Integrated Hybrid Framework for Urban Traffic Signal Optimization Based on Metaheuristic Algorithm and Fuzzy Multi-Criteria Decision-Making

Traffic signal control at urban intersections is one of the key determinants of the overall efficiency of the transportation system, given its direct impact on travel time, congestion levels, and emissions of exhaust fumes. This study proposes an integrated hybrid model that combines a metaheuristic Genetic Algorithm for generating potential signal timing plans with fuzzy multi-criteria decision-making (MCDM) for their evaluation and selection of the optimal solution. In order to determine the relative importance of criteria, the fuzzy methods F-AHP, F-FUCOM, and F-PIPRECIA were employed, thus providing stable assessments of criteria importance under conditions of uncertainty and expert subjectivity. The ranking of generated alternatives was performed by employing the F-TOPSIS, F-WASPAS, and F-ARAS methods, while the robust decision-making rule approach was employed to develop a robust decision-making rule by integrating multiple MCDM methods. The proposed model was tested using data collected from a real urban intersection. The results show that the integrated hybrid approach enables a significantly more reliable selection of the optimal signal timing plan and achieves higher traffic management efficiency compared to traditional methods. The proposed model provides a flexible and scalable framework that can be adapted to different types of intersections and traffic demand conditions, thereby significantly contributing to the development of modern intelligent traffic management systems.