This paper addresses the problem of designing a decentralized traffic-responsive signal control solution, proposing two methods based on different formulations of the store-and-forward model: (i) the Decentralized Traffic-responsive Urban Control (DTUC) method and (ii) the Decentralized Decoupled Traffic-responsive Urban Control (D2TUC). The use of decentralized signal control strategies for large-scale urban traffic networks is a solution to this problem, since it allows for the implementation of such strategies on networks whose centralized solution is not easily scalable. As a result, strategies based on a centralized framework are not scalable. Signal control strategies for congested urban road networks designed in a centralized framework require many communication links, serious processing power, and infrastructure for the centralized coordination. A preliminary simulation-based investigation of the signal control problem for a large-scale urban road network using these methodologies demonstrates the comparative efficiency and real-time feasibility of the developed signal control methods.

Yet a different formulation leads to an open-loop constrained nonlinear optimal control problem, whose numerical solution is achieved by use of a feasible-direction algorithm. An alternative, novel methodology consists of an open-loop constrained quadratic optimal control problem, whose numerical solution is achieved via quadratic programming. The known methodology is a linear multivariable feedback regulator derived through the formulation of a linear-quadratic optimal control problem. One known and two novel methodologies, all based on the store-and-forward modeling paradigm, are presented and compared. The problem of designing network-wide traffic signal control strategies for large-scale congested urban road networks is considered.

Angelo coppola flowstate collective