Estimating average vehicle mileage for various vehicle classes using polynomial models in deep classifiers

Niroomand, Naghmeh; Bach, Christian

doi:10.1109/ACCESS.2024.3359990

Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-30028

Publication type:	Article in scientific journal
Type of review:	Peer review (publication)
Title:	Estimating average vehicle mileage for various vehicle classes using polynomial models in deep classifiers
Authors:	Niroomand, Naghmeh Bach, Christian
et. al:	No
DOI:	10.1109/ACCESS.2024.3359990 10.21256/zhaw-30028
Published in:	IEEE Access
Volume(Issue):	12
Page(s):	17404
Pages to:	17418
Issue Date:	30-Jan-2024
Publisher / Ed. Institution:	IEEE
ISSN:	2169-3536
Language:	English
Subjects:	Average vehicle mileage; Mileage model; CO2 emission; Deep feature learning; Polynomial deep classifier; Vehicle classification
Subject (DDC):	006: Special computer methods 363: Environmental and security problems
Abstract:	Accurately 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.
URI:	https://digitalcollection.zhaw.ch/handle/11475/30028
Fulltext version:	Published version
License (according to publishing contract):	CC BY 4.0: Attribution 4.0 International
Departement:	School of Management and Law
Appears in collections:	Publikationen School of Management and Law

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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.