Demand and State Estimation for Perimeter Control in Large-Scale Urban Networks

State observability and demand estimation are two main issues in large-scale traffic networks which hinder real-world application of real-time control strategies. This study proposes a novel combined estimation and control framework (CECF) to develop perimeter control strategies based on macroscopic fundamental diagram (MFD). The proposed CECF is designed to operate with limited real-time traffic data and capture discrepancies in a priori demand estimates. The CECF is developed with a moving horizon estimator (MHE) that estimates traffic states, route choices and demand flows considering region accumulations and boundary flows observed from the network. The estimated traffic states are incorporated into a model predictive controller (MPC) scheme to derive the control decisions, which are then executed in the urban network. A novel accumulation-based MFD model is developed in this study to address the observability problem, which is incorporated into MHE and MPC schemes. The proposed CECF is implemented in a large-scale traffic network where several demand scenarios are tested. The results confirm the success of the CECF in overcoming observability issues and improving the performance of perimeter control strategies.