Cross-comparison of Network Clustering Methods: Potential Macroscopic Fundamental Diagram (MFD)-based Applications

The macroscopic fundamental diagram (MFD) is an effective model for monitoring, predicting, and controlling urban traffic. A critical factor in accurately observing an empirical MFD is the spatial homogeneity of traffic states, which can be achieved through effective clustering of urban networks. This study explores and compares various machine learning (ML) clustering algorithms, including k -means, Gaussian mixture models (GMM), density-based spatial clustering of applications with noise (DBSCAN), and hierarchical agglomerative clustering (HAC), to enhance traffic homogeneity and spatial connectivity. Our methodology evaluates these algorithms using traffic-relevant indicators such as homogeneity, connectivity, MFD shape, and travel time estimation accuracy, contrasting them with clustering methods from the literature, such as snake similarity and sensor-bias-corrected community detection (SBCCD). The results demonstrate that ML algorithms, especially when optimized for hyperparameters, consistently surpass state-of-the-art methods in computational performance and, in most cases, achieve a good balance between homogeneity and connectivity of the resulting clusters. Specifically, GMM produces highly homogeneous clusters ideal for routing and macroscopic travel time estimation, although they lack connectivity. In contrast, HAC ensures connected clusters suitable for traffic management strategies such as perimeter control and congestion pricing. k -means achieve a balanced performance between homogeneity and connectivity, while DBSCAN effectively identifies congestion pockets that may interest transport planners. These findings highlight ML algorithms as promising alternatives to more complex methods.