Augmented Multi-Component Recurrent Graph Convolutional Network for Traffic Flow Forecasting

Augmented Multi-Component Recurrent Graph Convolutional Network for Traffic Flow Forecasting

Due to the periodic and dynamic changes of traffic flow and the spatial–temporal coupling interaction of complex road networks, traffic flow forecasting is highly challenging and rarely yields satisfactory prediction results. In this paper, we propose a novel methodology named the Augmented Multi-co...

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Bibliographic Details
Main Authors:	Chi Zhang, Hong-Yu Zhou, Qiang Qiu, Zhichun Jian, Daoye Zhu, Chengqi Cheng, Liesong He, Guoping Liu, Xiang Wen, Runbo Hu
Format:	Article
Language:	English
Published:	MDPI AG 2022-01-01
Series:	ISPRS International Journal of Geo-Information
Subjects:	traffic flow forecasting spatial–temporal prediction graph convolutional networks augmented multi-component
Online Access:	https://www.mdpi.com/2220-9964/11/2/88

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