Long-Term Traffic Prediction Based on LSTM Encoder-Decoder Architecture

Abstract

Accurate traffic flow prediction is becoming increasingly important for transportation planning, control, management, and information services of successful. Numerous existing models focus on short-term traffic forecasts, but effective long-term forecasting of traffic flows have become a challenging issue in recent years. To solve this problem, this paper proposes a deep learning architecture which consisting of two parts: the long short-term memory encoder-decoder structure at the bottom and the calibration layer at the top. In the encoder-decoder model, we propose an hard attention mechanism based on learning similar patterns to enhance neuronal memory and reduce the accumulation of error propagation. To correct some of the missing details, we design a control gate in the calibration layer to learn the predicted data in groups according to different forms. The proposed method is evaluated on real-world datasets and compared with other state-of-the-art methods. It is verified that our model can accurately learn local feature and long-term dependence, and has better accuracy and stability in long-term sequence prediction.