A hybrid approach of traffic simulation and machine learning techniques for enhancing real-time traffic prediction-Reference-Cited by-同舟云学术

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A hybrid approach of traffic simulation and machine learning techniques for enhancing real-time traffic prediction

Published:2024-03 Issue: Volume:160 Page:104490
ISSN:0968-090X
Container-title:Transportation Research Part C: Emerging Technologies
language:en
Short-container-title:Transportation Research Part C: Emerging Technologies

Author:

Kim Yeeun^ORCID,Tak Hye-young^ORCID,Kim Sunghoon^ORCID,Yeo Hwasoo^ORCID

Publisher

Elsevier BV

Reference44 articles.

1. Traffic simulation with MITSIMLab;Ben-Akiva,2010

2. Traffic simulation with aimsun;Casas,2010

3. Real-time travel time prediction using particle filtering with a non-explicit state-transition model;Chen;Transp. Res. C,2014

4. Stacked bidirectional and unidirectional LSTM recurrent neural network for forecasting network-wide traffic state with missing values;Cui;Transp. Res. C,2020

5. The cell transmission model: A dynamic representation of highway traffic consistent with the hydrodynamic theory;Daganzo;Transp. Res. B,1994

Cited by 2 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. Koopman theory meets graph convolutional network: Learning the complex dynamics of non-stationary highway traffic flow for spatiotemporal prediction;Chaos, Solitons & Fractals;2024-10

2. Intelligent trapezoid and variable weight combination-based reconstructed GM model;Heliyon;2024-08

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