Model-based Identification, Estimation, and Control for Large-scale Urban Road Networks

Network-level road traffic control remains a challenging problem. Macroscopic fundamental diagram (MFD) based dynamical models of large-scale urban networks enable development of model predictive perimeter control methods, which represent an efficient congestion control solution with substantial potential for practical implementation. In this paper we propose a model-based system identification method for computing the MFD parameters given measurements on historical trajectories of the traffic state and inflow demand. Furthermore, nonlinear moving horizon estimation (MHE) and model predictive control (MPC) formulations for MFD-based dynamics are presented, which enable high-performance traffic control under severe measurement noise. Microsimulation-based case studies, considering an urban network with 1500 links, where the MFD parameters obtained by the identification method are used in MHE and MPC design, demonstrate the operation of the proposed framework.