  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.
