# Leveraging Large Language Models for Enhancing Public Transit Services

arXiv: https://arxiv.org/abs/2410.14147 · HTML: https://mlsanity.com/paper/2410.14147/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 38 references that are in the map.
- First posted on arXiv 18 Oct 2024.

## Builds on (most cited first, 24 of 24)

- [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)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [Emergent Abilities of Large Language Models](https://mlsanity.com/paper/2206.07682/) (2022)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [Large Language Models Encode Clinical Knowledge](https://mlsanity.com/paper/2212.13138/) (2022)
- [Multitask Prompted Training Enables Zero-Shot Task Generalization](https://mlsanity.com/paper/2110.08207/) (2021)
- [NExT-GPT: Any-to-Any Multimodal LLM](https://mlsanity.com/paper/2309.05519/) (2023)
- [Pretraining Language Models with Human Preferences](https://mlsanity.com/paper/2302.08582/) (2023)
- [Can large language models reason about medical questions?](https://mlsanity.com/paper/2207.08143/) (2022)
- [Birth of a Transformer: A Memory Viewpoint](https://mlsanity.com/paper/2306.00802/) (2023)
- [Open-vocabulary Queryable Scene Representations for Real World Planning](https://mlsanity.com/paper/2209.09874/) (2022)
- [Radiology-GPT: A Large Language Model for Radiology](https://mlsanity.com/paper/2306.08666/) (2023)
- [Context Matters: A Strategy to Pre-train Language Model for Science Education](https://mlsanity.com/paper/2301.12031/) (2023)
- [Investigating the use of ChatGPT for the scheduling of construction projects](https://mlsanity.com/paper/2302.02805/) (2023)
- [Towards Human-AI Collaborative Urban Science Research Enabled by Pre-trained Large Language Models](https://mlsanity.com/paper/2305.11418/) (2023)
- [Stargazer: An Interactive Camera Robot for Capturing How-To Videos Based on Subtle Instructor Cues](https://mlsanity.com/paper/2303.03221/) (2023)

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

- [Large Language Models and Their Applications in Roadway Safety and Mobility Enhancement: A Comprehensive Review](https://mlsanity.com/paper/2506.06301/) (2025)
- [ChatPlanner: A Large Language Model Framework for Personalized Public Transit Routing](https://mlsanity.com/paper/2606.15315/) (2026)
- [MetroLLM-Bench: Evaluating Language Models as Transit Kiosk Runtimes](https://mlsanity.com/paper/2609.10016/) (2026)

## Abstract (arXiv)

Public transit systems play a crucial role in providing efficient and sustainable transportation options in urban areas. However, these systems face various challenges in meeting commuters' needs. On the other hand, despite the rapid development of Large Language Models (LLMs) worldwide, their integration into transit systems remains relatively unexplored. The objective of this paper is to explore the utilization of LLMs in the public transit system, with a specific focus on improving the customers' experience and transit staff performance. We present a general framework for developing LLM applications in transit systems, wherein the LLM serves as the intermediary for information communication between natural language content and the resources within the database. In this context, the LLM serves a multifaceted role, including understanding users' requirements, retrieving data from the dataset in response to user queries, and tailoring the information to align with the users' specific needs. Three transit LLM applications are presented: Tweet Writer, Trip Advisor, and Policy Navigator. Tweet Writer automates updates to the transit system alerts on social media, Trip Advisor offers customized transit trip suggestions, and Policy Navigator provides clear and personalized answers to policy queries. Leveraging LLMs in these applications enhances seamless communication with their capabilities of understanding and generating human-like languages. With the help of these three LLM transit applications, transit system media personnel can provide system updates more efficiently, and customers can access travel information and policy answers in a more user-friendly manner.