OPTIMIZING ROAD SAFETY THROUGH A MULTI-TASK ENSEMBLE MODEL UTILIZING GENETIC ALGORITHM AND FEATURE SELECTION

ABSTRACT This paper introduces MTES-GA-DFS, a multi-task ensemble framework for road safety prediction. The proposed approach jointly predicts three key outcomes-accident severity, time of day, and accident hotspot-providing a unified view of road safety dynamics. Multiple heterogeneous base models are trained in parallel and combined through a stacking strategy using a multi-task meta-model. The stacking process is optimized via a genetic algorithm to select effective model combinations. In addition, a dynamic feature selection method is proposed to identify task- and model-specific relevant features, reducing dimensionality while preserving predictive performance. The proposed framework facilitates proactive interventions, supports efficient resource allocation, and enables informed decision-making for accident prevention. The framework is evaluated on the US Accidents dataset (2016-2023). Experimental results show an F1-score improvement of 22.56% and a reduction in false alarms of 47.24% compared to baseline approaches, demonstrating the effectiveness of the proposed method for accurate and robust road safety prediction.