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A Comprehensive Survey of Deep Learning-Based Traffic Flow Prediction Models for Intelligent Transportation Systems
Review Article | Published: 24 August 2024
ICCK Transactions on Advanced Computing and Systems Volume 1, Issue 3, 2024: 117-137
Volume 1, Issue 3, ICCK Transactions on Advanced Computing and Systems
Volume 1, Issue 3, 2024
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ICCK Transactions on Advanced Computing and Systems, Volume 1, Issue 3, 2024: 117-137

Open Access | Review Article | 24 August 2024
A Comprehensive Survey of Deep Learning-Based Traffic Flow Prediction Models for Intelligent Transportation Systems
by
Riaz Ali Riaz Ali 1
Ahmad Ali Ahmad Ali 2,3 *
Hafiz Muhammad Yasir Naeem Hafiz Muhammad Yasir Naeem 2,3
Mujtaba Asad Mujtaba Asad 4
Tamam Alsarhan Tamam Alsarhan 5
Belal Bin Heyat Belal Bin Heyat 6
1 Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
2 College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China
3 College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China
4 Institute of Image Processing & Pattern Recognition Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China
5 King Abdullah II School of Information Technology, The University of Jordan, Amman, Jordan
6 Department of Computer Science, Westlake University, Hangzhou, China
* Corresponding Author: Ahmad Ali, [email protected]
DOI: 10.62762/TACS.2024.795448
Received: 03 April 2024, Accepted: 28 July 2024, Published: 24 August 2024  
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Abstract
Traffic flow prediction is a critical component of Intelligent Transportation Systems (ITS) and smart city infrastructures. This survey paper provides a comprehensive analysis of recent advancements in deep learning-based approaches for traffic flow prediction, focusing on spatiotemporal correlations and attention mechanisms. We systematically review five seminal papers that propose innovative neural network architectures including DHSTNet, Att-DHSTNet, and ASTMGCNet for citywide traffic prediction. Our survey examines their methodologies, key contributions, experimental results, and comparative performance. We organize the discussion around three main themes: (1) modeling dynamic spatiotemporal dependencies, (2) attention mechanisms for traffic prediction, and (3) hybrid neural network architectures. The paper includes detailed comparison tables and conceptual figures synthesized from the reviewed works. Our analysis shows that attention-based hybrid models outperform traditional techniques, with ASTMGCNet having the lowest RMSE (4.06) and MAPE (12.56%) on benchmark datasets. We end by outlining current issues and potential research directions in this rapidly changing subject.
Graphical Abstract
A Comprehensive Survey of Deep Learning-Based Traffic Flow Prediction Models for Intelligent Transportation Systems
Keywords
intelligent transportation systems
traffic prediction
deep learning
machine learning
graph neural network
neural network
Data Availability Statement
Not applicable.
Funding
This work was supported without any funding.
Conflicts of Interest
The authors declare no conflicts of interest.
Ethical Approval and Consent to Participate
Not applicable.
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Cite This Article
APA Style
Ali, R., Ali, A., Naeem, H. M. Y., Asad, M., Alsarhan, T., & Heyat, B. B. (2024). A Comprehensive Survey of Deep Learning-Based Traffic Flow Prediction Models for Intelligent Transportation Systems. ICCK Transactions on Advanced Computing and Systems, 1(3), 117–137. https://doi.org/10.62762/TACS.2024.795448
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