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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Bibliographic Details
Main Authors: Yong Han, Shukang Wang, Yibin Ren, Cheng Wang, Peng Gao, Ge Chen
Format: Artigo
Language:Inglês
Published: MDPI AG 2019-05-01
Series:ISPRS International Journal of Geo-Information
Subjects:
citywide metro network
graph convolution neural network
spatiotemporal flow-volume prediction
deep learning
Online Access:https://www.mdpi.com/2220-9964/8/6/243
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