Deep learning algorithms for traffic forecasting: a comprehensive review and comparison with classical ones
Open accessAbstract
Accurate and timely forecasting of critical components is pivotal in intelligent transportation systems and traffic management, crucially mitigating congestion and enhancing safety. This paper aims to comprehensively review deep learning algorithms and classical models employed in traffic forecasting. Spanning diverse traffic datasets, the study encompasses various scenarios, offering a nuanced understanding of traffic forecasting methods. Reviewing 111 seminal research works since the 1980s, encompassing both deep learning and classical models, the paper begins by detailing the data sources utilized in transportation systems. Subsequently, it delves into the theoretical underpinnings of prevalent deep learning algorithms and classical models prevalent in traffic forecasting. Furthermore, it investigates the application of these algorithms and models in forecasting key traffic characteristics, informed by their utility in transport and traffic analyses. Finally, the study elucidates the merits and drawbacks of proposed models through applied research in traffic forecasting. Findings indicate that while deep learning algorithms and classic models serve as valuable tools, their suitability varies across contexts, necessitating careful consideration in future studies. The study underscores research opportunities in road traffic forecasting, providing a comprehensive guide for future endeavors in this domain.
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ML models leveraging LSTM networks can predict short-term traffic volumes, travel times, and congestion hotspots more accurately than traditional time-series methods.
“ML models, leveraging techniques such as LSTM networks for spatial-temporal speed prediction (Abduljabbar et al., 2021), can predict short-term traffic volumes, travel times, and congestion hotspots more accurately than traditional time-series methods, as reviewed by Afandizadeh et al. (2024).”
From Artificial intelligence in civil engineering: emerging applications and opportunities (2025) · cites this paper as Afandizadeh et al., 2024
Section 4.1.1 reports LSTM-based predictions of traffic flow and travel time, and the Conclusion says machine-learning models have outperformed classical models in many studies. Section 4.4 discusses deep-learning predictions of congestion states, but the passages do not establish that LSTM predicts congestion hotspots more accurately than traditional time-series methods. No page numbers are available in our copy. (LLM, conf=0.85)
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Manivannan, S., Prasad, N., Patil, N., Nikhil, V. G. K., Shinde, Y., & Gupta, S. K. (2026) · International Conference on Natural Language Processing and Computer Vision (ICNPCV) · doi:10.1109/icnpcv69434.2026.11708925
Edge-Intelligent Traffic Prediction and Optimization for Smart Cities
Nasar, M., & Kausar, M. A. (2026) · International Conference on Intelligent Systems and Artificial Intelligence Applications (ISAA) · doi:10.1109/isaa67392.2026.11726639
A Deployable Dual-Mode Vision Framework for Adaptive Intersection Control and Traffic Analytics
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- Authors
- Afandizadeh, S., Abdolahi, S., & Mirzahossein, H.
- Affiliations
- Iran University of Science and Technology (IR)
- Imam Khomeini International University (IR)
- Venue
- Journal of Advanced Transportation
- Publisher
- Wiley
- Published
- 2024-01
- Volume
- 2024
- Issue
- 1
- ISSN
- 0197-6729 (ISSN-L)
- 2042-3195
- OpenAlex
- W4402438362
- Cited by
- 64
- References
- 136
- Citations by year
- 2026: 39 · 2025: 25
- FWCI
- 11.85
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Journal of Advanced Transportation (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Engineering › Building and Construction › Traffic Prediction and Management Techniques
- Physical Sciences › Engineering › Control and Systems Engineering › Traffic control and management
- Social Sciences › Decision Sciences › Management Science and Operations Research › Forecasting Techniques and Applications
- Keywords
- traffic prediction, deep learning, intelligent transportation systems, traffic management, traffic congestion, road safety, model comparison
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