Unveiling Multistability in Urban Traffic Through Percolation Theory and Network Analysis

Traffic congestion poses a persistent challenge for modern cities, yet the complex behavior of urban road networks-particularly multistability in traffic flow-remains poorly understood. To address this gap, we analyzed a high-resolution traffic dataset from four Chinese cities over 20 working days (5-min intervals), applying percolation theory to characterize system performance via congestion rate (<i>f</i>) and the size of the largest functional cluster (<i>G</i>). Our analysis revealed clear bimodal and multimodal distributions of <i>G</i> versus <i>f</i> across different periods, ruling out random failure models and confirming the presence of multistability. Leveraging data-driven clustering and classification techniques, we demonstrated that road segments with high betweenness centrality are disproportionately likely to become congested, and that the top 1% most topologically important roads accurately predict both stable state types and the joint behavior of <i>G</i> and <i>f</i>. These findings offer the first large-scale empirical evidence of multistability in urban traffic, laying a quantitative foundation for forecasting phase transitions in congestion and informing more effective traffic management strategies.