A mixture model with Poisson and zero-truncated Poisson components to analyze road traffic accidents in Turkey

KONŞUK ÜNLÜ H., Young D. S., YİĞİTER A., Hilal Ozcebe L. H.

Journal of Applied Statistics, vol.49, no.4, pp.1003-1017, 2022 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 49 Issue: 4
  • Publication Date: 2022
  • Doi Number: 10.1080/02664763.2020.1843610
  • Journal Name: Journal of Applied Statistics
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, ABI/INFORM, Aerospace Database, Business Source Elite, Business Source Premier, CAB Abstracts, Veterinary Science Database, zbMATH
  • Page Numbers: pp.1003-1017
  • Keywords: Count data, EM algorithm, finite mixture models, identifiability, zero-truncated Poisson, COUNT DATA, REGRESSION-MODELS, PUBLIC-HEALTH, PERSONALITY, SEVERITY, CRASHES
  • Hacettepe University Affiliated: Yes


© 2020 Informa UK Limited, trading as Taylor & Francis Group.The analysis of traffic accident data is crucial to address numerous concerns, such as understanding contributing factors in an accident's chain-of-events, identifying hotspots, and informing policy decisions about road safety management. The majority of statistical models employed for analyzing traffic accident data are logically count regression models (commonly Poisson regression) since a count–like the number of accidents–is used as the response. However, features of the observed data frequently do not make the Poisson distribution a tenable assumption. For example, observed data rarely demonstrate an equal mean and variance and often times possess excess zeros. Sometimes, data may have heterogeneous structure consisting of a mixture of populations, rather than a single population. In such data analyses, mixtures-of-Poisson-regression models can be used. In this study, the number of injuries resulting from casualties of traffic accidents registered by the General Directorate of Security (Turkey, 2005–2014) are modeled using a novel mixture distribution with two components: a Poisson and zero-truncated-Poisson distribution. Such a model differs from existing mixture models in literature where the components are either all Poisson distributions or all zero-truncated Poisson distributions. The proposed model is compared with the Poisson regression model via simulation and in the analysis of the traffic data.