Multi-Recurrent Networks for Traffic Forecasting

Abstract

Recurrent neural networks solving the task of short-term traffic forecasting are presented in this report. They turned out to be very well suited to this task, they even outperformed the best results obtained with conventional statistical methods. The outcome of a comparative study shows that multiple combinations of feedback can greatly enhance the network performance. Best results were obtained with the newly developed Multi-recurrent Network combining output, hidden, and input layer memories having self-recurrent feedback loops of different strengths. The outcome of this research will be used for installing an actual tool at a highway check point. The investigated methods provide short-term memories of different length which are not only needed for the given application, but which are of importance for numerous other real world tasks. 1 Introduction Forecasting the number of cars passing a check point on a highway is important for warning the people working there of upcoming heavy ...