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An efficiency-enhanced deep learning model for citywide crowd flows prediction

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Abstract

The crowd flows prediction plays an important role in urban planning management and urban public safety. Accuracy is a challenge for predicting the flow of crowds in a region. On the one hand, crowd flow is influenced by many factors such as holidays and weather. On the other hand, sample data about crowd flows are generally high-dimensional, which not only has a negative impact on the prediction accuracy but also increases computational complexity. In this paper, an efficiency-enhanced model is constructed for predicting citywide crowd flows based on multi-source data using deep learning techniques. Specifically, a data reconstruction mechanism is built with Bernoulli restricted Boltzmann machine (BRBM), for the purpose of reducing the dimension of sample data. A collaborative prediction mechanism is introduced to improve the prediction accuracy of crowd flows, in which a spatio-temporal data oriented prediction model is constructed based on bottleneck residual network that can reduce the effectively computational complexity of model training, and an auxiliary prediction to further optimize the prediction accuracy based on the fully-connected network. The proposed method is evaluated by using two open datasets. The experimental results show that our method can significantly improve the prediction accuracy and reduce the training time of the prediction model, compared with other methods.

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Acknowledgements

This work was supported by the National Nature Science Foundation of China (Nos. 61862014, 61902086, 61966009), Guangxi Natural Science Foundation of China (2018GXNSFBA281142), Innovation Project of Guangxi Department of Science and Technology (AD18281054), Open Foundation of State key Laboratory of Networking and Switching Technology in China (SKLNST-2018-1-04), Guangxi Key Laboratory of Trusted Software (kx201718).

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Authors and Affiliations

  1. Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin, 541004, China

    Zhongyi Zhai, Peipei Liu, Lingzhong Zhao & Junyan Qian

  2. State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Bo Cheng

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  1. Zhongyi Zhai
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  2. Peipei Liu
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  3. Lingzhong Zhao
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  4. Junyan Qian
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  5. Bo Cheng
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Correspondence to Lingzhong Zhao or Junyan Qian.

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Zhai, Z., Liu, P., Zhao, L. et al. An efficiency-enhanced deep learning model for citywide crowd flows prediction. Int. J. Mach. Learn. & Cyber. 12, 1879–1891 (2021). https://doi.org/10.1007/s13042-021-01282-z

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  • DOI: https://doi.org/10.1007/s13042-021-01282-z

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