An Evolutionary Hierarchical Interval Type-2 Fuzzy Knowledge
  Representation System (EHIT2FKRS) for Travel Route Assignment
  Urban Traffic Networks are characterized by high dynamics of traffic flow and
increased travel time, including waiting times. This leads to more complex road
traffic management. The present research paper suggests an innovative advanced
traffic management system based on Hierarchical Interval Type-2 Fuzzy Logic
model optimized by the Particle Swarm Optimization (PSO) method. The aim of
designing this system is to perform dynamic route assignment to relieve traffic
congestion and limit the unexpected fluctuation effects on traffic flow. The
suggested system is executed and simulated using SUMO, a well-known microscopic
traffic simulator. For the present study, we have tested four large and
heterogeneous metropolitan areas located in the cities of Sfax, Luxembourg,
Bologna and Cologne. The experimental results proved the effectiveness of
learning the Hierarchical Interval type-2 Fuzzy logic using real time particle
swarm optimization technique PSO to accomplish multiobjective optimality
regarding two criteria: number of vehicles that reach their destination and
average travel time. The obtained results are encouraging, confirming the
efficiency of the proposed system.
