A fuzzy-based planning method and meta-heuristic optimization for traffic signal control

To regulate traffic volume and average vehicle delay, traffic signal control systems aim to develop a computational model to examine vehicle lines on urban roadways. Based on the optimization of vehicle speed on each street and traffic light timing, a hybrid traffic signal control system based on fuzzy logic and wild horse optimization algorithm (HFWHO-TSC) is introduced in this study. The first phase is optimized using the wild horse meta-heuristic algorithm (WHO) technique, and the timing of the traffic lights is optimized using fuzzy logic depending on the traffic intensity of the nearby streets in various directions of the intersection. Additionally, an iterative loop is used in this work to find the ideal fuzzy time for traffic light lighting. The proposed hybrid system is compared with two other controller systems: a fixed-time signal controller and a signal controller that combines particle swarm optimization algorithms and fuzzy logic in a hybrid manner (HFPSO-TSC). With a performance gain of almost 37% over the fixed-time traffic controller and 20% over HFPSO-TSO, respectively, the HFWHO-TSC solution outperformed the others. The proposed hybrid system's performance effectively reduces both traffic congestion and driver annoyance brought on by signals. Additionally, it lessens fuel consumption and pollution.