A Computational Analysis of Traffic Cluster Dynamics Using a Percolation-Based Approach in Urban Road Networks

TL;DR

This paper introduces a novel percolation-based computational framework to analyze traffic cluster dynamics in urban road networks, revealing insights into congestion patterns and informing traffic management strategies.

Contribution

It applies percolation theory to urban traffic data, providing a new metric for traffic dynamics and demonstrating the impact of network topology and temporal factors on cluster formation.

Findings

Identification of distinct behaviors in traffic jam and free-flow clusters

Quantification of long-range traffic correlations using a new computational metric

Insights into the influence of network topology and temporal variations on traffic clusters

Abstract

Understanding the dynamics of traffic clusters is crucial for enhancing urban transportation systems, particularly in managing congestion and free-flow states. This study applies computational percolation theory to analyze the formation and growth of traffic clusters within urban road networks, using high-resolution taxi data from Chengdu, China. Presenting the road network as a time-dependent, weighted, directed graph, we identify distinct behaviors in traffic jam and free-flow clusters through the growth patterns of giant connected components (GCCs). A persistent gap between GCC size curves, especially during rush hours, highlights disparities driven by spatial traffic correlations. These are quantified through long-range weight-weight correlations, offering a novel computational metric for traffic dynamics. Our approach demonstrates the influence of network topology and temporal variations on cluster formation, providing a robust framework for modeling complex traffic systems. The findings have practical implications for traffic management, including dynamic signal optimization, infrastructure prioritization, and strategies to mitigate congestion. By integrating graph theory, percolation analysis, and traffic modeling, this study advances computational methods in urban traffic analysis and offers a foundation for optimizing large-scale transportation systems.