Promoted Short-term Traffic Flow Prediction Model Based on Deep Learning and Support Vector Regression

Short-term traffic flow prediction is the basis of an Intelligent Transport Systems (ITS) project. However, in current practice, the methods for short-term traffic flow prediction have encountered many challenges in fitting the traffic flow data, one is it depends too much on historical data. Therefore, a novel short-term traffic flow forecasting method based on Deep Learning and Support Vector Regression (DL-SVR) is proposed in this paper. Firstly, the DL-SVR model is composed by a Restricted Boltzmann Machine (RBM) visible inputting layer with some RBM intermediate layers and a radial SVR output layer. Furthermore, in order to enhance the generalization of the model, an improved Particle Swarm Optimization (PSO) algorithm is designed to optimize the number of nodes in the inputting layer. Finally, the DL-SVR method is compared with other typical short-term traffic flow prediction algorithms on the same computing platform. The experimental results show that the proposed DL-SVR method gets a higher accuracy in its real-time prediction. Copyright © 2019 by Science Press.