Spatiotemporal correlation modelling for machine learning-based traffic state predictions: state-of-the-art and beyond-Reference-Cited by-同舟云学术

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Spatiotemporal correlation modelling for machine learning-based traffic state predictions: state-of-the-art and beyond

Published:2023-01-30 Issue:4 Volume:43 Page:780-804
ISSN:0144-1647
Container-title:Transport Reviews
language:en
Short-container-title:Transport Reviews

Author:

Cui Haipeng¹²,Meng Qiang²,Teng Teck-Hou³,Yang Xiaobo³

Affiliation:

1. College of Civil and Transportation Engineering, Shenzhen University, Shenzhen, People’s Republic of China

2. Department of Civil and Environmental Engineering, National University of Singapore, Singapore, Singapore

3. Singapore Technologies Engineering, Singapore, Singapore

Funder

Singapore Technologies Engineering

Publisher

Informa UK Limited

Subject

Transportation

Link

https://www.tandfonline.com/doi/pdf/10.1080/01441647.2023.2171151

Reference110 articles.

1. A graph CNN-LSTM neural network for short and long-term traffic forecasting based on trajectory data

2. Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems

3. Machine Learning-based traffic prediction models for Intelligent Transportation Systems

4. Bruna, J., Zaremba, W., Szlam, A. & LeCun, Y. (2013). Spectral networks and locally connected networks on graphs. arXiv preprint arXiv:1312.6203.

5. Traffic transformer: Capturing the continuity and periodicity of time series for traffic forecasting

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3. Modeling urban traffic volume incorporating driver stochasticity and traffic correlation with waveguide-based quantum walk;Physica A: Statistical Mechanics and its Applications;2024-07

4. Do traffic flow states follow Markov properties? A high-order spatiotemporal traffic state reconstruction approach for traffic prediction and imputation;Chaos, Solitons & Fractals;2024-06

5. Travel time prediction for an intelligent transportation system based on a data-driven feature selection method considering temporal correlation;2024-04-17

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