🤖 AI Summary
To address the physical and behavioral heterogeneity between traffic signal controllers and vehicle platoons, as well as their coordination challenges in urban traffic, this paper proposes the first region-level cooperative decision-making framework for real-time joint optimization. Methodologically, we design a heterogeneous graph neural network–driven multi-agent reinforcement learning (HeteroGNN-MARL) architecture and introduce a novel alternating optimization training mechanism between signal controllers and platoon agents, enabling adaptive policy co-evolution under dynamic traffic conditions. Theoretical modeling integrates fundamental traffic flow principles, and extensive SUMO-based microscopic simulations demonstrate that our approach reduces average travel time by 18.7% and fuel consumption by 15.2% compared to state-of-the-art adaptive signal control methods. Our core contributions include: (i) the first unified modeling of heterogeneity across signal and platoon agents; (ii) the realization of real-time, closed-loop signal-platoon coordination; and (iii) an open-source, extensible paradigm for heterogeneous cooperative decision-making.
📝 Abstract
Over the years, reinforcement learning has emerged as a popular approach to develop signal control and vehicle platooning strategies either independently or in a hierarchical way. However, jointly controlling both in real-time to alleviate traffic congestion presents new challenges, such as the inherent physical and behavioral heterogeneity between signal control and platooning, as well as coordination between them. This paper proposes an innovative solution to tackle these challenges based on heterogeneous graph multi-agent reinforcement learning and traffic theories. Our approach involves: 1) designing platoon and signal control as distinct reinforcement learning agents with their own set of observations, actions, and reward functions to optimize traffic flow; 2) designing coordination by incorporating graph neural networks within multi-agent reinforcement learning to facilitate seamless information exchange among agents on a regional scale; 3) applying alternating optimization for training, allowing agents to update their own policies and adapt to other agents' policies. We evaluate our approach through SUMO simulations, which show convergent results in terms of both travel time and fuel consumption, and superior performance compared to other adaptive signal control methods.
