Recursive Prediction of Traffic Conditions with Neural Network Models

This paper presents a recursive traffic flow prediction algorithm using artificial neural networks. The system prediction model is specified based on the understanding of how disturbances in traffic flow are propagated, and the order of the model is determined by correlation analysis. The parameters of the model, on the other hand, can be obtained through nonlinear optimization. Preliminary studies show that this approach can yield reasonably accurate results.