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A Comprehensive Survey of Deep Learning-Based Traffic Flow Prediction Models for Intelligent Transportation Systems
Review Article  ·  Published: 08 July 2025
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ICCK Transactions on Advanced Computing and Systems
Volume 1, Issue 3, 2025: 117-137
Review Article Open Access

A Comprehensive Survey of Deep Learning-Based Traffic Flow Prediction Models for Intelligent Transportation Systems

by
Riaz Ali Riaz Ali 1 ORCID
Ahmad Ali Ahmad Ali 2,3 * ORCID
Hafiz Muhammad Yasir Naeem Hafiz Muhammad Yasir Naeem 2,3
Mujtaba Asad Mujtaba Asad 4
Tamam Alsarhan Tamam Alsarhan 5
Md Belal Bin Heyat Md 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 School of Engineering, Westlake University, Hangzhou 310030, China
* Corresponding Author: Ahmad Ali, [email protected]
Volume 1, Issue 3
Volume 1, Issue 3 2025
Received: 03 April 2025 Accepted: 04 June 2025 Published: 08 July 2025 CrossMark
Download PDF 2.09 MB Cite Metrics
DOI: 10.62762/TACS.2025.795448
ARK: ark:/57805/tacs.2025.795448

Article Information

Published in ICCK Transactions on Advanced Computing and Systems
Volume/Issue Volume 1, Issue 3, 2025
Pages 117-137
Cited by 17 (Crossref) 20 (Scopus)

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 key representative works 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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APA Style
Ali, R., Ali, A., Naeem, H. M. Y., Asad, M., Alsarhan, T., & Heyat, M. 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.2025.795448
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TY  - JOUR
AU  - Ali, Riaz
AU  - Ali, Ahmad
AU  - Naeem, Hafiz Muhammad Yasir
AU  - Asad, Mujtaba
AU  - Alsarhan, Tamam
AU  - Heyat, Md Belal Bin
PY  - 2025
DA  - 2025/07/08
TI  - A Comprehensive Survey of Deep Learning-Based Traffic Flow Prediction Models for Intelligent Transportation Systems
JO  - ICCK Transactions on Advanced Computing and Systems
T2  - ICCK Transactions on Advanced Computing and Systems
JF  - ICCK Transactions on Advanced Computing and Systems
VL  - 1
IS  - 3
SP  - 117
EP  - 137
DO  - 10.62762/TACS.2025.795448
UR  - https://www.icck.org/article/abs/TACS.2025.795448
KW  - intelligent transportation systems
KW  - traffic prediction
KW  - deep learning
KW  - machine learning
KW  - graph neural network
KW  - neural network
AB  - 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 key representative works 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.
SN  - 3068-7969
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  -
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@article{Ali2025A,
  author = {Riaz Ali and Ahmad Ali and Hafiz Muhammad Yasir Naeem and Mujtaba Asad and Tamam Alsarhan and Md Belal Bin Heyat},
  title = {A Comprehensive Survey of Deep Learning-Based Traffic Flow Prediction Models for Intelligent Transportation Systems},
  journal = {ICCK Transactions on Advanced Computing and Systems},
  year = {2025},
  volume = {1},
  number = {3},
  pages = {117-137},
  doi = {10.62762/TACS.2025.795448},
  url = {https://www.icck.org/article/abs/TACS.2025.795448},
  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 key representative works 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.},
  keywords = {intelligent transportation systems, traffic prediction, deep learning, machine learning, graph neural network, neural network},
  issn = {3068-7969},
  publisher = {Institute of Central Computation and Knowledge}
}

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