  Imagine there is a disruption in train 1 near Times Square metro station. You
try to find an alternative subway route to the JFK airport on Google Maps, but
the app fails to provide a suitable recommendation that takes into account the
disruption and your preferences to avoid crowded stations. We find that in many
such situations, current navigation apps may fall short and fail to give a
reasonable recommendation. To fill this gap, in this paper, we develop a
prototype, TraveLLM, to plan routing of public transit in face of disruption
that relies on Large Language Models (LLMs). LLMs have shown remarkable
capabilities in reasoning and planning across various domains. Here we hope to
investigate the potential of LLMs that lies in incorporating multi-modal
user-specific queries and constraints into public transit route
recommendations. Various test cases are designed under different scenarios,
including varying weather conditions, emergency events, and the introduction of
new transportation services. We then compare the performance of
state-of-the-art LLMs, including GPT-4, Claude 3 and Gemini, in generating
accurate routes. Our comparative analysis demonstrates the effectiveness of
LLMs, particularly GPT-4 in providing navigation plans. Our findings hold the
potential for LLMs to enhance existing navigation systems and provide a more
flexible and intelligent method for addressing diverse user needs in face of
disruptions.
