Signal Control Method for Urban Road Networks Based on Dynamic Identification of Critical Nodes

Abstract

Ensuring the reliable operation of all nodes in a road network is often challenging due to the influence of managed resources and other dynamic factors. This study proposes a method for identifying critical nodes based on multi-attribute decision-making, aimed at enhancing traffic efficiency and reliability. By utilizing dynamic traffic flow data and real-time ranking of node criticality, an adaptive signal optimization approach was developed to establish a collaborative control method for road network signals. First, a quantitative analysis was conducted to evaluate the impact of road network topology, traffic volume, and travel time reliability, enabling a comprehensive ranking of critical nodes. Subsequently, based on real-time traffic flow and critical node rankings, a signal collaborative control method was established to optimize travel time reliability while mitigating congestion and resource inefficiencies. Case analysis revealed that nodes with higher OD (origin–destination) pairs do not necessarily exhibit high traffic flow or criticality, underscoring the importance of targeted signal control strategies. The results demonstrate that the proposed optimization method effectively improves the dynamic reliability and operational efficiency of road networks while contributing to sustainable transportation by enhancing adaptability to traffic fluctuations. This study provides theoretical and practical references for advancing sustainable traffic management and supporting the transition to smarter transportation systems.