A Novel Approach for Traffic Congestion Control: Using a Modified Type-2 Fuzzy System

Road transport operators in Nigeria have been in search of measures that will assist them operate, manage and facilitate their activities on a timely dispatch. The dynamic optimization process that will accommodate far more uncertainties in the fuzzy modelling process. With the utilization of a novel approach for a modified Type-2 fuzzy system for Traffic Control Management Information System (TRACMIS-2) using Gaussian membership function for traffic congestion control will go a long way in managing their business as alternative to the usual conventional traffic control system. A fuzzy technique with higher footprint-of-uncertainty as in the case of Type-2 fuzzy systems that uses specific context data, this can lead to improved results, particularly in real-time simulation studies. However, this research focuses on designing a modified version of a type-2 fuzzy network model using Gaussian membership function for traffic congestion control. Dynamic modelling performed with real-time data obtained from Rumuokoro roundabout, in the outskirts of Port-Harcourt were tested using the Type-2 fuzzy logic toolbox. The methodology used is Model-based Process Oriented Methodology in MATLAB-SIMULINK environment. Simulation results obtained were compared with conventional Type-2 fuzzy logic system based on trapezoidal membership function and Karnik-Mendel (KM) technique with a modified version of KMmod. The switch-point algorithm is what led to improved runtimes. Results of simulations show that the modified type-2 fuzzy logic systems reduce waiting times of vehicles on signal which is an improved performance in traffic congestion control management.