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Predicting Station-Level Short-Term Passenger Flow in a Citywide Metro Network Using Spatiotemporal Graph Convolutional Neural Networks

Predicting the passenger flow of metro networks is of great importance for traffic management and public safety. However, such predictions are very challenging, as passenger flow is affected by complex spatial dependencies (nearby and distant) and temporal dependencies (recent and periodic). In this...

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Main Authors: Yong Han, Shukang Wang, Yibin Ren, Cheng Wang, Peng Gao, Ge Chen
格式: Artigo
語言:Inglês
出版: MDPI AG 2019-05-01
叢編:ISPRS International Journal of Geo-Information
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在線閱讀:https://www.mdpi.com/2220-9964/8/6/243
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