LAUSR

Network-wide traffic signal control (NTSG) is one of the most important factors that impact the transportation network efficiency. Nevertheless, the NTSG problem still faces some issues impeding its development: (1) the traditional flow-based data has poor applicability to the evaluation of the network-wide traffic state, and the identification of the most delayed bottleneck intersections; (2) the existing control methods are difficult to capture the global optimal solution, owing to the lack of collaboration among multiple controllers. In this paper, to overcome the aforementioned challenges, we collect the trip-based data to evaluate the network-wide traffic state, and identify the most delayed trip-based bottlenecks as the controllers. Then, a bi-hierarchical game-theoretic (BHGT) method is proposed to solve the NTSG problem. At the lower layer, the NTSG problem is decomposed into several sub-problems of bottleneck control. A coalition game of intersections is formulated to solve the optimal signal control strategy of each single bottleneck. Furthermore, at the upper layer, a potential game is formulated to collaborate the multiple bottlenecks’ strategies solved from the lower-layer. After several iterations between two layers, the BHGT method will converge to a solution which minimizes the total travel delay of the network. Experimental results on a real-world dataset in Xuancheng City prove that the BHGT method outperforms other baseline methods in reducing the network-wide travel delay, both in low-traffic and high-traffic scenarios.