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

• URL: http://arxiv.org/abs/2412.12622v1
• Date: Tue, 17 Dec 2024 07:35:56 GMT
• Title: Neighbor-Aware Reinforcement Learning for Mixed Traffic Optimization in Large-scale Networks
• Authors: Iftekharul Islam, Weizi Li,
• Abstract summary: This paper proposes a reinforcement learning framework for coordinating mixed traffic across interconnected intersections.<n>Our key contribution is a neighbor-aware reward mechanism that enables RVs to maintain balanced distribution across the network.<n>Results show that our method reduces average waiting times by 39.2% compared to the state-of-the-art single-intersection control policy.
• Score: 1.9413548770753521
• License: http://creativecommons.org/licenses/by/4.0/
• 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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