Demand estimation for perimeter control in large-scale traffic networks

State observability and demand estimation are two main issues in large-scale traffic networks which hinder realworld 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 in a model predictive controller (MPC) to derive future control decisions in the CECF, which are then executed in the urban network. A novel accumulation based MFD model is developed in this study to address observability problem, which is incorporated in MHE and MPC as an analytical approximation of the urban network. The proposed CECF is implemented in a numerical simulation of a large-scale traffic network and preliminary scenarios are tested. The results confirm the success of the CECF to avoid observability issues and develop perimeter control strategies.