Out of Alignment: Fixing Overlapping Segments in German Car Classification Through Data-Driven Clustering

The passenger car market has experienced a radical shift: the rise of SUV, crossover vehicles, but also Battery Electric Vehicle (BEV) and Plug-In Hybrid Vehicle (PHEV), has blurred the borders between traditional vehicle segments as well as body types, resulting in reduced applicability of conventional taxonomies of vehicle types. This study aims to provide an overview of the vehicle market by proposing a new, machine-learning-based segmentation of the entire German vehicle fleet covering the past years. We merge over 40 million registered vehicles with a technical specifications database and apply data-mining techniques to derive an improved market segmentation. We demonstrate that unsupervised learning techniques, specifically Ward and k-means clustering, yield clusters with enhanced separation, clarity, and practical usability. Clustering was applied to both raw technical features and engineered features designed to capture aspects of economy, ecology, usability, and performance. The silhouette scores can reach 0.19, a significant increase over the +0.05/−0.05 scores of the existing vehicle segments or chassis types.