Dynamic Graph Convolutional LSTM application for traffic flow estimation from error-prone measurements: results and transferability analysis - Archive ouverte HAL Access content directly
Conference Papers Year : 2021

Dynamic Graph Convolutional LSTM application for traffic flow estimation from error-prone measurements: results and transferability analysis

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Mathematics [math] Statistics [stat] Machine Learning [stat.ML] Mathematics [math] Statistics [math.ST] Mathematics [math] Probability [math.PR]

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Submitted on : Sunday, February 6, 2022-4:09:11 PM

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hal-03559364 , version 1 (06-02-2022)

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Stéphan Clémençon, Safa Boudabous, Houda Labiod, Julian Garbiso. Dynamic Graph Convolutional LSTM application for traffic flow estimation from error-prone measurements: results and transferability analysis. 2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA), Oct 2021, Porto, Portugal. pp.1-10, ⟨10.1109/DSAA53316.2021.9564245⟩. ⟨hal-03559364⟩

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