Spatiotemporal urban traffic safety analytical framework by integrating nonparametric approaches

Since more than 75% of the population lives in cities, it is crucial to create a safe transportation environment for all urban residents. In this context, significant efforts are required to mitigate potential accident risks and make cities more inclusive. To gain insights into an inclusive traffic safety environment and develop a system that provides useful traffic safety information accessible to all stakeholders, from end-users to decision-makers, this article aims to develop a novel nonparametric modeling framework, the Mixed-Effect Tree Ensemble with a Gaussian Process (ME-GP), for city-wide traffic safety analysis. In this study, we use police-reported accident data from Seoul (South Korea). The framework leverages the advantages of integrating nonparametric modeling approaches to predict accident risks at the road-segment level while accounting for spatiotemporal heterogeneity and unobserved data complexities. The Gaussian process, in particular, enables us to capture nonlinearities and discontinuities when estimating random parameters. Due to the nature of the police-reported accident data, Tree-ensemble is integrated with the Gaussian process. Compared to other nonparametric models, including integrated modeling approaches, ME-GP demonstrated a 15% improvement in predictive accuracy and lower variance in out-of-sample predictions, highlighting its robustness and reliability. The result revealed that demographics, traffic conditions, and road structure are the most determinant factors in accident risks. As expected, the relationship between determinant factors and accident risks is nonlinear and spatiotemporally heterogeneous. Elderly accidents were found to have a maximum accident risk of 20% higher than that of youth. In contrast, children who are also physically vulnerable showed a lower accident risk, which is partly because of school zones that effectively protect children. The findings from the framework can provide useful insights into establishing safe and inclusive urban networks.