Quantifying Traffic Patterns with Percolation Theory: A Case Study of Seoul Roads

Abstract

Urban traffic systems are characterized by dynamic interactions between congestion and free-flow states, influenced by human activity and road topology. This study employs percolation theory to analyze traffic dynamics in Seoul, focusing on the transition point $q_c$ and Fisher exponent $\tau$. The transition point $q_c$ quantifies the robustness of the free-flow clusters, while the exponent $\tau$ captures the spatial fragmentation of the traffic networks. Our analysis reveals temporal variations in these metrics, with lower $q_c$ and lower $\tau$ values during rush hours representing low-dimensional behavior. Weight-weight correlations are found to significantly impact cluster formation, driving the early onset of dominant traffic states. Comparisons with uncorrelated models highlight the role of real-world correlations. This approach provides a comprehensive framework for evaluating traffic resilience and informs strategies to optimize urban transportation systems.