Application of Poisson Regression on Traffic Safety

Sammanfattning: This study presents a model that explains the traffic fatality by exploring the Poisson regression model using two types of explanatory variables – referred to as internal and external factors. Internal factors contain variables closely linked to traffic safety, such as speed limits and belt usage (Strandroth et al., 2012), whereas external factors comprise a set of variables that the Swedish Transport Administration cannot control, such as the economy and demographic change (Wiklund et al., 2012). The purpose of the study is to evaluate the impact that internal and external factors have on the traffic fatality. This is done by modeling the traffic fatality using internal factors and then assessing the contribution of adding external factors in the regression model with a forward variable selection strategy. This study uses Swedish traffic fatality data as monthly statistics. The main characteristics of the data are that fatalities have generally decreased with time. Also, the data is characterized by a long term cyclical pattern as well as a yearly cyclical pattern. For the purpose of modeling the impact of internal factors, a model inspired by Brüde (1995) has been adopted, using the variable time as the only explanatory variable. It is concluded that internal factors can be used to significantly explain the general trend of the development of traffic fatalities. The variables chosen to represent external factors were economic development, traffic exposure, demographic development and seasonal trend. The study concludes that the variables economic development, traffic exposure and demographic development significantly contribute to explain the long term cyclical trends, indicating that traffic fatality is a complex multivariate system where no single variable can solely explain its dynamics. The external factor seasonal trend has the most impact of the examined external factors and explains the yearly cyclical pattern by itself. The model presented in this study shows high explanatory power and overall good fit to fatality data, making it a promising tool for statistical analysis of factors contributing to fatality. Especially for the Swedish Transport Administration, the impact of external factors can be evaluated statistically. This study leaves room for further research to assess the impact of additional external factors as well as evaluating the model’s predictive power, both of interest to the Swedish Transport Administration.