Deep Convolutional Neural Network for Indoor Regional Crowd Flow Prediction

Crowd flow prediction plays a vital role in modern city management and public safety prewarning. However, the existing approaches related to this topic mostly focus on single sites or road segments, and indoor regional crowd flow prediction has yet to receive sufficient academic attention. Therefore...

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Bibliographic Details
Published in:Electronics (Basel) 2024-01, Vol.13 (1), p.172
Main Authors: Teng, Qiaoshuang, Sun, Shangyu, Song, Weidong, Bei, Jinzhong, Wang, Chongchang
Format: Article
Language:English
Subjects:
Accuracy
Artificial neural networks
Cities
Crowds
Deep learning
Energy consumption
Location based services
Location-based systems
Machine learning
Neural networks
Prediction models
Prediction theory
Public safety
Regions
Safety management
Semantics
Sensors
Traffic flow
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Summary:Crowd flow prediction plays a vital role in modern city management and public safety prewarning. However, the existing approaches related to this topic mostly focus on single sites or road segments, and indoor regional crowd flow prediction has yet to receive sufficient academic attention. Therefore, this paper proposes a novel prediction model, named the spatial–temporal attention-based crowd flow prediction network (STA-CFPNet), to forecast the indoor regional crowd flow volume. The model has four branches of temporal closeness, periodicity, tendency and external factors. Each branch of this model takes a convolutional neural network (CNN) as its principal component, which computes spatial correlations from near to distant areas by stacking multiple CNN layers. By incorporating the output of the four branches into the model’s fusion layer, it is possible to utilize ensemble learning to mine the temporal dependence implicit within the data. In order to improve both the convergence speed and prediction performance of the model, a building block based on spatial–temporal attention mechanisms was designed. Furthermore, a fully convolutional structure was applied to the external factors branch to provide globally shared external factors contexts for the research area. The empirical study demonstrates that STA-CFPNet outperforms other well-known crowd flow prediction methods in processing the experimental datasets.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics13010172