Tailoring Energy Efficiency for Urban Electric Buses: The GTECM Model for Enhanced Range and Sustainable Operation Using Real-Time Big Data

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dc.contributor.authorEkici, Yunus Emre
dc.contributor.authorKaradag, Teoman
dc.contributor.authorAkdag, Ozan
dc.contributor.authorAydin, Ahmet Arif
dc.contributor.authorTekin, Huseyn Ozan
dc.date.accessioned2026-04-04T13:33:23Z
dc.date.available2026-04-04T13:33:23Z
dc.date.issued2025
dc.departmentİnönü Üniversitesi
dc.description.abstractThe increasing depletion of fossil fuels and growing environmental concerns are increasing the need for energy efficient and sustainable solutions, particularly in transport. At this point, especially in public transport, electric vehicles (EVs) offer a promising alternative; however, issues such as range anxiety and energy efficiency require comprehensive solutions. This study introduces the Gauss-based Trolleybus Energy Consumption Model (GTECM) for electric buses, harnessing real-time big data to mitigate range anxiety and enhance energy efficiency. This model employs Gaussian Process Regression to a large-scale dataset including 100,000 entries collected over six months in T & uuml;rkiye. With an overall Root Mean Square Error (RMSE) of 0.013905, GTECM substantially outperforms linear approaches across T & uuml;rkiye's primary routes, exhibiting route-specific RMSE values between 0.28117 and 0.30540. Empirical findings suggest potential energy savings of up to 50%, alongside a 10% extension in driving range, thereby mitigating an estimated 4,220 tons of CO2 and 129.88 tons of NO2 emissions annually. Moreover, the projected amortization period for diesel-to-electric bus conversion stands at 6.83 years, underscoring GTECM's pragmatic utility for sustainable urban transit optimization. The findings of the study can form the basis for future research and guide policy makers and urban planners in the development of more efficient and sustainable transport networks.
dc.description.sponsorshipResearch Fund of Inonu University [FDK-2023-3215, FDP-2021-2678, FBG-2021-2283]
dc.description.sponsorshipThis work wassupported by the Research Fund of Inonu University under Project FDK-2023-3215, Project FDP-2021-2678, and Project FBG-2021-2283
dc.identifier.doi10.1109/TITS.2025.3558147
dc.identifier.endpage12614
dc.identifier.issn1524-9050
dc.identifier.issn1558-0016
dc.identifier.issue8
dc.identifier.orcid0000-0001-8163-8898
dc.identifier.orcid0000-0002-7682-7771
dc.identifier.orcid0000-0002-4124-7275
dc.identifier.orcid0000-0001-7791-0473
dc.identifier.scopus2-s2.0-105003380312
dc.identifier.scopusqualityQ1
dc.identifier.startpage12600
dc.identifier.urihttps://doi.org/10.1109/TITS.2025.3558147
dc.identifier.urihttps://hdl.handle.net/11616/109124
dc.identifier.volume26
dc.identifier.wosWOS:001480209600001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherIeee-Inst Electrical Electronics Engineers Inc
dc.relation.ispartofIEEE Transactions on Intelligent Transportation Systems
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20250329
dc.subjectEnergy consumption
dc.subjectOptimization
dc.subjectHVAC
dc.subjectBatteries
dc.subjectAdaptation models
dc.subjectTransportation
dc.subjectData models
dc.subjectReal-time systems
dc.subjectEnergy efficiency
dc.subjectBig Data
dc.subjectArtificial intelligence
dc.subjectbig data
dc.subjectelectric bus
dc.subjectenergy efficiency
dc.subjectprediction
dc.subjectsustainable transportation
dc.subjecttrolleybus environmental effect
dc.titleTailoring Energy Efficiency for Urban Electric Buses: The GTECM Model for Enhanced Range and Sustainable Operation Using Real-Time Big Data
dc.typeArticle

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