Can RL optimize urban traffic for both pedestrians and vehicles?
Joint Pedestrian and Vehicle Traffic Optimization in Urban Environments using Reinforcement Learning
April 8, 2025
https://arxiv.org/pdf/2504.05018This paper explores using deep reinforcement learning (DRL) to optimize traffic flow for both vehicles and pedestrians in a real-world urban corridor. It uses real-world data, rather than simulations, to train a single-agent policy to control eight traffic signals, including intersections and mid-block crosswalks.
Relevant to LLM-based multi-agent systems, this research highlights:
- Real-world data application: The use of real-world Wi-Fi and video data for training demonstrates the feasibility of applying DRL to complex real-world scenarios. This is crucial for multi-agent LLM systems which will need to interact with real-world information.
- Single-agent control of multiple entities: The single agent successfully coordinates multiple traffic signals. This is relevant to scenarios where a single LLM might orchestrate the actions of multiple agents or components.
- Emergent behavior: The DRL agent learned complex behaviors like "green wave" coordination and adaptive switching frequency without explicit programming, suggesting the potential for LLMs to develop sophisticated strategies in multi-agent contexts.
- Multi-objective optimization: The system successfully balances the competing objectives of minimizing wait times for both vehicles and pedestrians, showcasing the possibility of using DRL/LLMs to address scenarios with multiple, potentially conflicting goals.
- Generalization: The trained policy performs well even under traffic conditions not seen during training, highlighting the robustness and adaptability that is desirable in multi-agent LLM deployments.