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Title	A Graph Neural Network Based Learning Model For Urban Metro Flow Prediction
ID_Doc	1983
Authors	Drosouli I.; Voulodimos A.; Mastorocostas P.; Miaoulis G.; Ghazanfarpour D.
Year	2023
Published	Proceedings - 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023
DOI	http://dx.doi.org/10.1109/ICMLA58977.2023.00285
Abstract	- Abstract-Transport data with dynamic spatial-temporal dependencies elevates transportation flow forecasting to a significant issue for operational planning, managing passenger flow, and arranging for individual travel in a smart city. The task is challenging due to the composite spatial dependency on transportation networks and the non-linear temporal dynamics with mobility conditions changing over time. To address these challenges, we propose a Spatial- Temporal Graph Convolutional Recurrent Network that learns from both the spatial stations network data and time-series of historical mobility changes so as to predict urban metro flow at a future time. The model is based on Graph Convolutional Networks (GCN) and Long Short-Term Memory (LSTM) in order to further improve the estimation accuracy. Extensive experiments on a real-world dataset of Hangzhou metro system prove the effectiveness of the proposed model. © 2023 IEEE.
Author Keywords	Graph Convolutional Networks; spatial-temporal dependencies; urban metro flow prediction

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