A spatiotemporal hierarchical attention mechanism-based model for multi-step station-level crowd flow prediction

DOI10.1016/j.ins.2020.07.049zbMath1479.76010OpenAlexW3046213253WikidataQ114386621 ScholiaQ114386621MaRDI QIDQ2054089

Hao Chen, Jiangjiang Wu, Jun Li, Yirong Zhou, Ye Wu, Luo Chen

Publication date: 30 November 2021

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ins.2020.07.049

zbMATH Keywords

spatio-temporal analysismulti-output regressioncrowd-flow forecasthierarchical attention mechanismmulti-step ahead prediction

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Traffic and pedestrian flow models (76A30)

Uses Software

• XGBoost
• Adam
• Tensor2Tensor
• U-Net

Cites Work

• Unnamed Item
• Predicting citywide crowd flows using deep spatio-temporal residual networks
• Inventory rebalancing and vehicle routing in bike sharing systems

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