Moving Horizon Demand and State Estimation for Model Predictive Perimeter Control of Large-scale Urban Networks

Perimeter control schemes proposed to alleviate congestion in large-scale urban networks usually assume perfect knowledge of the accumulation state and inflow demands, both requiring information about the origins and destinations of drivers. Such assumptions are problematic for practice due to measurement noise and difficulty of obtaining OD-based information. We address these by a nonlinear moving horizon estimation (MHE) scheme for the combined demand and state estimation for a two region large-scale urban road network with dynamics described via macroscopic fundamental diagram. We consider various measurement configurations likely to be encountered in practice, such as measurements on regional accumulations and transfer flows without OD information, and provide results of their observability tests. A model predictive perimeter control scheme is combined with the MHE to present an application case. Simulation studies demonstrate operation of the proposed scheme.