Adaptive signal control for urban traffic network gridlock

With rapid urbanization all over the world, the traffic congestion problem is becoming increasing severe. In particular, many metropolitan areas suffer from the problem of network gridlock in peak hours, which is mainly attributed to the limited street space as well as inefficient intersection signal control to cope the excessive demand. In light of this, in this paper we propose a model to describe the risk of gridlock on a network and develop an intersection control strategy to mitigate such risk. The intersection control is formulated within a decentralized agent-based framework, casting it as a dynamic portfolio management problem. Some properties of the proposed control strategy are discussed, along with numerical experiments demonstrating the performance on a grid network.