkale Onsekiz Mart Üniversitesi
dc.description.abstractUrban traffic congestion is of utmost importance for modern societies due to population and economic growth. Thus, it contributes to environmental problems like increasing greenhouse gas emissions and noise pollution. Improved traffic flow in urban networks relies heavily on traffic signal control. Hence, optimizing cycle timing at many intersections is paramount to reducing congestion and increasing sustainability. This paper introduces an alternative to conventional traffic signal control, EcoLight+, which incorporates future noise predictions with the deep dueling Q-network reinforcement Learning algorithm to reduce noise levels, CO2 emissions, and fuel consumption. An innovative data fusion approach is also proposed to improve our LSTM-based noise prediction model by integrating heterogeneous data from different sources. Our proposed solution allows the system to achieve higher efficiency than its competitors based on real-world data from Tallinn, Estonia.
dc.description.sponsorshipH2020 [952410]; Estonian Research Council [PRG1573]; EU-Astra - TUT Development Plan for 2016-2022 (ASTRA) [2014-2020.4.1.16-0032]
dc.description.sponsorshipThis work was supported by grants to TalTech - TalTech Industrial (H2020, grant No 952410), Estonian Research Council (PRG1573), and EU-Astra - TUT Development Plan for 2016-2022 (ASTRA) reg no. 2014-2020.4.1.16-0032.
dc.identifier.doi10.1007/s10115-023-01938-y
dc.identifier.endpage5329
dc.identifier.issn0219-1377
dc.identifier.issn0219-3116
dc.identifier.issue12
dc.identifier.scopus2-s2.0-85165672342
dc.identifier.scopusqualityQ2
dc.identifier.startpage5309
dc.identifier.urihttps://doi.org/10.1007/s10115-023-01938-y
dc.identifier.urihttps://hdl.handle.net/20.500.12428/26241
dc.identifier.volume65
dc.identifier.wosWOS:001036727000001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer London Ltd
dc.relation.ispartofKnowledge and Information Systems
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğrenci
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WoS_20250125
dc.subjectCO2 emissions
dc.subjectCongestion
dc.subjectFuel consumption
dc.subjectData Fusion
dc.subjectDueling DQN SUMO Simulation
dc.subjectTraffic signal control
dc.subjectUrban noise
dc.titleEcoLight plus : a novel multi-modal data fusion for enhanced eco-friendly traffic signal control driven by urban traffic noise prediction
dc.typeArticle

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