Comparison of RBF and MLP neural networks in short-term traffic flow forecasting

Abstract

Expanding mathematical models and forecasting the traffic flow is a crucial case in studying the dynamic behaviors of the traffic systems these days. Artificial Neural Networks (ANNs) are of the technologies presented recently that can be used in the intelligent transportation system field. In this paper, two different algorithms, the Multi-Layer Perceptron (MLP) and the Radial Basis Function (RBF) have been discussed. In the training of the ANNs, we use historic data. Then we use ANNs for forecasting a daily real time short-term traffic flow. The ANNs are trained by the Back-Propagation (BP) algorithm. The variable coefficients produced by temporal signals improve the performance of the BP algorithm. The temporal signals provide a new method of learning called Temporal Difference Back-Propagation (TDBP) learning. We demonstrate the capability and the performance of the TDBP learning method with the simulation results. The data of the two lane street I-494 in Minnesota city are used for this analysis.