From coordination to selective intervention: an on-demand coordination method for adaptive traffic signal control,...

Coordinating multiple traffic interventions does not necessarily lead to safer or more efficient traffic operations, because redundant or poorly timed interventions may introduce additional disturbances. This study moves beyond the conventional focus on how multiple interventions should cooperate by formulating coordination as a hybrid decision-making problem that determines whether additional interventions are needed, which interventions to activate, and how to implement them. Accordingly, an on-demand coordination method is proposed to selectively coordinate adaptive traffic signal control, dynamic speed regulation, and route guidance in emerging mixed traffic comprising human-driven vehicles and connected and automated vehicles (CAVs). The method employs two functionally differentiated deep reinforcement learning agents to decouple primary signal control from auxiliary intervention selection while jointly optimizing safety and efficiency. To support risk-aware decision-making, extreme value theory (EVT) is integrated into the control framework to characterize approach-specific tail crash risk. Specifically, a non-stationary generalized extreme value model is developed to capture extreme traffic conflicts and derive value-at-risk estimates, enabling the controller to identify and prioritize severe risk states that may be overlooked by average or instantaneous safety measures. The proposed method is evaluated at two signalized intersections through simulations across various CAV market penetration rates. Compared with non-coordinated and coordinated benchmarks, the proposed method consistently reduces average waiting time and generally reduces upper-tail crash risk. These safety and efficiency benefits are accompanied by a moderate increase in fuel consumption, with the trade-off becoming less pronounced as CAV penetration increases. The findings of this study support a shift from indiscriminate multi-intervention control toward selective coordination and highlight the potential to extend EVT-based safety analysis from post hoc risk assessment to active control decision-making.