PSO-Based Adaptive Hierarchical Interval Type-2 Fuzzy Knowledge Representation System (PSO-AHIT2FKRS) for Travel Route Guidance

Urban Traffic Networks are characterized by their high dynamics and increased traffic congestion cases, leading to a more complex road management. The present research work suggests an innovative advanced vehicle guidance system based on Hierarchical Interval Type-2 Fuzzy Logic model optimized the Particle Swarm Optimization (PSO) method. Indeed, this allows intelligent prompt adjustment of network in dynamic way improves entire quality, particularly case congestions or jams, considering real-time information. best followed is selected according quality route length, together with contextual factors pertaining driver, environment, path. proposed executed simulated using SUMO (Simulation Mobility), for which four large areas situated cities Sfax, Luxembourg, Bologna Cologne have been tested. simulation results proved effectiveness learning PSO real time technique accomplish multi-objective optimality regarding two criteria: number cars that attain destination average travel time. obtained confirmed efficiency system.