IDEAS home Printed from https://ideas.repec.org/a/wly/jnljps/v2025y2025i1n3900755.html

Road Accident Case Modeling in Ghana: A Perspective of Negative Binomial–Based Generalized Linear Models

Author

Listed:
  • Stephen Adomako
  • Irene Kafui Vorsah Amponsah
  • David Kwamena Mensah
  • James Damsere-Derry

Abstract

The gravity of casualties due to road traffic accidents poses a threat to national development, and this requires pragmatic and actionable road traffic policy and regulation restructuring. This restructuring in policy and regulations can be best achieved through appropriate statistical modeling framework in the space of large road traffic accident data in which hidden data issues are inevitable. In this regard, this paper investigates the perspective of negative binomial generalized linear models (NBGLMs) with and without LASSO regularization in modeling road traffic accident casualties in Ghana, in which predictor space data issues are inevitable. Implementation using real road traffic accident casualty data compiled by Building and Road Research Institute in Ghana from 2017 to 2021 illustrates the potential of the LASSO negative binomial generalized linear model (LNBGLM) over its unregularized counterpart NBGLM in terms of both fitting and predictive performances. Model assessment was quantified in statistics such as deviance, AIC, BIC, and graphical predictive performance analysis. In particular, results show that LNBGLM is more suitable than NBGLM in estimating road accident casualties in Ghana with type of weather, vehicle involved in an accident, location, and age of victims as major determinants. The practical implications of the results become apparently clear and realistic, examining the state of the identified predictors on our roads currently.

Suggested Citation

  • Stephen Adomako & Irene Kafui Vorsah Amponsah & David Kwamena Mensah & James Damsere-Derry, 2025. "Road Accident Case Modeling in Ghana: A Perspective of Negative Binomial–Based Generalized Linear Models," Journal of Probability and Statistics, John Wiley & Sons, vol. 2025(1).
    • Handle: RePEc:wly:jnljps:v:2025:y:2025:i:1:n:3900755
    DOI: 10.1155/jpas/3900755
    as

    Download full text from publisher

    File URL: https://doi.org/10.1155/jpas/3900755
    Download Restriction: no
    File URL: https://libkey.io/10.1155/jpas/3900755?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
    ---><---

    References listed on IDEAS

    as
    1. Yudhie Andriyana & Rinda Fitriani & Bertho Tantular & Neneng Sunengsih & Kurnia Wahyudi & I Gede Nyoman Mindra Jaya & Annisa Nur Falah, 2023. "Modeling the Cigarette Consumption of Poor Households Using Penalized Zero-Inflated Negative Binomial Regression with Minimax Concave Penalty," Mathematics, MDPI, vol. 11(14), pages 1-13, July.
    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.

      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:wly:jnljps:v:2025:y:2025:i:1:n:3900755. 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: Wiley Content Delivery (email available below). General contact details of provider: https://onlinelibrary.wiley.com/journal/5139 .

      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.