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Identifying key grid cells for crowd flow predictions based on CNN-based models with the Grad-CAM kit

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Abstract

Scholars have long sought to identify key city locations that have a pronounced effect on the flow of people under various conditions. Identifying key locations makes it possible for government and/or enterprises to obtain accurate estimates as to the flow of people and thereby formulate reasonable management strategies. Note however that much of the previous research based on professional knowledge or machine learning fails to provide accurate results. Some researchers have employed CNN-based models to predict the flow of people, claiming that those models can extract freatures from multiple locations to improve prediction accuracy. Theoretically, the features extracted using CNN-based models could be used themselves as key locations for specific problems; however, the fact that the features are consolidated via multiple operations often renders interpretation difficult when applied to a real-world setting. In the current study, we developed a novel approach to the identification of key locations based on the results of a CNN-based model and the Grad-Cam kit. The structure of a CNN-based models can have a profound effect on the results of the Grad-Cam kit; therefore, we compared the results obtained using three state-of-art models as well as our CNN-LSTM. Actual flow patterns based on telecommunication data in Taipei were used to verify the efficacy of the proposed method.

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Data Availability

The people flow data used in this paper cannot be made public because it is regulated by the privacy law of the Taiwan government.

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Acknowledgments

This research was funded by Ministry of Science and Technology Taiwan, grant number MOST 107-2119-M-224-003-MY3, MOST 110-2121-M-224-001, MOST 110-2221-E-006-176-, MOST 108-2621-M-006-007-, MOST 111-2121-M-224-001 and MOST 111-2221-E-006-187-MY2.

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Authors and Affiliations

  1. Department of Computer Science and Information Engineering, National Cheng Kung University, No.1, University Road, Tainan City, 701, Taiwan

    Sheng-Min Chiu, Yow-Shin Liou & Chiang Lee

  2. Department of Industrial Engineering and Management, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin, 64002, Taiwan

    Yi-Chung Chen

  3. National Science and Technology Center for Disaster Reduction, 9F., No.200, Sec. 3, Beisin Rd., Xindian District, New Taipei City, 23143, Taiwan

    Rong-Kang Shang & Tzu-Yin Chang

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  1. Sheng-Min Chiu
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Correspondence to Yi-Chung Chen.

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Yow-Shin Liou, Yi-Chun Chen, Chiang Lee, Rong-Kang Shang and Tzu-Yin Chang contributed equally to this work.

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Chiu, SM., Liou, YS., Chen, YC. et al. Identifying key grid cells for crowd flow predictions based on CNN-based models with the Grad-CAM kit. Appl Intell 53, 13323–13351 (2023). https://doi.org/10.1007/s10489-022-03988-1

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