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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-30028
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dc.contributor.authorNiroomand, Naghmeh-
dc.contributor.authorBach, Christian-
dc.date.accessioned2024-02-29T13:25:02Z-
dc.date.available2024-02-29T13:25:02Z-
dc.date.issued2024-01-30-
dc.identifier.issn2169-3536de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/30028-
dc.description.abstractAccurately measuring vehicle mileage is pivotal in precise CO2 emission calculations and the development of reliable emission models. Nonetheless, mileage data gathered from surveys relying on self-estimation, garage reports, and other estimation-based sources often yield rough approximations that substantially deviate from the actual mileage. To tackle this issue, we present a comprehensive framework aimed at bolstering the accuracy of CO2 emission models. This paper harnesses two innovative techniques: the deep learning semi-supervised fuzzy C-means (SSFCM) and polynomial classifier models. By leveraging these sophisticated mathematical techniques, we achieve successful classification of passenger vehicles, enabling more precise evaluations of average mileage. Real data shows that vehicles in Switzerland considerably exceed the estimated mileage in the years following the first registration of the vehicle. The difference lies in the covered mileage after vehicles reach five years of age. Our framework supports segment-based analysis for assessing average mileage and enhancing emission models for better understanding of vehicle-related environmental impact.de_CH
dc.language.isoende_CH
dc.publisherIEEEde_CH
dc.relation.ispartofIEEE Accessde_CH
dc.rightshttp://creativecommons.org/licenses/by/4.0/de_CH
dc.subjectAverage vehicle mileagede_CH
dc.subjectMileage modelde_CH
dc.subjectCO2 emissionde_CH
dc.subjectDeep feature learningde_CH
dc.subjectPolynomial deep classifierde_CH
dc.subjectVehicle classificationde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.subject.ddc363: Umwelt- und Sicherheitsproblemede_CH
dc.titleEstimating average vehicle mileage for various vehicle classes using polynomial models in deep classifiersde_CH
dc.typeBeitrag in wissenschaftlicher Zeitschriftde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Management and Lawde_CH
dc.identifier.doi10.1109/ACCESS.2024.3359990de_CH
dc.identifier.doi10.21256/zhaw-30028-
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end17418de_CH
zhaw.pages.start17404de_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume12de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.webfeedW: Spitzenpublikationde_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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Niroomand, N., & Bach, C. (2024). Estimating average vehicle mileage for various vehicle classes using polynomial models in deep classifiers. IEEE Access, 12, 17404–17418. https://doi.org/10.1109/ACCESS.2024.3359990
Niroomand, N. and Bach, C. (2024) ‘Estimating average vehicle mileage for various vehicle classes using polynomial models in deep classifiers’, IEEE Access, 12, pp. 17404–17418. Available at: https://doi.org/10.1109/ACCESS.2024.3359990.
N. Niroomand and C. Bach, “Estimating average vehicle mileage for various vehicle classes using polynomial models in deep classifiers,” IEEE Access, vol. 12, pp. 17404–17418, Jan. 2024, doi: 10.1109/ACCESS.2024.3359990.
NIROOMAND, Naghmeh und Christian BACH, 2024. Estimating average vehicle mileage for various vehicle classes using polynomial models in deep classifiers. IEEE Access. 30 Januar 2024. Bd. 12, S. 17404–17418. DOI 10.1109/ACCESS.2024.3359990
Niroomand, Naghmeh, and Christian Bach. 2024. “Estimating Average Vehicle Mileage for Various Vehicle Classes Using Polynomial Models in Deep Classifiers.” IEEE Access 12 (January): 17404–18. https://doi.org/10.1109/ACCESS.2024.3359990.
Niroomand, Naghmeh, and Christian Bach. “Estimating Average Vehicle Mileage for Various Vehicle Classes Using Polynomial Models in Deep Classifiers.” IEEE Access, vol. 12, Jan. 2024, pp. 17404–18, https://doi.org/10.1109/ACCESS.2024.3359990.


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