A hybrid decision support system, focused on in-depth road risk assessment by integrating iRAP and fuzzy AHP methods

OBJECTIVE: This study aims to assess and model road risk by addressing the multidimensional complexity of road accidents, which result from the interaction of infrastructure characteristics, traffic composition, driver behavior, and environmental conditions. The main objective is to develop an integrated analytical framework capable of identifying high-risk road segments, quantifying their overall risk levels, and prioritizing intervention strategies to enhance safety. The proposed approach seeks to overcome the limitations of traditional geometry-based methods by providing an adaptive decision-support tool that better reflects the contextual and operational realities of road networks. METHODS: This study employs a hybrid methodology combining the International Road Assessment Program (iRAP) and the Fuzzy AHP (Fuzzy AHP) to analyze to 999 road sections (100 m each) located in various Algerian regions: Mascara, Ghazaouet, Djebahia, and Blida. This combination enables the development of a simplified road risk assessment model for each 100-meter section. The steps include identifying the factors affecting safety, building a hierarchical model, applying the fuzzy method to evaluate the main and sub-factors and calculating their weights, and finally classifying the sections from least to most risky based on a star scale derived from the iRAP methodology. RESULTS: The analysis revealed that infrastructure-related factors exert the strongest influence on road risk, followed by human and traffic-related variables, while environmental factors showed the least impact. The hybrid model corrected several safety overestimations observed in iRAP by incorporating behavioral and dynamic parameters. A strong correlation between the hybrid model's risk levels and actual accident data confirmed its predictive reliability and robustness. CONCLUSION: The proposed hybrid methodology offers a reliable and adaptable tool for road safety assessment. By integrating infrastructure, human, traffic, and environmental factors, it provides a realistic view of road risk. It also suggests targeted measures-such as lane separation, improved visibility, and speed management to enhance safety, especially in developing regions with limited data.