Urban Intersection Safety Risk Index: Machine Learning Methods for Real-Time Classification

The safety of urban intersections is a critical concern for city planners. Technological advancements, such as LiDAR sensors, enable better risk assessment for road users. This study proposes a hybrid model that combines Post-Encroachment Time (PET) data with unsupervised machine learning techniques, specifically DBSCAN clustering, to detect traffic anomalies. A generalized Pareto distribution (GPD) is then applied to estimate a risk index. Finally, categorical safety risk classification is performed using an optimizable neural network (ONN), support vector machine (OSVM), efficient logistic regression (ELR), and Gaussian Naive Bayes (GNB). The impact of these methods is evaluated in real-time for urban traffic management in Trois-Rivieres, Quebec, Canada. This work aims to assist decision-makers in urban traffic planning and accident prevention.