Multi-Level and Learning-Based Model Predictive Control for Traffic Management

This thesis focuses on management and control of traffic networks, including urban networks and freeway networks, in which we aim to reduce traffic congestion by minimizing the total time spent of all the vehicles in the network, and also consider green mobility by minimizing the total emissions produced by the vehicles. In this thesis, we have addressed the challenges of model predictive control (MPC) for traffic management in terms of computational complexity and model mismatches by developing several novel MPC-based control frameworks for urban and freeway traffic networks. More specifically, several multi-level and learning-based MPC control frameworks are proposed. First, a novel bi-level temporally-distributed MPC framework is proposed to deal with the green urban mobility issue that usually involves long-term (e.g., one year) emission constraints, and is thus computationally intractable due to the large window of the problem. Second, we employ a grammatical evolution method to generate parameterized control laws for parameterized MPC (PMPC) with application to urban traffic signal control. Third, we develop a novel combined MPC- deep reinforcement learning (DRL) multi-level control framework, in which the MPC module provides a basic control performance at a lower frequency based on a prediction model, and the DRL module works at a higher frequency to compensate for the model mismatches and external disturbances through learning. Forth, we propose a synthesis framework of reinforcement learning (RL)-based adaptive PMPC. In this framework, all components of the PMPC scheme, such as the cost function, the prediction model, the control law, the constraint set, and the terminal set, can be parameterized and adjusted by a high-level RL agent.