Traffic modeling and identification using a Self-adaptive Fuzzy Inference Network

Abstract

Traffic modeling and identification is an important aspect of traffic control today. With an increase in the demands on today's transportation network, an efficient system to model and understand the changes in the network is necessary for policy makers to make timely decisions which affect the overall level of service experienced by commuters. This paper proposes a novel approach to traffic modeling and identification using a Self-adaptive Fuzzy Inference Network (SaFIN). The study is performed on a set of real world traffic data collected along the Pan Island Expressway (PIE) in Singapore. By applying a hybrid fuzzy neural network in the traffic modeling task, SaFIN is able to capitalize on the functionalities of both the fuzzy system and the neural network to (1) provide meaningful and intuitive insights to the traffic data, and (2) demonstrate excellent modeling and identification capabilities for highly nonlinear traffic flow conditions.