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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| Published in: | Journal of advanced transportation Vol. 2024; no. 1 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
London
John Wiley & Sons, Inc
11.09.2024
Wiley |
| Subjects: | |
| ISSN: | 0197-6729, 2042-3195 |
| Online Access: | Get full text |
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| 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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| AbstractList | 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. |
| Audience | Academic |
| Author | Abdolahi, Saeid Afandizadeh, Shahriar Mirzahossein, Hamid |
| Author_xml | – sequence: 1 givenname: Shahriar orcidid: 0000-0001-5137-3673 surname: Afandizadeh fullname: Afandizadeh, Shahriar – sequence: 2 givenname: Saeid surname: Abdolahi fullname: Abdolahi, Saeid – sequence: 3 givenname: Hamid orcidid: 0000-0003-1615-9553 surname: Mirzahossein fullname: Mirzahossein, Hamid |
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| ContentType | Journal Article |
| Copyright | COPYRIGHT 2024 John Wiley & Sons, Inc. Copyright © 2024 Shahriar Afandizadeh et al. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
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| SubjectTerms | 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 |
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| Title | Deep Learning Algorithms for Traffic Forecasting: A Comprehensive Review and Comparison with Classical Ones |
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