Dynamic Multi-View Coupled Graph Convolution Network for Urban Travel Demand Forecasting

Dynamic Multi-View Coupled Graph Convolution Network for Urban Travel Demand Forecasting

Accurate urban travel demand forecasting can help organize traffic flow, improve traffic utilization, reduce passenger waiting time, etc. It plays an important role in intelligent transportation systems. Most of the existing research methods construct static graphs from a single perspective or two p...

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Bibliographic Details
Main Authors:	Zhi Liu, Jixin Bian, Deju Zhang, Yang Chen, Guojiang Shen, Xiangjie Kong
Format:	Article
Language:	English
Published:	MDPI AG 2022-08-01
Series:	Electronics
Subjects:	urban travel demand prediction coupled graph convolutional network spatio-temporal data multi-view fusion
Online Access:	https://www.mdpi.com/2079-9292/11/16/2620

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