Can ChatGPT Enable ITS? The Case of Mixed Traffic Control via Reinforcement Learning
This work addresses the challenge of reducing reliance on domain experts for defining RL objectives in intelligent transportation systems, though it is incremental as it builds on existing LLM capabilities.
The study investigated whether novices could use ChatGPT to solve mixed traffic control problems via reinforcement learning, finding that it increased successful policies by 150% and 136% in intersection and bottleneck scenarios compared to beginners alone, though improvements were inconsistent across all tested environments.
The surge in Reinforcement Learning (RL) applications in Intelligent Transportation Systems (ITS) has contributed to its growth as well as highlighted key challenges. However, defining objectives of RL agents in traffic control and management tasks, as well as aligning policies with these goals through an effective formulation of Markov Decision Process (MDP), can be challenging and often require domain experts in both RL and ITS. Recent advancements in Large Language Models (LLMs) such as GPT-4 highlight their broad general knowledge, reasoning capabilities, and commonsense priors across various domains. In this work, we conduct a large-scale user study involving 70 participants to investigate whether novices can leverage ChatGPT to solve complex mixed traffic control problems. Three environments are tested, including ring road, bottleneck, and intersection. We find ChatGPT has mixed results. For intersection and bottleneck, ChatGPT increases number of successful policies by 150% and 136% compared to solely beginner capabilities, with some of them even outperforming experts. However, ChatGPT does not provide consistent improvements across all scenarios.