A Cooperative Agent-Based Traffic Signal Control for Vehicular Networks Under Stochastic Flow

A two-tier Cooperative Agent-based Traffic Signal control (CATS) is proposed tominimize total delay for independent-learning signal-controlled junctions. For vehicular networks with traffic congestion, a link traffic model is presented to estimate time-varying signal delay under stochastic travel demand. To capture essential features of signal-controlled junctions, an agent-based value function approximator is proposed. For the 1st tier, common cycle time and offsets are explored to achieve collaboration among control agents. For the 2nd tier, green splits are exploited to ensure scalability over entire vehicular networks. A stochastic bi-level program is presented to minimize total delays. Numerical experiments are performed at a real-data vehicular network under stochastic flow. Comparisons are made with state-of-the-art traffic signal controls. As reported, the proposed CATS outperforms other alternatives in all cases.