OPTIMIZING ROAD SAFETY THROUGH A MULTI-TASK ENSEMBLE MODEL UTILIZING GENETIC ALGORITHM AND FEATURE SELECTION
Abid Saber et al.
What the paper says
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.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.