Beyond sensors: IntelliSignal’s pap-integrated intelligence in traffic flow optimization

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dc.contributor.authorSreejith, K.en
dc.contributor.authorMathi, Senthilkumaren
dc.contributor.authorPradeep, Preejaen
dc.contributor.funderScience Foundation Irelanden
dc.date.accessioned2024-09-03T14:23:11Z
dc.date.available2024-09-03T14:23:11Z
dc.date.issued2024-03-11en
dc.description.abstractThe burgeoning growth of vehicular traffic, fuelled by rapid urbanization and an ever-expanding population, has resulted in congested road networks. Traditional and sensor-based adaptive traffic light management systems have shown commendable progress in some scenarios, but they suffer from inherent disadvantages that hinder their effectiveness and scalability. To combat these challenges, a dynamic and adaptable traffic control system is imperative to optimize traffic flow. The present paper explores the limitations of sensor-based adaptive traffic light management and advocates for integrating a novel algorithm to overcome the existing drawbacks. It proposes an intelligent traffic management algorithm called IntelliSignal that leverages the map service for fetching real-time traffic information to calculate the optimal green time and a penalty-based road selection to optimize traffic flow. The proposed IntelliSignal is designed to provide equal chances for all roads while prioritizing higher-density roads with more green time, effectively mitigating traffic congestion and improving overall transportation efficiency. It incorporates Q-learning, a reinforcement learning technique that enables the system to adapt and learn from traffic patterns. The proposed IntelliSignal’s performance is assessed through rigorous evaluations conducted on the simulation platform SUMO. The acquired results demonstrate substantial enhancements across various crucial metrics, including average waiting time, vehicle density, travel time, CO2 emissions, and queue length. Furthermore, the simulation results demonstrate that the proposed IntelliSignal algorithm exhibits a remarkable 30.52% increment in system throughput compared to the traditional approach. This significant enhancement underscores the efficacy of the proposed IntelliSignal in optimizing system performance and merits consideration for practical implementation.en
dc.description.statusPeer revieweden
dc.description.versionPublished Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationSreejith, K., Mathi, S. and Pradeep, P. (2024) ‘Beyond sensors: Intellisignal’s map-integrated intelligence in traffic flow optimization’, IEEE Access, 12, pp. 39028–39040. Available at: https://doi.org/10.1109/ACCESS.2024.3375335en
dc.identifier.doihttps://doi.org/10.1109/ACCESS.2024.3375335en
dc.identifier.endpage39040en
dc.identifier.issn2169-3536en
dc.identifier.journaltitleIEEE Accessen
dc.identifier.startpage39028en
dc.identifier.urihttps://hdl.handle.net/10468/16251
dc.identifier.volume12en
dc.language.isoenen
dc.publisherIEEEen
dc.relation.ispartofIEEE Accessen
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres/12/RC/2289/IE/INSIGHT - Irelands Big Data and Analytics Research Centre/en
dc.rights© 2024 The Authors. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.subjectIntelliSignalen
dc.subjectMap serviceen
dc.subjectQ-learningen
dc.subjectSensor-free traffic optimizationen
dc.subjectSustainabilityen
dc.subjectTraffic controlen
dc.subjectRoad trafficen
dc.subjectSensorsen
dc.subjectReal-time systemsen
dc.subjectHeuristic algorithmsen
dc.subjectAdaptive systemsen
dc.subjectVehicle drivingen
dc.subjectQ-learningen
dc.subjectSustainable developmenten
dc.subjectUrban areasen
dc.subjectIntelligent sensorsen
dc.titleBeyond sensors: IntelliSignal’s pap-integrated intelligence in traffic flow optimizationen
dc.typeArticle (peer-reviewed)en
oaire.citation.volume12en
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