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Effective traffic control methods have great potential in alleviating networkcongestion. Existing literature generally focuses on a single control approach,while few studies have explored the effectiveness of integrated and coordinatedcontrol approaches. This study considers two representative control approaches:ramp metering for freeways and perimeter control for homogeneous urban roads,and we aim to develop a deep reinforcement learning (DRL)-based coordinatedcontrol framework for large-scale networks. The main challenges are 1) there isa lack of efficient dynamic models for both freeways and urban roads; 2) thestandard DRL method becomes ineffective due to the complex and non-stationarynetwork dynamics. In view of this, we propose a novel meso-macro dynamicnetwork model and first time develop a demonstration-guided DRL method toachieve large-scale coordinated ramp metering and perimeter control. Thedynamic network model hybridizes the link and generalized bathtub models todepict the traffic dynamics of freeways and urban roads, respectively. For theDRL method, we incorporate demonstration to guide the DRL method for betterconvergence by introducing the concept of "teacher" and "student" models. Theteacher models are traditional controllers (e.g., ALINEA, Gating), whichprovide control demonstrations. The student models are DRL methods, which learnfrom the teacher and aim to surpass the teacher's performance. To validate theproposed framework, we conduct two case studies in a small-scale network and areal-world large-scale traffic network in Hong Kong. The research outcomereveals the great potential of combining traditional controllers with DRL forcoordinated control in large-scale networks.

Demonstration-guided Deep Reinforcement Learning for Coordinated Ramp Metering and Perimeter Control in Large Scale Networks