  Urban traffic management faces significant challenges due to the dynamic
environments, and traditional algorithms fail to quickly adapt to this
environment in real-time and predict possible conflicts. This study explores
the ability of a Large Language Model (LLM), specifically, GPT-4o-mini to
improve traffic management at urban intersections. We recruited GPT-4o-mini to
analyze, predict position, detect and resolve the conflicts at an intersection
in real-time for various basic scenarios. The key findings of this study to
investigate whether LLMs can logically reason and understand the scenarios to
enhance the traffic efficiency and safety by providing real-time analysis. The
study highlights the potential of LLMs in urban traffic management creating
more intelligent and more adaptive systems. Results showed the GPT-4o-mini was
effectively able to detect and resolve conflicts in heavy traffic, congestion,
and mixed-speed conditions. The complex scenario of multiple intersections with
obstacles and pedestrians saw successful conflict management as well. Results
show that the integration of LLMs promises to improve the effectiveness of
traffic control for safer and more efficient urban intersection management.
