Abstract
There has been globally continuous growth in passenger car sizes and types over the past few decades. To assess the development of vehicular specifications in this context and to evaluate changes in powertrain technologies depending on surrounding frame conditions, such as charging stations and vehicle taxation policy, we need a detailed understanding of the vehicle fleet composition. This paper aims therefore to introduce a novel mathematical approach to segment passenger vehicles based on dimensions features using a means fuzzy clustering algorithm, Fuzzy C-means (FCM), and a non-fuzzy clustering algorithm, K-means (KM). We analyze the performance of the proposed algorithms and compare them with Swiss expert segmentation. Experiments on the real data sets demonstrate that the FCM classifier has better correlation with the expert segmentation than KM. Furthermore, the outputs from FCM with five clusters show that the proposed algorithm has a superior performance for accurate vehicle categorization because of its capacity to recognize and consolidate dimension attributes from the unsupervised data set. Its performance in categorizing vehicles was promising with an average accuracy rate of 79% and an average positive predictive value of 75%.
Highlights
There has been globally continuous growth in passenger car sizes and types over the past few decades
The implementation of KM and Fuzzy C-means (FCM) is done on the first registered passenger cars in Switzerland in 2018 in MATLAB version R2018a
We developed potential vehicle classification tools based on a scientific approach in contrast to the expertise approach to investigate high levels of vehicle classification
Summary
There has been globally continuous growth in passenger car sizes and types over the past few decades. Numerous image-based vehicle classification methods have been available and are being widely used by departments of transportation These techniques lack the ability to accurately produce classification because of the occlusion, shadow, illumination, and clear definition of the measured characteristics [4,5,6,7,8,9]. This complexity will be even more for vehicles that have different classes with similar dimensions or have visually similar appearances but not similar dimensions, such as the Audi A4 versus the Audi A6. This makes it difficult to follow the development of the sizes during vehicle categorization (Table 1)
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