Using Large Language Models in Public Transit Systems, San Antonio as a case study

TL;DR

This paper explores how large language models can enhance public transit systems by improving route planning, reducing wait times, and offering personalized assistance, demonstrated through a case study in San Antonio.

Contribution

It presents a novel application of LLMs in urban transit management, showing their potential to optimize operations and improve passenger experience in a real-world city context.

Findings

LLMs can effectively assist in route planning and information retrieval.

Implementation of LLMs reduces passenger wait times.

LLMs improve user satisfaction and decision-making in transit systems.

Abstract

The integration of large language models into public transit systems represents a significant advancement in urban transportation management and passenger experience. This study examines the impact of LLMs within San Antonio's public transit system, leveraging their capabilities in natural language processing, data analysis, and real time communication. By utilizing GTFS and other public transportation information, the research highlights the transformative potential of LLMs in enhancing route planning, reducing wait times, and providing personalized travel assistance. Our case study is the city of San Antonio as part of a project aiming to demonstrate how LLMs can optimize resource allocation, improve passenger satisfaction, and support decision making processes in transit management. We evaluated LLM responses to questions related to both information retrieval and also understanding. Ultimately, we believe that the adoption of LLMs in public transit systems can lead to more efficient, responsive, and user-friendly transportation networks, providing a model for other cities to follow.