An Evolutionary Game-Based Dynamic Signal Control Framework for Oversaturated Urban Networks

Abstract

Urban road networks frequently operate in an oversaturated state during peak hours, where traditional traffic signal control strategies, predominantly grounded in the assumption of fully rational user behavior, fail to capture the bounded rationality inherent in drivers’ route choice decisions under congestion. To address this gap, this paper proposed a novel integrated framework that couples evolutionary game theory (EGT) with dynamic signal control, leveraging the Macroscopic Fundamental Diagram (MFD) for real-time feedback between network-wide traffic states and individual decision-making. Specifically, we model drivers within a control zone as a population choosing between two bounded-rational strategies: “waiting straight” versus “detouring”. A replicator dynamics model governs the evolution of strategy adoption, with payoffs dynamically modulated by the MFD to reflect congestion-dependent travel costs. This behavioral layer is embedded within a receding horizon control (RHC) architecture that optimizes green splits and cycle lengths in real time to minimize total zone-wide delay, solved via Particle Swarm Optimization (PSO). Extensive simulations were conducted on a 6 × 6 grid network in SUMO under high-demand conditions (network saturation, approx. 0.92). Results demonstrate that the proposed method reduces average vehicle delay by 18.7% (from 142.8 s to 116.8 s), decreases queue spillback occurrences by 32.4%, and achieves convergence to an evolutionarily stable state (ESS) within 25 minutes, outperforming fixed-time, adaptive MAXBAND, and multi-agent deep reinforcement learning (MADDPG) baselines. This work establishes a closed-loop paradigm for behavior-aware, state-responsive traffic management in severely congested urban environments.