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Short-term traffic flow prediction based on SAE and its parallel training

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Abstract

The alleviation of traffic congestion relies on efficient traffic control and traffic guidance, which are based on real-time short-term traffic flow prediction. In this paper, the stacked autoencoder (SAE) deep learning model with powerful feature learning capability is selected to predict the traffic flow on road sections. The process of training SAE includes the pre-training phase and the fine-tuning phase, which mainly apply the BP algorithm. However, the process of training SAE is time-consuming and cannot meet the real-time performance of modern application systems. This paper proposes a parallel training strategy for the SAE prediction model based on data parallel mode. The gradient solution process in our algorithm satisfies the conditions of parallel computing, so the training process can be designed in a parallel manner. The original dataset is distributed to some computing nodes, which are work nodes. The work node is responsible for gradient calculation using the local data. The task of the sole master node is to synthesize the gradient calculation results and then broadcast the updated gradient to each work node. The simulation results show that the SAE-based prediction model achieves better results than the traditional model, and the parallel algorithm reduces the running time of training processes.

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Data availability and access

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

This work was financially supported by the National Natural Science Foundation of China (Grant No. 62373037).

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Author notes

  1. Xiaoxue Tan and Lu Zhao contributed equally to this work.

Authors and Affiliations

  1. School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, 100044, China

    Xiaoxue Tan & Yonghua Zhou

  2. China Mobile (Hangzhou) Information Technology Co., Ltd, Hangzhou, 310006, China

    Lu Zhao

  3. Zhongguancun Smart City Co., Ltd, Beijing, 100071, China

    Yiduo Mei

Authors
  1. Xiaoxue Tan
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  2. Yonghua Zhou
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  3. Lu Zhao
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  4. Yiduo Mei
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Contributions

Conceptualization: Yonghua Zhou, Yiduo Mei; Methodology: Lu Zhao; Formal analysis and investigation: Xiaoxue Tan; Writing - original draft preparation: Xiaoxue Tan, Lu Zhao; Writing - review and editing: Yonghua Zhou, Yiduo Mei; Funding acquisition: Yonghua Zhou; Resources: Yonghua Zhou; Supervision: Yonghua Zhou,Yiduo Mei.

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Correspondence to Yonghua Zhou or Yiduo Mei.

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Tan, X., Zhou, Y., Zhao, L. et al. Short-term traffic flow prediction based on SAE and its parallel training. Appl Intell 54, 3650–3664 (2024). https://doi.org/10.1007/s10489-023-05157-4

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