The nonlinear impact of road safety policy implementation on the severity of road traffic crashes: A fusion of deep learning and Bayesian random parameter methods.

Road crashes remain a primary focus in the field of traffic safety research due to their potentially severe societal and individual consequences. While many studies have focused on environmental and collision-related factors that influence traffic accident fatalities, research on the impact of road safety policing, particularly its spatiotemporal heterogeneity, remains to be explored. To bridge this critical gap, we analyzed road traffic accident data from seven Australian states, along with data on the strength of each state's road safety policies. We propose a hybrid modeling strategy to examine the complex relationship between policy interventions and road traffic accident severity. This approach integrates interpretable machine learning techniques with Bayesian random parameter method, enabling the identification of high-dimensional predictors and the exploration of nonlinear relationships. Our findings indicate that the effect of road safety policy implementation on accident severity is inherently nonlinear. Specifically, our analysis suggests that higher numbers of licensed drivers and the presence of seatbelt cameras are associated with a reduction in fatal collisions, while incidents resulting solely in property damage may increase. Rigorous testing confirms the reliability of our models, offering a robust foundation for future research on traffic policy interventions.