Modelling road accident fatalities with underdispersion and zero-inflated counts

BackgroundThailand was rank second in the world in 2013 on the road accident fatality (RAF) rate, killing 36.2 of every 100,000 Thai peoples. In the past decade, during Songkran festival, the traditional Thai new year, the number of road traffic accidents (RTAs) was markedly higher than normal day life, but few studies have yet investigated this issue as the effect of festivity. The objective of this study was to investigate factors contributing to RAF using various count regression models. MethodsData of 20,229 accidents in 2015 were collected from the Department of Disaster Prevention and Mitigation, Thailand. Poisson, Conway-Maxwell-Poisson, and their Zero-Inflated versions were applied to analyze factors associated with the number of fatalities in an accident. ResultsThe RAFs in Thailand follow a count distribution with underdispersion and excessive zeros which is rare. The best fitting model, the ZICMP regression model returns significant predictors (road characteristics, weather conditions, environmental conditions, and month) on the number of fatalities in an accident. The model consists of the count part encapsulating both non-excess zeros and death counts and the zero-part representing the considerable number of zeros during the festival months. The estimated proportion of the zero-part is 0.275 accounting for 5,563 non-fatal accidents. More specifically, the excessive number of no deaths can be explained by the month factor. The mean number of fatalities was lower in the festive periods than other months, with the highest in November. ConclusionFor long, Thai authorities have put a lot of efforts and resources into improving road safety over the festival weeks, often they failed. This study indicates that peoples risk perception and public awareness of RAFs are mislead. Instead, nationwide road safety should have been announced by the authorities to raise the awareness of society towards everyday personal safety and the safety of others.