Characterizing the Evolution of Multi-Scale Communities in Urban Road Networks

The growing abundance of traffic data offers new opportunities to uncover dynamic traffic patterns in urban road networks, providing valuable insights for promoting sustainable mobility. By leveraging these data, road segments can be grouped into communities to capture the spatiotemporal correlations driving the dynamic evolution of traffic states. However, existing distance-based methods lack the capacity to facilitate multi-scale analysis of urban traffic patterns and are limited in capturing the heterogeneity of road regions. To address this gap, in this study, we introduce a traffic-data-driven approach to detect road segment communities and extract multi-scale traffic patterns. Here, traffic data are mapped onto a dual graph of urban road networks, with node correlations weighted using Dynamic Time Warping (DTW). A hierarchical community detection algorithm is then applied to identify multi-scale communities, revealing the spatiotemporal structure of urban traffic dynamics. The robustness and effectiveness of the proposed method were tested on the road network of Chengdu. The results show that the method successfully integrates the topological structure with traffic data, capturing multi-scale spatial autocorrelation communities. By characterizing the evolution of traffic patterns, our method has potential applications in traffic prediction, traffic control, and urban planning applications, contributing to sustainable urban transportation through congestion mitigation and efficiency enhancement.