Statistical Modelling on the Severity of Road Accidents in Great Britain

Great Britain has a modern road network and is well-known with the advanced technology in road engineering. Although with excellent road infrastructure, road accidents remain one of the main concerns in road safety literature among researchers and policymakers. One of the main strategies for improving road safety is to identify the contributing factors and then to develop countermeasures. There have been numerous studies that analyse road accident severity including binary outcome models, ordered discrete outcome models, unordered multinomial discrete outcome models, and other data mining approaches. The aim of this thesis is to identify the contributing factors affecting road accident severity in Great Britain and estimate the accident cost for all types of accident severity. For accident severity study, three statistical models are selected: multinomial logistic regression (MNL) model, log-linear graphical model and multinomial logistic with random effects (MNLRE) model. Markov Chain Monte Carlo (MCMC) simulation method by applying random walk Metropolis-Hastings (M-H) algorithm is used for parameter estimation in the MNLRE model. Accident cost study is investigated by applying three models: Gamma, Weibull and Log-normal distribution.