IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0269022.html

Modeling road accident fatalities with underdispersion and zero-inflated counts

Author

Listed:
  • Teerawat Simmachan
  • Noppachai Wongsai
  • Sangdao Wongsai
  • Rattana Lerdsuwansri

Abstract

In 2013, Thailand was ranked second in the world in road accident fatalities (RAFs), with 36.2 per 100,000 people. During the Songkran festival, which takes place during the traditional Thai New Year in April, the number of road traffic accidents (RTAs) and RAFs are markedly higher than on regular days, but few studies have investigated this issue as an effect of festivity. This study investigated the factors that contribute to RAFs using various count regression models. Data on 20,229 accidents in 2015 were collected from the Department of Disaster Prevention and Mitigation in Thailand. The Poisson and Conway–Maxwell–Poisson (CMP) distributions, and their zero-Inflated (ZI) versions were applied to fit the data. The results showed that RAFs in Thailand follow a count distribution with underdispersion and excessive zeros, which is rare. The ZICMP model marginally outperformed the CMP model, suggesting that having many zeros does not necessarily mean that the ZI model is required. The model choice depends on the question of interest, and a separate set of predictors highlights the distinct aspects of the data. Using ZICMP, road, weather, and environmental factors affected the differences in RAFs among all accidents, whereas month distinguished actual non-fatal accidents and crashes with or without deaths. As expected, actual non-fatal accidents were 2.37 times higher in April than in January. Using CMP, these variables were significant predictors of zeros and frequent deaths in each accident. The RAF average was surprisingly higher in other months than in January, except for April, which was unexpectedly lower. Thai authorities have invested considerable effort and resources to improve road safety during festival weeks to no avail. However, our study results indicate that people’s risk perceptions and public awareness of RAFs are misleading. Therefore, nationwide road safety should instead be advocated by the authorities to raise society’s awareness of everyday personal safety and the safety of others.

