Cited by 64Traffic Prediction and Management TechniquesTraffic control and managementForecasting Techniques and Applications

Deep learning algorithms for traffic forecasting: a comprehensive review and comparison with classical ones

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Afandizadeh, Shahriar, Abdolahi, Saeid, Mirzahossein, Hamid·2024·Journal of Advanced Transportation

Abstract

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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Crossref · 10.1155/2024/9981657

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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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Cites

Flagged first, then by citations

Research works printed but not linked: 29

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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
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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