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 Bathroom Cleaners 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 Umbrellas 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.