{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T09:09:56Z","timestamp":1785316196276,"version":"3.55.0"},"reference-count":53,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2023,8,30]],"date-time":"2023-08-30T00:00:00Z","timestamp":1693353600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s23177534","type":"journal-article","created":{"date-parts":[[2023,8,30]],"date-time":"2023-08-30T10:30:52Z","timestamp":1693391452000},"page":"7534","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["A Spatial-Temporal Graph Convolutional Recurrent Network for Transportation Flow Estimation"],"prefix":"10.3390","volume":"23","author":[{"given":"Ifigenia","family":"Drosouli","sequence":"first","affiliation":[{"name":"Department of Informatics and Computer Engineering, University of West Attica, 12243 Egaleo, Greece"},{"name":"Department of Informatics, University of Limoges, 87032 Limoges, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0632-9769","authenticated-orcid":false,"given":"Athanasios","family":"Voulodimos","sequence":"additional","affiliation":[{"name":"School of Electrical and Computer Engineering, National Technical University of Athens, 15773 Athens, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2892-8017","authenticated-orcid":false,"given":"Paris","family":"Mastorocostas","sequence":"additional","affiliation":[{"name":"Department of Informatics and Computer Engineering, University of West Attica, 12243 Egaleo, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Georgios","family":"Miaoulis","sequence":"additional","affiliation":[{"name":"Department of Informatics and Computer Engineering, University of West Attica, 12243 Egaleo, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Djamchid","family":"Ghazanfarpour","sequence":"additional","affiliation":[{"name":"Department of Informatics, University of Limoges, 87032 Limoges, France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,30]]},"reference":[{"key":"ref_1","unstructured":"Wu, W., Yang, Z., and Li, K. 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