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Title	A Spatial-Temporal Graph Convolutional Recurrent Network For Transportation Flow Estimation
ID_Doc	4873
Authors	Drosouli I.; Voulodimos A.; Mastorocostas P.; Miaoulis G.; Ghazanfarpour D.
Year	2023
Published	Sensors, 23, 17
DOI	http://dx.doi.org/10.3390/s23177534
Abstract	Accurate estimation of transportation flow is a challenging task in Intelligent Transportation Systems (ITS). Transporting 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 (ST-GCRN) that learns from both the spatial stations network data and time series of historical mobility changes in order to estimate transportation 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 accuracy of transportation flow estimation. Extensive experiments on two real-world datasets of transportation flow, New York bike-sharing system and Hangzhou metro system, prove the effectiveness of the proposed model. Compared to the current state-of-the-art baselines, it decreases the estimation error by 98% in the metro system and 63% in the bike-sharing system. © 2023 by the authors.
Author Keywords	bike-sharing system dataset; deep learning; graph convolutional networks; LSTΜ; metro dataset; spatial-temporal dependencies; transportation flow estimation

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