计算机工程 ›› 2020, Vol. 46 ›› Issue (1): 31-37.doi: 10.19678/j.issn.1000-3428.0055105

• 热点与综述 • 上一篇    下一篇

基于时空相关性的短时交通流量预测方法

闫杨, 孙丽珺, 朱兰婷   

  1. 青岛科技大学 信息科学技术学院, 山东 青岛 266061
  • 收稿日期:2019-06-04 修回日期:2019-08-15 出版日期:2020-01-15 发布日期:2019-08-21
  • 作者简介:闫杨(1994-),男,硕士研究生,主研方向为智能交通、物联网;孙丽珺,副教授、博士;朱兰婷,硕士研究生。
  • 基金项目:
    国家自然科学基金(61671261);国家自然科学基金青年基金(61802217)。

Short-Term Traffic Flow Prediction Method Based on Spatiotemporal Relativity

YAN Yang, SUN Lijun, ZHU Lanting   

  1. College of Information Science and Technology, Qingdao University of Science and Technology, Qingdao, Shandong 266061, China
  • Received:2019-06-04 Revised:2019-08-15 Online:2020-01-15 Published:2019-08-21

摘要: 新一代智能交通系统的智能出行、交通大数据智能化决策需要精准及时的短时交通流量预测,深度学习通过机器学习技术自身产生特征,可为短时交通流量预测提供解决方法。以深度学习模型为基础,提出一种结合Conv-GRU和Bi-GRU的短时交通流量预测方法,利用卷积-门控循环单元提取交通流量的时空特征,通过双向门控循环单元提取交通流量的周期特征,将提取的特征进行融合得到交通流量的预测值。实验结果表明,该方法能够准确地预测短时交通流量,与Conv-LSTM方法相比,收敛速度较快,具有更短的运行时间。

关键词: 短时交通流量, 卷积-门控循环单元, 双向门控循环单元, 时空特征, 周期性特征

Abstract: The intelligent travel of the new generation intelligent traffic system and the intelligent decision-making of traffic big data need accurate and timely short-term traffic flow prediction.Deep learning can generate features by machine learning technology,which provides a new solution to the short-term traffic prediction.Based on deep learning model,this paper proposes a short-term traffic flow prediction method that combines Convolution-Gated Recurrent Unit(Conv-GRU) and Bi-directional Gated Recurrent Unit(Bi-GRU).The proposed method uses Conv-GRU to extract the spatial feature of traffic flow and Bi-GRU to extract the periodic feature of traffic flow.The extracted features are integrated to obtain the prediction value of traffic flow.Experimental results show that the proposed method can accurately predict the short-term traffic flow.Compared with the Conv-LSTM method,this method has faster convergence speed and shorter running time.

Key words: short-term traffic flow, Convolution-Gated Recurrent Unit(Conv-GRU), Bi-directional Gated Recurrent Unit(Bi-GRU), spatiotemporal feature, periodic feature

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