Distributed Event-Triggered Model Predictive Control for Urban Traffic Lights

Effective traffic signal control strategies are critical for traffic management in urban traffic networks. Most existing optimization-based urban traffic control approaches update the traffic signal at regular time instants, where the length of the fixed update time interval is determined based on a trade-off between the computational efficiency and the control performance. Since event-triggered control (ETC) allows for more flexible and more efficient control than conventional time-triggered control by triggering the control action by events, and since it can refrain from redundant optimization while retaining a satisfactory behavior, we use an ETC scheme for traffic light control. In addition, based on the geographically distributed feature of traffic networks, a distributed paradigm is adopted to reduce the computational complexity for the optimization. We propose a distributed threshold-based event-triggered control strategy, where the independent triggering of agents leads to an asynchronous update of traffic signals in the system. The triggered agent then solves a mixed-integer linear programming problem and updates its traffic signals. The proposed approach is evaluated under various traffic demands by simulation, and is shown to yield the best trade-off between control performance and computational complexity compared to other control strategies.