Capacity Region Evaluation for Traffic Signal Control

Performance comparisons between traffic signal controllers are often conducted without<br/>checking whether a controller can handle the imposed traffic demand. When arrival rate exceeds a<br/>controller’s effective service capacity, queues grow without bound and corrupt performance statistics.<br/>In this work, we study the capacity region of a signalized intersection under stochastic arrivals. Using<br/>SUMO, we evaluate a Fixed-Time (FT) controller and a Deep Q-Network (DQN) reinforcement learning<br/>(RL) controller, and empirically identify their capacity regions by detecting whether queue lengths remain<br/>bounded or grow over time under fixed arrival rates. The results show that the RL controller operates<br/>stably over a larger capacity region than traditional methods, and achieves lower average travel time<br/>and shorter queue lengths within the shared feasible region, without exceeding the theoretical maximum<br/>service capacity.