Transition-Sensitive Congestion Dynamics in Heterogeneous Urban Traffic Networks Under Coordinated Reinforcement Learning

Urban traffic networks under high-demand and incident-like perturbations can evolve from stable operation to cascading congestion, increasing delay, stop-and-go traffic, fuel or energy consumption, and traffic-related emissions. These effects make congestion regulation an important component of sustainable urban traffic management. Existing signal control methods still focus mainly on local delay reduction or short-horizon response, limiting their ability to regulate congestion propagation and stress-induced network degradation. This paper proposes Mamba-PTC, a coordinated reinforcement learning framework for urban signal control in heterogeneous traffic networks. The framework combines centralized multi-intersection control with a simplified Mamba-style sequence encoder and a transition-aware objective optimized by PPO. To connect control with network-level traffic dynamics, we introduce a transition risk indicator for online regulation and macroscopic observables for evaluation, including a composite congestion measure and an instability-amplification proxy. Experiments on stressed heterogeneous urban networks show that Mamba-PTC improves the throughput–duration profile while reducing congestion degradation indicators under heavy load and perturbation. Matched control comparisons, ablation analysis, and cross-network validation further show that these gains arise from the joint effect of temporal representation, transition-aware objective design, and coordinated control. The results suggest that coordinated reinforcement learning can support sustainable network operation by regulating congestion growth in stressed urban traffic networks. The findings provide a basis for designing congestion-aware signal control strategies, robustness evaluation protocols, and future intelligent traffic management systems for stressed urban networks.