Footfall prediction using graph neural networks [Çizge sinir ağları ile yaya trafiği tahmini]

Boz, Hasan Alp and Bahrami, Mohsen and Balcısoy, Selim and Pentland, Alex (2023) Footfall prediction using graph neural networks [Çizge sinir ağları ile yaya trafiği tahmini]. In: 31st Signal Processing and Communications Applications Conference (SIU), Istanbul, Turkiye

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Accurately predicting the potential foot traffic for a new business is a crucial task since it directly impacts a business's ability to generate revenue. In this work, a graph neural networkbased approach is introduced in which the foot traffic between businesses and neighborhoods is represented in a bipartite network setting where edges capture the yearly-aggregated foot traffic quartile labels. Resulting bipartite networks are fed to the graph neural network to predict the foot traffic label for a new business for all the available neighborhoods. The graph neural network model outperforms well-established Huff model by 3% higher F1 score. Our results indicate that utilizing graph neural network architectures for foot traffic prediction is promising and requires more attention from the field.
Item Type: Papers in Conference Proceedings
Uncontrolled Keywords: Graph Neural Networks; Human Mobility; omputational Social Science
Divisions: Faculty of Engineering and Natural Sciences
Depositing User: Selim Balcısoy
Date Deposited: 29 Sep 2023 12:09
Last Modified: 07 Feb 2024 11:45

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