A machine-learning approach for traffic state classification tackling spatial heterogeneity

This study introduces a novel classification indicator, the Feature-Weighting Speed (FWS), and a hybrid clustering approach integrating DBSCAN and Natural Breaks to address the challenge of spatial heterogeneity in traffic state classification. Key findings indicate that FWS significantly reduces cross-city variability in data distribution, as evidenced by a low Coefficient of Variation, thereby ensuring consistent classification performance across diverse urban environments. The hybrid clustering model achieves precise traffic state separation, with DBSCAN identifying thresholds for congested and free-flow speeds and Natural Breaks delineating boundaries for intermediate states. Empirical validation using large-scale data from five Chinese cities confirms that the proposed method not only achieves Davies–Bouldin Index values below 0.5 across all datasets, outperforming traditional clustering algorithms, but also demonstrates strong adaptability to varying traffic conditions. These findings underscore the practical value of the proposed framework for real-world traffic management, enabling accurate congestion pattern identification and supporting data-informed decision-making.