SHORT-TERM TRAFFIC FLOW PREDICTION: NEURAL NETWORK APPROACH

Abstract

Much of the current activity in the area of intelligent vehicle-highway systems (IVHSs) focuses on one simple objective: to collect more data. Clearly, improvements in sensor technology and communication systems will allow transportation agencies to more closely monitor the condition of the surface transportation system. However, monitoring alone cannot improve the safety or efficiency of the system. It is imperative that surveillance data be used to manage the system in a proactive rather than a reactive manner. Proactive traffic management will require the ability to predict traffic conditions. Previous predictive modeling approaches can be grouped into three categories: (a) historical, data-based algorithms; (b) time-series models; and (c) simulations. A relatively new mathematical model, the neural network, offers an attractive alternative because neural networks can model undefined, complex nonlinear surfaces. In a comparison of a backpropagation neural network model with the more traditional approaches of an historical, data-based algorithm and a time-series model, the backpropagation model was clearly superior, although all three models did an adequate job of predicting future traffic volumes. The backpropagation model was more responsive to dynamic conditions than the historical, data-based algorithm, and it did not experience the lag and overprediction characteristics of the time-series model. Given these advantages and the backpropagation model's ability to run in a parallel computing environment, it appears that such neural network prediction models hold considerable potential for use in real-time IVHS applications.