Di-GraphGAN: An enhanced adversarial learning framework for accurate spatial-temporal traffic forecasting under data missing scenarios-Reference-Cited by-同舟云学术

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Di-GraphGAN: An enhanced adversarial learning framework for accurate spatial-temporal traffic forecasting under data missing scenarios

Published:2024-08 Issue: Volume:677 Page:120911
ISSN:0020-0255
Container-title:Information Sciences
language:en
Short-container-title:Information Sciences

Author:

Li Lincan^ORCID,Bi Jichao^ORCID,Yang Kaixiang^ORCID,Luo Fengji^ORCID

Publisher

Elsevier BV

Reference49 articles.

1. Tcp-bast: a novel approach to traffic congestion prediction with bilateral alternation on spatiality and temporality;Zhang;Inf. Sci.,2022

2. Short-term traffic flow prediction in smart multimedia system for internet of vehicles based on deep belief network;Kong;Future Gener. Comput. Syst.,2019

3. Smarter and more connected: future intelligent transportation system;Sumalee;IATSS Res.,2018

4. Multi-scale spatial-temporal aware transformer for traffic prediction;Tian;Inf. Sci.,2023

5. Deep and embedded learning approach for traffic flow prediction in urban informatics;Zheng;IEEE Trans. Intell. Transp. Syst.,2019

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