Summary of Using Large Language Models in Public Transit Systems, San Antonio As a Case Study, by Ramya Jonnala et al.
Using Large Language Models in Public Transit Systems, San Antonio as a case study
by Ramya Jonnala, Gongbo Liang, Jeong Yang, Izzat Alsmadi
First submitted to arxiv on: 25 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper explores the integration of large language models (LLMs) into San Antonio’s public transit system. The study leverages LLM capabilities in natural language processing, data analysis, and real-time communication to enhance route planning, reduce wait times, and provide personalized travel assistance. By utilizing General Transit Feed Specification (GTFS) and other public transportation information, the research highlights the transformative potential of LLMs in optimizing resource allocation, improving passenger satisfaction, and supporting decision-making processes in transit management. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study shows how using large language models can make public transportation better for people who use it. The researchers used these models to analyze data from San Antonio’s buses and trains. They found that the models could help with things like planning routes, reducing wait times, and giving personalized travel advice. This could lead to more efficient, responsive, and user-friendly transportation networks. |
Keywords
* Artificial intelligence * Natural language processing
