Koopman theory meets graph convolutional network: Learning the complex dynamics of non-stationary highway traffic flow for spatiotemporal prediction-Reference-Cited by-同舟云学术

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Koopman theory meets graph convolutional network: Learning the complex dynamics of non-stationary highway traffic flow for spatiotemporal prediction

Published:2024-10 Issue: Volume:187 Page:115437
ISSN:0960-0779
Container-title:Chaos, Solitons & Fractals
language:en
Short-container-title:Chaos, Solitons & Fractals

Author:

Wang Ting^ORCID,Ngoduy Dong,Li Ye,Lyu Hao,Zou Guojian,Dantsuji Takao

Funder

Shanghai City Science and Technology Commission

National Natural Science Foundation of China

National Key Research and Development Program of China

China Scholarship Council

National Key Research and Development Program of China Stem Cell and Translational Research

Publisher

Elsevier BV

Reference51 articles.

1. Graph neural network for traffic forecasting: A survey;Jiang;Expert Syst Appl,2022

2. Do traffic flow states follow Markov properties? A high-order spatiotemporal traffic state reconstruction approach for traffic prediction and imputation;Hu;Chaos Solitons Fractals,2024

3. Jamming transition in two-lane lattice model integrating the deception attacks on influx during the lane-changing process under vehicle to everything environment;Peng;Chaos Solitons Fractals,2023

4. Spatio-temporal graph neural networks for predictive learning in urban computing: A survey;Jin;IEEE Trans Knowl Data Eng,2023

5. An advanced deep learning approach to real-time estimation of lane-based queue lengths at a signalized junction;Lee;Transp Res C,2019

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