A novel residual graph convolution deep learning model for short-term network-based traffic forecasting-Reference-Cited by-同舟云学术

A novel residual graph convolution deep learning model for short-term network-based traffic forecasting

Published:2019-12-02 Issue:5 Volume:34 Page:969-995
ISSN:1365-8816
Container-title:International Journal of Geographical Information Science
language:en
Short-container-title:International Journal of Geographical Information Science

Author:

Zhang Yang¹²^ORCID,Cheng Tao¹^ORCID,Ren Yibin³^ORCID,Xie Kun⁴^ORCID

Affiliation:

1. SpaceTimeLab for Big Data Analytics, Department of Civil, Environmental and Geomatic Engineering, University College London, London, UK

2. College of Systems Engineering, National University of Defense Technology, Changsha, China

3. CAS Key Laboratory of Ocean Circulation and Waves, Institute of Oceanology, Chinese Academy of Sciences and Centre for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao, China

4. Department of Civil & Environmental Engineering, Old Dominion University, Norfolk, VA, USA

Funder

China Scholarship Council

UK Economic and Social Research Council

Publisher

Informa UK Limited

Subject

Library and Information Sciences,Geography, Planning and Development,Information Systems

Link

https://www.tandfonline.com/doi/pdf/10.1080/13658816.2019.1697879

Reference53 articles.

1. Distribution of Residual Autocorrelations in Autoregressive-Integrated Moving Average Time Series Models

2. A spatiotemporal correlative k-nearest neighbor model for short-term traffic multistep forecasting

3. Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions

4. Fine-grained prediction of urban population using mobile phone location data

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