Traffic Network Flow Prediction Using Parallel Training for Deep Convolutional Neural Networks on Spark Cloud
Yongnan Zhang, Yonghua Zhou, Huapu Lu, Hamido Fujita
Abstract
Traffic flow in a road network is mutually interactive and interdependent with each other. It is challenging to describe the dynamics of traffic network flow by using analytical methods. In this article, the deep convolutional neural network (DCNN) model is employed to address traffic network flow prediction. To improve the parameter learning efficiency confronting traffic big data, a parallel training approach is developed for the DCNN prediction model. The theoretical foundation is developed for the parallel training algorithm of the DCNN model. A master-slave parallel computing solution for traffic network flow prediction is implemented on the Spark cloud. Real data of traffic network flow are applied to verify the effectiveness of the DCNN prediction model and the parallel training algorithm. The experimental results demonstrate that the DCNN prediction model for traffic network flow outperforms the typical prediction models based on backpropagation neural networks, support vector regressions, radial basis functions, and decision tree regressions. The proposed parallel training method can improve the training efficiency and obtain global features of the entire dataset from local learning with regard to the respective data subsets.