Crowd Flow Prediction Model based-on Adaptive Flow Attention for Smart Library
Authors: 
Yi Yang, Jiaxing Chen, Shuya ZhangAuthors Info & Claims
Yi Yang, Jiaxing Chen, Shuya ZhangAuthors Info & ClaimsPages 973 - 978
Abstract
With the popularity of digitisation in major libraries, managers pay more and more attention to the task of people flow prediction in smart libraries. In this paper, Crowd Flow Prediction Model based-on Adaptive Flow Attention is designed to predict the foot traffic in smart libraries. It extracts, filters, learns and retains important feature information and semantic information using the designed Adaptive Flow Attention and Residual Gated Convolutional. And it assigns weights to each channel of the extracted spatial feature maps, which helps the model to better learn the potential temporal dependencies in the people flow data. For the influence of external factors on people flow prediction, the model adopts a fully connected network to process the features of weather, holidays and other external factors to assist prediction, and finally generates people flow prediction results. Through this model, library managers can make scientific decisions according to different situations and improve the efficiency of resource utilisation.
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Index Terms
- Crowd Flow Prediction Model based-on Adaptive Flow Attention for Smart Library
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Published In
October 2023
1065 pages
ISBN:9798400709449
DOI:10.1145/3650215
Copyright © 2023 ACM.
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Association for Computing Machinery
New York, NY, United States
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Published: 16 April 2024
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ICMLCA 2023
ICMLCA 2023: 2023 4th International Conference on Machine Learning and Computer Application
October 27 - 29, 2023
Hangzhou, China
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