Abstract
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.
Original language | English |
---|---|
Pages (from-to) | 1003-1017 |
Number of pages | 15 |
Journal | Journal of Applied Statistics |
Volume | 49 |
Issue number | 4 |
DOIs | |
State | Published - 2022 |
Bibliographical note
Publisher Copyright:© 2020 Informa UK Limited, trading as Taylor & Francis Group.
Keywords
- Count data
- EM algorithm
- finite mixture models
- identifiability
- zero-truncated Poisson
ASJC Scopus subject areas
- Statistics and Probability
- Statistics, Probability and Uncertainty