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 |
Files in This Item:
File | Description | Size | Format | |
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2024_Niroomand-etal_Estimating-average-vehicle-mileage-for-various-vehicle-classes.pdf | 2.72 MB | Adobe PDF | View/Open |
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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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