Neighbor-Aware Reinforcement Learning for Mixed Traffic Optimization in Large-scale Networks

Iftekharul Islam; Weizi Li

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arXiv · 2412.12622

Neighbor-Aware Reinforcement Learning for Mixed Traffic Optimization in Large-scale Networks

Iftekharul Islam·Weizi Li

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Abstract

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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BibTeX RIS

Iftekharul Islam, Weizi Li. 2024-12-17. Neighbor-Aware Reinforcement Learning for Mixed Traffic Optimization in Large-scale Networks. https://arxiv.org/abs/2412.12622

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