Prediction of Urban Population-Facilities Interactions with Graph Neural Network

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Authors

MISHINA Margarita SOBOLEVSKY Stanislav KOVTUN Elizaveta KHRULKOV Alexander BELY Aliaksandr BUDENNYY Semen MITYAGIN Sergey

Year of publication 2023
Type Article in Proceedings
Conference Computational Science and Its Applications – ICCSA 2023 : Lecture Notes in Computer Science, vol 13956
MU Faculty or unit

Faculty of Science

Citation
Web https://doi.org/10.1007/978-3-031-36805-9_23
Doi http://dx.doi.org/10.1007/978-3-031-36805-9_23
Keywords urban mobility; graph neural network; flows prediction
Description The urban population interacts with service facilities on a daily basis. The information on population-facilities interactions is considered when analyzing the current city organization and revealing gaps in infrastructure at the neighborhood level. However, often this information is limited to several observation areas. The paper presents a new graph-based deep learning approach to reconstruct population-facilities interactions. In the proposed approach, graph attention neural networks learn latent nodes’ representation and discover interpretable dependencies in a graph of interactions based on observed data of one part of the city. A novel normalization technique is used to balance doubly-constrained flows between two locations. The experiments show that the proposed approach outperforms classic models in a bipartite graph of population-facilities interactions.
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