PSO-based adaptive hierarchical interval type-2 fuzzy knowledge representation system (PSO-AHIT2FKRS) for travel route guidance

Abstract

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