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%.

在过去的几十年中，全球乘用车的尺寸和类型一直在持续增长。为了在这种情况下评估车辆规格的发展并评估动力总成技术（取决于充电站和车辆税收政策）的环境变化，我们需要对车辆的组成有详细的了解。因此，本文旨在介绍一种新颖的数学方法，该方法使用均值模糊聚类算法Fuzzy C均值（FCM）和非模糊聚类算法K均值（KM）对尺寸特征进行分段。我们分析了所提出算法的性能，并将其与瑞士专家细分进行了比较。在真实数据集上的实验表明，与KM相比，FCM分类器与专家细分具有更好的相关性。此外，具有五个聚类的FCM的输出表明，由于该算法能够识别和合并来自非监督数据集的尺寸属性，因此该算法在准确的车辆分类方面具有出色的性能。它在车辆分类方面的表现令人鼓舞，平均准确率达79％，平均阳性预测值达75％。