Integrating Pattern Features to Sequence Model for Traffic Index Prediction

• Main Authors: Yueying Zhang, Zhijie Xu, Jianqin Zhang, Jingjing Wang, Lizeng Mao
• Format: Article
• Language: English
• Published: Atlantis Press 2021-05-01
• Series: International Journal of Computational Intelligence Systems
• Subjects: Deep learning; Traffic index prediction; Pattern features learning; Sequence-to-sequence network
• Online Access: https://www.atlantis-press.com/article/125956491/view

Intelligent traffic system (ITS) is one of the effective ways to solve the problem of traffic congestion. As an important part of ITS, traffic index prediction is the key of traffic guidance and traffic control. In this paper, we propose a method integrating pattern feature to sequence model for traffic index prediction. First, the pattern feature of traffic indices is extracted using convolutional neural network (CNN). Then, the extracted pattern feature, as auxiliary information, is added to the sequence-to-sequence (Seq2Seq) network to assist traffic index prediction. Furthermore, noticing that the prediction curve is less smooth than the ground truth curve, we also add a linear regression (LR) module to the architecture to make the prediction curve smoother. The experiments comparing with long short-term memory (LSTM) and Seq2Seq network demonstrated advantages and effectiveness of our methods.