A Comparison of Traffic Flow Prediction Methods Based on DBN

Accurate and real-time traffic flow prediction nowadays shows more and more dependence on big transportation data. Deep learning, a powerful method for feature learning, has turned out to be an effective tool to cope with these explosive data. Recently, deep models, especially unsupervised models like deep belief networks (DBN) and stacked autoencoder (SAE), are being employed into the field of traffic research and have shown great prospect. However, there is still a vacancy in the exploration on comparing the performances of different kinds of deep architectures to find an optimal solution. In this paper, we set up two deep-learning-based traffic flow prediction models for feature extraction and performances comparison: One is a deep belief networks (DBN) based on restricted Boltzmann machines (RBMs) that have Gaussian visible units and binary hidden units, and the other is a DBN based on RBMs with all units being binary. A conclusion is drawn where the former one performs better in traffic flow prediction after a series of experiments.