Computer Science ›› 2019, Vol. 46 ›› Issue (3): 39-47.doi: 10.11896/j.issn.1002-137X.2019.03.005

• Surveys • Previous Articles     Next Articles

Survey on Short-term Traffic Flow Forecasting Based on Deep Learning

DAI Liang1,MEI Yang1,QIAO Chao1,MENG Yun1,LV Jin-ming2   

  1. School of Electronics and Control Engineering,Chang’an University,Xi’an 710064,China)1
    (IBM China Systems and Technology Laboratories,Xi’an 710068,China)2
  • Received:2018-04-27 Revised:2018-08-24 Online:2019-03-15 Published:2019-03-22

Abstract: Short-term traffic flow forecasting is a hot topic in the field of intelligent transportation,which is of great significance in traffic control and management.The traditional traffic flow forecasting methods are difficult to describe the internal characteristics of the traffic data accurately.Deep learningcan learn the internal complex multivariate coupled structure of the traffic flow data through its deep structure and then make a more accurate forecasting of the traffic flow,which makes deep learning a hot topic in the current traffic flow forecasting field.Firstly,the traditional traffic flow forecasting methods and the current research status of deep learning were briefly introduced.Then the methods of short-term traffic flow forecasting based on deep learningwere classified according to generative deep architecture and discriminative deep architecture.This paper also summarized the main methods of deep learning in the field of traffic flow forecasting and compared their performance.Finally,the existing problems and development directions of deep learning in short-term traffic flow forecasting were discussed.

Key words: Deep learning, Discriminative deep architecture, Generative deep architecture, Short-term traffic flow forecasting, Traffic control and management

CLC Number: 

  • TP391
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