Evaluating spatial and temporal effects of accidents likelihood using random effects panel count model

The application of Poisson and Negative Binomial models has been widely used in modeling road accident count. However, several restrictions on the data have been highlighted in the use of such model. Among which are the assumption of variance and mean to be equal, no serial correlation exist and the effect of unmeasured variables that may affect the dependent variable or so called the unobserved heterogeneity. Hence, an appropriate solution to this issue is to treat the data in a form time series and cross section data panel using panel model approach. This study analyzes the number of road accidents occurrences using time series cross sectional data for 14 states in Malaysia. The random effects negative binomial (RENB) model and the cross-sectional negative binomial (NB) models are examined. The models were developed to identify the contributing factors that affect the number of road accidents in Malaysia. We examine various factors associated with road accidents occurrence that includes the registered vehicle in the state, the amount of rainfall, the number of rainy day, time trend and the monthly effect of seasonality. Various model specifications were estimated. The specification comparisons indicate benefit from using the NB model when spatial and temporal effects are unobserved. While the RENB model appears to be more superior in the present case of accident count when incorporating temporal and cross sectional variations in which offers advantages in model flexibility.