IDEAS home Printed from https://ideas.repec.org/a/taf/transp/v47y2024i8p1184-1204.html
   My bibliography  Save this article

Predicting crash occurrence at intersections in Texas: an opportunity for machine learning

Author

Listed:
  • Theodore Charm
  • Haoqi Wang
  • Natalia Zuniga-Garcia
  • Mostaq Ahmed
  • Kara M. Kockelman

Abstract

This paper studies the frequency of traffic crashes at intersections across Texas by employing Zero-inflated Negative Binomial (ZINB) and Negative Binomial-Lindley (NB-L) generalized linear models, as well as various tree-based machine learning (ML) methods, namely Random Forests (RF), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Bayesian Additive Regression Trees (BART) to predict the frequency of crashes at intersections. Official crash reports from 2010 through 2019 were linked to Texas' over 700,000 intersections. RF provided best prediction performance (using R-square and Root Mean Square Error metrics) while serving well for highly imbalanced crash data (with many zero cases). Sensitivity analysis highlights the practical significance of signalized intersection, annual average daily traffic, number of lanes at intersection approach, and other covariates.

Suggested Citation

  • Theodore Charm & Haoqi Wang & Natalia Zuniga-Garcia & Mostaq Ahmed & Kara M. Kockelman, 2024. "Predicting crash occurrence at intersections in Texas: an opportunity for machine learning," Transportation Planning and Technology, Taylor & Francis Journals, vol. 47(8), pages 1184-1204, November.
    • Handle: RePEc:taf:transp:v:47:y:2024:i:8:p:1184-1204
    DOI: 10.1080/03081060.2023.2177651
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/03081060.2023.2177651
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/03081060.2023.2177651?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 search for a different version of it.

    More about this item

    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:taf:transp:v:47:y:2024:i:8:p:1184-1204. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/GTPT20 .

    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.