An Improved ANFIS Based Traffic Flow Control Through a Novel Approach on Input Selection

Abstract

This paper introduces a new technique for efficient control of traffic flow in congested area. It is done through an optimal prediction of traffic flow, which is an important issue in intelligent transportation systems (ITS). However, the traffic flow is affected by manifold factors including nonlinear behavior of motorists. Therefore, it can be defined as an intensely non-linear time-variant system. As a practical case, the traffic system on a 5-street intersection in the city of Mashhad, Iran is tested. In this work, an innovative input selection adaptive network-based fuzzy inference system (ANFIS) approach is proposed which is an added- value to the ANFIS prediction system used in this work. ANFIS is a fuzzy system that possesses the self-training neural-network adaptive capability which has been served as an optimal controller proven by many applications. The flexibility of welcoming additional number of parameters plus with training process made ANFIS a good choice for various non-linear applications. The proposed technique has been modeled in MATLAB software and obtained results exhibit that the ANFIS model with the novel effective input determination technique represented promising outcomes. The results were compared with the classical model to support the proposed objectives.