IDEAS home Printed from https://ideas.repec.org/a/spr/pubtra/v17y2025i2d10.1007_s12469-025-00395-w.html
   My bibliography  Save this article

TransitGPT: a generative AI-based framework for interacting with GTFS data using large language models

Author

Listed:
  • Saipraneeth Devunuri

    (University of Illinois at Urbana Champaign)

  • Lewis Lehe

    (University of Illinois at Urbana Champaign)

Abstract

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.

Suggested Citation

  • Saipraneeth Devunuri & Lewis Lehe, 2025. "TransitGPT: a generative AI-based framework for interacting with GTFS data using large language models," Public Transport, Springer, vol. 17(2), pages 319-345, June.
    • Handle: RePEc:spr:pubtra:v:17:y:2025:i:2:d:10.1007_s12469-025-00395-w
    DOI: 10.1007/s12469-025-00395-w
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12469-025-00395-w
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s12469-025-00395-w?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Rafael H. M. Pereira & Pedro R. Andrade & João Pedro Bazzo Vieira, 2023. "Exploring the time geography of public transport networks with the gtfs2gps package," Journal of Geographical Systems, Springer, vol. 25(3), pages 453-466, July.
    2. Yan, Xiang & Bejleri, Ilir & Zhai, Liang, 2022. "A spatiotemporal analysis of transit accessibility to low-wage jobs in Miami-Dade County," Journal of Transport Geography, Elsevier, vol. 98(C).
    3. Sirapop Para & Thanachok Wirotsasithon & Thanisorn Jundee & Merkebe Getachew Demissie & Yoshihide Sekimoto & Filip Biljecki & Santi Phithakkitnukoon, 2024. "G2Viz: an online tool for visualizing and analyzing a public transit system from GTFS data," Public Transport, Springer, vol. 16(3), pages 893-928, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Singer, Matan E. & Cohen-Zada, Aviv L. & Martens, Karel, 2022. "Core versus periphery: Examining the spatial patterns of insufficient accessibility in U.S. metropolitan areas," Journal of Transport Geography, Elsevier, vol. 100(C).
    2. Vanessa Brum-Bastos & Antonio Páez, 2023. "Hägerstrand meets big data: time-geography in the age of mobility analytics," Journal of Geographical Systems, Springer, vol. 25(3), pages 327-336, July.
    3. Daniela Arias-Molinares & Juan Carlos García-Palomares & Gustavo Romanillos & Javier Gutiérrez, 2023. "Uncovering spatiotemporal micromobility patterns through the lens of space–time cubes and GIS tools," Journal of Geographical Systems, Springer, vol. 25(3), pages 403-427, July.
    4. Sirapop Para & Thanachok Wirotsasithon & Thanisorn Jundee & Merkebe Getachew Demissie & Yoshihide Sekimoto & Filip Biljecki & Santi Phithakkitnukoon, 2024. "G2Viz: an online tool for visualizing and analyzing a public transit system from GTFS data," Public Transport, Springer, vol. 16(3), pages 893-928, October.
    5. Gao, Wenxiu & Cui, Miaocun & Pan, Entong & Loo, Becky P.Y., 2024. "Green commuting within the x-minute city: Towards a systematic evaluation of its feasibility," Journal of Transport Geography, Elsevier, vol. 121(C).
    6. Saipraneeth Devunuri & Shirin Qiam & Lewis J. Lehe, 2024. "ChatGPT for GTFS: benchmarking LLMs on GTFS semantics... and retrieval," Public Transport, Springer, vol. 16(2), pages 333-357, June.
    7. Xu, Minhao & Shuai, Bin & Wang, Xin & Liu, Hongyi & Zhou, Hui, 2023. "Analysis of the accessibility of connecting transport at High-speed rail stations from the perspective of departing passengers," Transportation Research Part A: Policy and Practice, Elsevier, vol. 173(C).
    8. Phillip R. Carleton & J. David Porter, 2025. "Modeling transit travel times for predicting equity improvements," Public Transport, Springer, vol. 17(2), pages 565-590, June.
    9. Bjørnson Lunke, Erik, 2023. "Accessibility in a multi-ethnic city: Residential trade-offs among first-time parents," Journal of Transport Geography, Elsevier, vol. 107(C).
    10. Guo, Jing & Brakewood, Candace, 2024. "Analysis of spatiotemporal transit accessibility and transit inequity of essential services in low-density cities, a case study of Nashville, TN," Transportation Research Part A: Policy and Practice, Elsevier, vol. 179(C).
    11. Fernando H. Taques & Coro Chasco & Flávio H. Taques, 2025. "Integrating big data with KNIME as an alternative without programming code: an application to the PATSTAT patent database," Journal of Geographical Systems, Springer, vol. 27(1), pages 31-61, January.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:pubtra:v:17:y:2025:i:2:d:10.1007_s12469-025-00395-w. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.