Suggested Citation

  • Teerawat Simmachan & Noppachai Wongsai & Sangdao Wongsai & Rattana Lerdsuwansri, 2022. "Modeling road accident fatalities with underdispersion and zero-inflated counts," PLOS ONE, Public Library of Science, vol. 17(11), pages 1-23, November.
  • Handle: RePEc:plo:pone00:0269022
    DOI: 10.1371/journal.pone.0269022
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0269022
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0269022&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0269022?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. Sellers, Kimberly F. & Raim, Andrew, 2016. "A flexible zero-inflated model to address data dispersion," Computational Statistics & Data Analysis, Elsevier, vol. 99(C), pages 68-80.
    2. Galit Shmueli & Thomas P. Minka & Joseph B. Kadane & Sharad Borle & Peter Boatwright, 2005. "A useful distribution for fitting discrete data: revival of the Conway–Maxwell–Poisson distribution," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 54(1), pages 127-142, January.
    3. R. W. Conway & W. L. Maxwell, 1962. "Network Dispatching by the Shortest-Operation Discipline," Operations Research, INFORMS, vol. 10(1), pages 51-73, February.
    4. Zeileis, Achim & Kleiber, Christian & Jackman, Simon, 2008. "Regression Models for Count Data in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i08).
    5. Michener, Ron & Tighe, Carla, 1992. "A Poisson Regression Model of Highway Fatalities," American Economic Review, American Economic Association, vol. 82(2), pages 452-456, May.
    6. Thanapong Champahom & Sajjakaj Jomnonkwao & Chinnakrit Banyong & Watanya Nambulee & Ampol Karoonsoontawong & Vatanavongs Ratanavaraha, 2021. "Analysis of Crash Frequency and Crash Severity in Thailand: Hierarchical Structure Models Approach," Sustainability, MDPI, vol. 13(18), pages 1-19, September.
    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. Kimberly F. Sellers & Andrew W. Swift & Kimberly S. Weems, 2017. "A flexible distribution class for count data," Journal of Statistical Distributions and Applications, Springer, vol. 4(1), pages 1-21, December.
    2. Dongying Zhan & Derek S. Young, 2024. "Finite mixtures of mean-parameterized Conway–Maxwell–Poisson models," Statistical Papers, Springer, vol. 65(3), pages 1469-1492, May.
    3. Douglas Toledo & Cristiane Akemi Umetsu & Antonio Fernando Monteiro Camargo & Idemauro Antonio Rodrigues Lara, 2022. "Flexible models for non-equidispersed count data: comparative performance of parametric models to deal with underdispersion," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 106(3), pages 473-497, September.
    4. John Haslett & Andrew C. Parnell & John Hinde & Rafael de Andrade Moral, 2022. "Modelling Excess Zeros in Count Data: A New Perspective on Modelling Approaches," International Statistical Review, International Statistical Institute, vol. 90(2), pages 216-236, August.
    5. Andrea Pellegrini & Riccardo Scarpa & Maria De Salvo & Laura Giuffrida & Giovanni Signorello, 2026. "Geographical and environmental dependencies in multivariate count models: Recreation demand for Italian national parks," Environmental Economics and Policy Studies, Springer;Society for Environmental Economics and Policy Studies - SEEPS, vol. 28(2), pages 679-702, April.
    6. Gauss Cordeiro & Josemar Rodrigues & Mário Castro, 2012. "The exponential COM-Poisson distribution," Statistical Papers, Springer, vol. 53(3), pages 653-664, August.
    7. Totterman, Stephen, 2021. "Vehicle-based recreation and compliance for three beaches in northern New South Wales," OSF Preprints ja8h6, Center for Open Science.
    8. Mevin B. Hooten & Michael R. Schwob & Devin S. Johnson & Jacob S. Ivan, 2023. "Multistage hierarchical capture–recapture models," Environmetrics, John Wiley & Sons, Ltd., vol. 34(6), September.
    9. Can Zhou & Yan Jiao & Joan Browder, 2019. "How much do we know about seabird bycatch in pelagic longline fisheries? A simulation study on the potential bias caused by the usually unobserved portion of seabird bycatch," PLOS ONE, Public Library of Science, vol. 14(8), pages 1-19, August.
    10. Jong-Hyun Kim & Yong-Gil Lee, 2021. "Factors of Collaboration Affecting the Performance of Alternative Energy Patents in South Korea from 2010 to 2017," Sustainability, MDPI, vol. 13(18), pages 1-25, September.
    11. Schmidt, Dominik & Stöckl, Thomas & Palan, Stefan, 2024. "Voting for insider trading regulation. An experimental study of informed and uninformed traders’ preferences," Journal of Banking & Finance, Elsevier, vol. 169(C).
    12. Olga Alipova & Lada Litvinova & Andrey Lovakov & Maria Yudkevich, 2018. "Inbreds And Non-Inbreds Among Russian Academics: Short-Term Similarity And Long-Term Differences In Productivity," HSE Working papers WP BRP 48/EDU/2018, National Research University Higher School of Economics.
    13. Christian Kleiber & Achim Zeileis, 2016. "Visualizing Count Data Regressions Using Rootograms," The American Statistician, Taylor & Francis Journals, vol. 70(3), pages 296-303, July.
    14. repec:plo:pone00:0224286 is not listed on IDEAS
    15. Darcy Steeg Morris & Kimberly F. Sellers, 2022. "A Flexible Mixed Model for Clustered Count Data," Stats, MDPI, vol. 5(1), pages 1-18, January.
    16. Guilherme Ribeiro & Gonçalo F. Neves & Miguel I. Grilo & Virgínia Infante & António R. Andrade, 2026. "Assessment of the automatic train protection system and its association to signals passed at danger in railway safety management," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 17(3), pages 743-758, March.
    17. Sewando, Ponsian T. & Mdoe, N. Y. S. & Mutabazi, K. D. S, 2011. "Farmers’ preferential choice decisions to alternative cassava value chain strands in Morogoro rural district, Tanzania," MPRA Paper 29797, University Library of Munich, Germany.
    18. Deborah Kanda & Jingjing Yin & Xinyan Zhang & Hani Samawi, 2025. "Efficient regression analyses with zero-augmented models based on ranking," Computational Statistics, Springer, vol. 40(2), pages 601-632, February.
    19. Merl, Robert & Palan, Stefan & Schmidt, Dominik & Stöckl, Thomas, 2023. "Insider trading regulation and trader migration," Journal of Financial Markets, Elsevier, vol. 66(C).
    20. repec:oup:beheco:v:23:y:2012:i:1:p:69-74. is not listed on IDEAS
    21. Joseph B. Kadane & Ramayya Krishnan & Galit Shmueli, 2006. "A Data Disclosure Policy for Count Data Based on the COM-Poisson Distribution," Management Science, INFORMS, vol. 52(10), pages 1610-1617, October.
    22. Burkey, Mark L. & Obeng, Kofi, 2005. "Crash Risk Reduction at Signalized Intersections Using Longitudinal Data," MPRA Paper 36281, University Library of Munich, Germany.

    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:plo:pone00:0269022. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.