Computer Engineering and Applications ›› 2020, Vol. 56 ›› Issue (17): 258-265.DOI: 10.3778/j.issn.1002-8331.1906-0164

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Prediction Research on Road Network Traffic Flow in Background of Time and Space Analysis

LI Tongwei, WANG Qingrong   

  1. School of Electronic & Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
  • Online:2020-09-01 Published:2020-08-31



  1. 兰州交通大学 电子与信息工程学院,兰州 730070


Deep learning has been widely used in the field of traffic engineering in recent years. For the problem of single traffic flow prediction in a large road network with a single consideration and low prediction accuracy, the advantages of Long Short-Term Memory(LSTM) network in time series data processing are fully utilized. It combines the time and space analysis of road network traffic flow and uses LSTM model for prediction. Correlation calculation is carried out on the traffic flow data between the road segments in the road network, and different thresholds are set to select the original data matrix of the number representing the road segment, and the matrix is compressed to increase the operation efficiency. Finally, the compression matrix is input into the model to predict. The simulation contrast experiment is set up to verify that the proposed method can improve the prediction accuracy by 11.84% compared with other models. It is a highly efficient traffic flow prediction method.

Key words: intelligent transportation system, short-term traffic flow prediction, deep learning, road network, Long Short-Term Memory(LSTM) network



关键词: 智能交通系统, 短时交通流预测, 深度学习, 路网, 长短时记忆(LSTM)网络