A graph-based approach for traffic prediction using similarity and causal relations between nodes-Reference-Cited by-同舟云学术

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A graph-based approach for traffic prediction using similarity and causal relations between nodes

Published:2024-07 Issue: Volume:296 Page:111913
ISSN:0950-7051
Container-title:Knowledge-Based Systems
language:en
Short-container-title:Knowledge-Based Systems

Author:

Khaled Alkilane^ORCID,Elsir Alfateh M. Tag^ORCID,Wang Pengfei,Shen Yanming^ORCID,Zhang Qiang

Funder

National Major Science and Technology Projects of China

Publisher

Elsevier BV

Reference51 articles.

1. Graph neural network for traffic forecasting: A survey;Jiang,2021

2. Dl-traff: Survey and benchmark of deep learning models for urban traffic prediction;Jiang,2021

3. A comprehensive study of speed prediction in transportation system: From vehicle to traffic;Zhou;iScience,2022

4. Distribution of residual autocorrelations in autoregressive-integrated moving average time series models;Box;J. Amer. Statist. Assoc.,1970

5. Travel-time prediction with support vector regression;Wu;IEEE Trans. Intell. Transp. Syst.,2004

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. An Intelligent Deep Learning Framework for Traffic Flow Imputation and Short-term Prediction Based on Dynamic Features;Knowledge-Based Systems;2024-09

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