Traffic Prediction Using Ying-Yang Fuzzy Cerebellar Model Articulation Controller

Traffic prediction is a critical element in traffic control today. With the increase of transportation, an effective traffic prediction allows to prevent traffic problems. This research aims to propose a novel approach to traffic prediction using Ying-Yang fuzzy cerebellar model articulation controller (YY-FCMAC). The model is motivated from the famous Chinese ancient Ying-Yang philosophy, which views everything as a product of conflict-harmony process between Ying and Yang. That principle is applied to find the optimal number of clusters and fuzzy sets in the fuzzification phase of the hybrid fuzzy-neural YY-FCMAC network. The analyzed experiment on a set of real traffic data flow of the east-bound Pan Island Expressway (PIE) in Singapore shows the effectiveness of the YY-FCMAC in universal approximation and prediction