A Hierarchical Model Predictive Control Approach for Signal Splits Optimization in Large-Scale Urban Road Networks

In this paper, we propose a hierarchical model predictive control (MPC) approach for signal split optimization in large-scale urban road networks. To reduce the computational complexity, a large-scale urban road network is first divided into several subnetworks using a network decomposition method. Second, the MPC optimization problem of the large-scale urban road network is presented, in which the interactions between neighboring subnetworks are described with interconnecting constraints. To coordinate the subnetworks, Lagrange multipliers are introduced to deal with interconnecting constraints among subnetworks, and an augmented Lagrange function is constructed. Then, based on dual optimization theory and a decomposition strategy, the dual optimization problem of the original MPC problem is divided into several new subproblems. In addition, we develop a coordination algorithm based on an interaction prediction approach to coordinate the resulted subproblems with a two-level hierarchical structure. Finally, experimental results by means of simulation on a benchmark road network are presented, which illustrate the performance of the proposed approach.