Vehicular Ad Hoc Networks enable dynamic and self-organizing communication among vehicles and roadside units, forming a fundamental backbone for advanced intelligent transportation systems. Efficient clustering plays a crucial role in VANETs by improving communication reliability, reducing network overhead, and enhancing scalability in highly dynamic environments. This study presents a comprehensive and critical survey of partitioning-based clustering algorithms in VANETs, explicitly addressing the lack of unified evaluation frameworks for distance metric selection and cluster quality assessment in dynamic vehicular environments. The significance of this work lies in its ability to bridge the gap between theoretical clustering approaches and their practical applicability in highly dynamic VANET scenarios through a structured and reproducible evaluation framework. Unlike existing surveys that primarily provide descriptive comparisons, this work introduces a structured and reproducible evaluation framework to systematically analyze the impact of distance metrics and clustering strategies under controlled simulation conditions. Widely adopted partitioning algorithms, including K-Means, K-Medoids, CLARA, and CLARANS, are systematically analyzed under diverse environmental conditions. Each algorithm is evaluated using multiple distance metrics, namely Euclidean, Manhattan, Minkowski, and Gaussian, to quantify similarity and dissimilarity among vehicles and to identify suitable clustering approaches for varying scenarios. The study identifies key research gaps, including the absence of standardized benchmarking, limited consideration of mobility-aware metrics, and insufficient analysis of distance metric sensitivity in highly dynamic scenarios. The quality of clustering is assessed using standard validation metrics, including Silhouette Score, Davies–Bouldin Index, and Calinski–Harabasz Index, along with cluster head lifetime to capture stability characteristics. Experimental results are presented as a supporting analytical component rather than a standalone contribution, with all simulation parameters, assumptions, and evaluation settings explicitly defined. The findings indicate that clustering performance is highly scenario-dependent, and while Euclidean distance and K-Means show strong performance under specific conditions, their effectiveness varies with network density, mobility patterns, and environmental dynamics. Overall, this study contributes to advancing the field by enabling more informed, reproducible, and context-aware clustering design, thereby supporting the development of more efficient and scalable intelligent transportation systems.