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

arXiv: https://arxiv.org/abs/2407.11003 · HTML: https://mlsanity.com/paper/2407.11003/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 36 references that are in the map.
- First posted on arXiv 25 Jun 2024.

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Generative Pre-trained Transformer: A Comprehensive Review on Enabling Technologies, Potential Applications, Emerging Challenges, and Future Directions](https://mlsanity.com/paper/2305.10435/) (2023)
- [TrafficSafetyGPT: Tuning a Pre-trained Large Language Model to a Domain-Specific Expert in Transportation Safety](https://mlsanity.com/paper/2307.15311/) (2023)
- [From Text to Transformation: A Comprehensive Review of Large Language Models' Versatility](https://mlsanity.com/paper/2402.16142/) (2024)
- [Plato Dialogue System: A Flexible Conversational AI Research Platform](https://mlsanity.com/paper/2001.06463/) (2020)
- [The Role of LLMs in Sustainable Smart Cities: Applications, Challenges, and Future Directions](https://mlsanity.com/paper/2402.14596/) (2024)
- [Joint 2D-3D Breast Cancer Classification](https://mlsanity.com/paper/2002.12392/) (2020)

## Built on by (most cited first, 3 of 3)

- [ModelingAgent: Bridging LLMs and Mathematical Modeling for Real-World Challenges](https://mlsanity.com/paper/2505.15068/) (2025)
- [BabelSteering: Multilingual Safety Alignment via English Steering Vectors](https://mlsanity.com/paper/2608.16577/) (2026)
- [Large Language Models in Transportation Systems Management and Operations: From Text Reasoning to Multi-modal Decision Support](https://mlsanity.com/paper/2606.00991/) (2026)

## Abstract (arXiv)

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.