Traffic speed prediction using deep learning method

Abstract

Successful traffic speed prediction is of great importance for the benefits of both road users and traffic management agencies. To solve the problem, traffic scientists have developed a number of time-series speed prediction approaches, including traditional statistical models and machine learning techniques. However, existing methods are still unsatisfying due to the difficulty to reflect the stochastic traffic flow characteristics. Recently, various deep learning models have been introduced to the prediction field. In this paper, a deep learning method, the Deep Belief Network (DBN) model, is proposed for short-term traffic speed information prediction. The DBN model is trained in a greedy unsupervised method and fine-tuned by labeled data. Based on traffic speed data collected from one arterial in Beijing, China, the model is trained and tested for different prediction time horizons. From experiment analysis, it is concluded that the DBN can outperform Back Propagation Neural Network (BPNN) and Auto-Regressive Integrated Moving Average (ARIMA) for all time horizons. The advantages of DBN indicate that deep learning is promising in traffic research area.