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Accurately measuring vehicle mileage is pivotal in precise CO 2  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 CO 2  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. Moreover, our framework supports segment-based analysis, which enables segment-specific assessment of average mileage. By implementing our proposed techniques, we aspire to enrich the precision of emission models, resulting in more dependable calculations and an enhanced understanding of the environmental implications associated with vehicles.

Estimating Average Vehicle Mileage for Various Vehicle Classes using Polynomial Models in Deep Classifiers