Deep Learning Algorithms for Traffic Forecasting: A Comprehensive Review and Comparison with Classical Ones

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 forecastin...

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Vydané v:Journal of advanced transportation Ročník 2024; číslo 1
Hlavní autori: Afandizadeh, Shahriar, Abdolahi, Saeid, Mirzahossein, Hamid
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London John Wiley & Sons, Inc 11.09.2024
Wiley
Predmet:
Algorithms
Analysis
Artificial intelligence
Computational linguistics
Critical components
Data mining
Deep learning
Forecasting
Forecasts and trends
Information sources
Intelligent transportation systems
Language processing
Learning algorithms
Literature reviews
Machine learning
Natural language interfaces
Safety management
Social networks
Traffic analysis
Traffic congestion
Traffic management
Transportation models
Trinidad and Tobago
ISSN:0197-6729, 2042-3195
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Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:0197-6729
2042-3195
DOI:10.1155/2024/9981657