Abstract The global escalation in road accidents has led to a substantial public health burden, with annual fatalities reaching into the millions and causing extensive economic and social disruption. While existing research has largely focused on predicting the likelihood of road accident fatalities as a classification problem, relatively few studies have investigated the underlying relationships among the complex factors contributing to such fatalities.This study aims to identify the significant determinants of road accident fatalities in Albania by employing advanced modelling approaches. The analysis is based on official statistics from the Albanian Institute of Statistics (INSTAT) covering eight years from 2018 to 2024. During this period, a total of 13,203 individuals were involved in road traffic accidents, with 1,349 fatalities recorded. Although the overall number of accidents has decreased, the fatality rate has shown a concerning upward trend.To better understand the dynamics of road accident fatalities (RAFs), this study examines the impact of driver behaviours, time factors, and road characteristics. Two regression approaches—Poisson–Lognormal (PL) and Conway–Maxwell–Poisson (CMP)regression models—are applied to model the number of fatalities concerning these key factors. These models are particularly suited to handling the over–dispersed nature of the data, in contrast to the traditional Poisson regression model, which assumes equi–dispersion. The CMP-Poisson model outperformed both, offering better model fit and more robust estimation under dispersion, validating its application in traffic fatality research with count data.By quantifying the effects of these risk factors, this study provides valuable insights for the development of targeted interventions and policy strategies aimed at reducing road accident fatalities in Albania.
Dervishi et al. (Fri,) studied this question.
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