Traffic Flow Data Prediction Using Residual Deconvolution Based Deep Generative Network

Abstract

Traffic flow prediction is quite crucial for estimating the future traffic states, efficient and accurate prediction models greatly contribute to the smooth traffic of road networks. However, existing methods mainly concentrate on short-term prediction. The challenging task of long-term flow prediction for the next day, as the important reference of traffic management, is still not well solved. In this paper, we present a residual deconvolution based deep generative network (RDBDGN) to handle the problem of long-term traffic flow prediction. The proposed method consists of a generator and a discriminator. The generator is composed of multi-channel residual deconvolutional neural networks, and the discriminator contains a convolutional neural network which aims to optimize the adversarial training process. The experiments are evaluated based on the traffic flow data of elevated highways, presented results demonstrate that our approach outperforms the state-of-the-art works.

Keywords

• Deep learning
• intelligent transportation system
• RDBDGN
• traffic flow prediction