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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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2024/9981657 |
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| _version_ | 1832568883714719744 |
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| author | Shahriar Afandizadeh Saeid Abdolahi Hamid Mirzahossein |
| author_facet | Shahriar Afandizadeh Saeid Abdolahi Hamid Mirzahossein |
| author_sort | Shahriar Afandizadeh |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-f19bbc85b1be4565ad35e0c639c648ad |
| institution | Kabale University |
| issn | 2042-3195 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-f19bbc85b1be4565ad35e0c639c648ad2025-02-03T00:20:43ZengWileyJournal of Advanced Transportation2042-31952024-01-01202410.1155/2024/9981657Deep Learning Algorithms for Traffic Forecasting: A Comprehensive Review and Comparison with Classical OnesShahriar Afandizadeh0Saeid Abdolahi1Hamid Mirzahossein2School of Civil EngineeringSchool of Civil EngineeringDepartment of Civil-Transportation PlanningAccurate 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.http://dx.doi.org/10.1155/2024/9981657 |
| spellingShingle | Shahriar Afandizadeh Saeid Abdolahi Hamid Mirzahossein Deep Learning Algorithms for Traffic Forecasting: A Comprehensive Review and Comparison with Classical Ones Journal of Advanced Transportation |
| title | Deep Learning Algorithms for Traffic Forecasting: A Comprehensive Review and Comparison with Classical Ones |
| title_full | Deep Learning Algorithms for Traffic Forecasting: A Comprehensive Review and Comparison with Classical Ones |
| title_fullStr | Deep Learning Algorithms for Traffic Forecasting: A Comprehensive Review and Comparison with Classical Ones |
| title_full_unstemmed | Deep Learning Algorithms for Traffic Forecasting: A Comprehensive Review and Comparison with Classical Ones |
| title_short | Deep Learning Algorithms for Traffic Forecasting: A Comprehensive Review and Comparison with Classical Ones |
| title_sort | deep learning algorithms for traffic forecasting a comprehensive review and comparison with classical ones |
| url | http://dx.doi.org/10.1155/2024/9981657 |
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