通过邻居感知机制优化大规模路网中人车混行交通流。
Neighbor-Aware Reinforcement Learning for Mixed Traffic Optimization in Large-scale Networks
- 设计邻居感知奖励机制，让自动驾驶车在多路口间保持分布均衡。
- 相比最优单路口策略，平均等待时间减少39.2%；比传统信号灯低79.8%。
- 适合城市智能交通系统部署，尤其适用于多交叉口协同控制场景。
在包含人类驾驶车辆与自动驾驶车（RVs）的大规模路网中协调混合交通流，远超单一交叉口控制的挑战。本文提出一种强化学习框架，用于跨多个互联交叉口的混合交通协调。核心贡献是引入邻居感知奖励机制，使自动驾驶车在优化局部交叉口效率的同时，维持网络内分布均衡。我们在真实路网中评估该方法，证明其在应对现实交通模式方面的有效性。结果表明，与当前最优单交叉口控制策略相比，平均等待时间降低39.2%；相较于传统交通信号灯，降幅达79.8%。该框架在实现多交叉口协同控制并保持自动驾驶车分布平衡方面表现优异，为学习型解决方案在城市交通系统中的部署提供了基础。
原文摘要 · Abstract (English)
Managing mixed traffic comprising human-driven and robot vehicles (RVs) across large-scale networks presents unique challenges beyond single-intersection control. This paper proposes a reinforcement learning framework for coordinating mixed traffic across multiple interconnected intersections. Our key contribution is a neighbor-aware reward mechanism that enables RVs to maintain balanced distribution across the network while optimizing local intersection efficiency. We evaluate our approach using a real-world network, demonstrating its effectiveness in managing realistic traffic patterns. Results show that our method reduces average waiting times by 39.2% compared to the state-of-the-art single-intersection control policy and 79.8% compared to traditional traffic signals. The framework's ability to coordinate traffic across multiple intersections while maintaining balanced RV distribution provides a foundation for deploying learning-based solutions in urban traffic systems.
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