Automatic Prediction of Traffic Flow Based on Deep Residual Networks

  • Rui Zhang
  • Nuofei Li
  • Siyuan Huang
  • Peng Xie
  • Hongbo Jiang
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 747)


Traffic flow often contains massive amounts of information that is related to location and shows some regularity. And the traffic flow analysis based on trajectory data has become one of the most popular research topics in recent years. With the wide application of deep learning and for its higher accuracy than other approaches, methods such as convolution neural network and deep residual network have been introduced in traffic flow research and achieve good results. However, these methods usually require the training of a large number of parameters, which leads to some problems. For example, frequent manual adjustment is needed, and some parameters cannot be dynamically adjusted with the training process. We find that learning rate plays a crucial role in all parameters, which has important influence on the training speed of the residual network. In other words, the soundness of traffic flow predication results depends on the learning rate. Hence, we propose G4 algorithm to automatically determine the learning rate. It can be adjusted automatically in the process of trajectory data mining, and therefore solve the traffic flow prediction problem. Experiments on real data sets show that our method is effective and superior over some traditional optimizing methods of traffic flow analysis.


Automatic prediction Traffic flow Trajectory Deep residual network Fourier series 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Rui Zhang
    • 1
    • 2
    • 3
  • Nuofei Li
    • 3
  • Siyuan Huang
    • 3
  • Peng Xie
    • 3
  • Hongbo Jiang
    • 4
  1. 1.Hubei Key Laboratory of Transportation Internet of ThingsWuhan University of TechnologyWuhanChina
  2. 2.Hubei Key Laboratory of Inland Shipping TechnologyWuhan University of TechnologyWuhanChina
  3. 3.School of Computer Science and TechnologyWuhan University of TechnologyWuhanChina
  4. 4.School of Electronic Information and CommunicationsHuazhong University of Science and TechnologyWuhanChina

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