Optimizing Urban Traffic Flow in Smart Cities: A Fuzzy Goal Programming Approach

The integration of new technologies, such as cloud computing, internet of things and artificial intelligent is essential for enhancing urban traffic management systems. This research aims to develop a comprehensive model for determining traffic volume in smart cities. An optimal planning algorithm was employed to analyze data, seeking the most effective solution from a set of alternatives. The proposed algorithm utilizes fuzzy weighting based on the average distance of vehicles approaching traffic lights, allowing for the determination of optimal timing for each light section. The results indicate that the algorithm outperforms fixed-time traffic lights, achieving a 15% reduction in urban traffic congestion. improvement being about 15% compared to the normal state.