mlsanityData to 14 Oct 2026

TransitGPT: A Generative AI-based framework for interacting with GTFS data using Large Language Models

arXiv 2412.06831 ↗First posted 7 Dec 2024cs.CL cs.AI stat.APcited by 5

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Abstract (arXiv)

This paper introduces a framework that leverages Large Language Models (LLMs) to answer natural language queries about General Transit Feed Specification (GTFS) data. The framework is implemented in a chatbot called TransitGPT with open-source code. TransitGPT works by guiding LLMs to generate Python code that extracts and manipulates GTFS data relevant to a query, which is then executed on a server where the GTFS feed is stored. It can accomplish a wide range of tasks, including data retrieval, calculations, and interactive visualizations, without requiring users to have extensive knowledge of GTFS or programming. The LLMs that produce the code are guided entirely by prompts, without fine-tuning or access to the actual GTFS feeds. We evaluate TransitGPT using GPT-4o and Claude-3.5-Sonnet LLMs on a benchmark dataset of 100 tasks, to demonstrate its effectiveness and versatility. The results show that TransitGPT can significantly enhance the accessibility and usability of transit data.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 27 of its 65 references that are in the map. First posted on arXiv 7 Dec 2024.

Builds on

27 of 65 references.

References · 27

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2021Training Verifiers to Solve Math Word Problemscited by 10,109
  3. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  4. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  5. 2020Measuring Massive Multitask Language Understandingcited by 7,963
  6. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,182
  7. 2023Lost in the Middle: How Language Models Use Long Contextscited by 3,455
  8. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,212
  9. 2024RULER: What's the Real Context Size of Your Long-Context Language Models?cited by 1,346
  10. 2024Large Language Models for Mathematical Reasoning: Progresses and Challengescited by 284
  11. 2023Drive Like a Human: Rethinking Autonomous Driving with Large Language Modelscited by 160
  12. 2023In-Context Impersonation Reveals Large Language Models' Strengths and Biasescited by 155
15 more
  1. 2023ExpertPrompting: Instructing Large Language Models to be Distinguished Expertscited by 142
  2. 2022CERT: Continual Pre-Training on Sketches for Library-Oriented Code Generationcited by 87
  3. 2022Machine Generated Text: A Comprehensive Survey of Threat Models and Detection Methodscited by 79
  4. 2023Receive, Reason, and React: Drive as You Say with Large Language Models in Autonomous Vehiclescited by 72
  5. 2023MLCopilot: Unleashing the Power of Large Language Models in Solving Machine Learning Taskscited by 66
  6. 2023Prompting Is All You Need: Automated Android Bug Replay with Large Language Modelscited by 40
  7. 2024Text2SQL is Not Enough: Unifying AI and Databases with TAGcited by 34
  8. 2024SQLBench: A Comprehensive Evaluation for Text-to-SQL Capabilities of Large Language Modelscited by 32
  9. 2024ACORN: Performant and Predicate-Agnostic Search Over Vector Embeddings and Structured Datacited by 32
  10. 2024Benchmarking Data Science Agentscited by 30
  11. 2023Large Language Models in Analyzing Crash Narratives -- A Comparative Study of ChatGPT, BARD and GPT-4cited by 16
  12. 2024Benchmarking the Capabilities of Large Language Models in Transportation System Engineering: Accuracy, Consistency, and Reasoning Behaviorscited by 13
  13. 2023From Words to Code: Harnessing Data for Program Synthesis from Natural Languagecited by 11
  14. 2022Bus Stop Spacings Statistics: Theory and Evidencecited by 1
  15. 2024Transit Pulse: Utilizing Social Media as a Source for Customer Feedback and Information Extraction with Large Language Modelcited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 1; 2026: 3.

5 papers.

  1. 2024Toward LLM-Agent-Based Modeling of Transportation Systems: A Conceptual Frameworkcited by 19
  2. 2025Generative AI in Transportation Planning: A Surveycited by 8
  3. 2026Earth Science Foundation Models: From Perception to Reasoning and Discoverycited by 0
  4. 2026Large Language Models in Transportation Systems Management and Operations: From Text Reasoning to Multi-modal Decision Supportcited by 0
  5. 2026Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challengescited by 0