Chapter 5: Using machine learning and deep learning for traffic congestion prediction: a review
Traffic congestion has long been a problem for many cities and commuters around the world, which causes long commuting hours, increases traffic crash rates and results in significant economic and productivity losses. Correctly predicting traffic congestion can help alleviate several problems that tr...
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| Ngā kaituhi matua: | , , , , , |
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| Hōputu: | Online |
| Reo: | Ingarihi |
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Edward Elgar Publishing
2026
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| Ngā marau: | |
| Urunga tuihono: | https://directory.doabooks.org/handle/20.500.12854/173417 |
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Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!
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| _version_ | 1869514262583443456 |
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| author | Mihaita, Adriana-Simona Li, Zhulin Singh, Harshpreet Sharma, Nabin Tuo, Mao Ou, Yuming |
| author_browse | Li, Zhulin Mihaita, Adriana-Simona Ou, Yuming Sharma, Nabin Singh, Harshpreet Tuo, Mao |
| author_facet | Mihaita, Adriana-Simona Li, Zhulin Singh, Harshpreet Sharma, Nabin Tuo, Mao Ou, Yuming |
| author_sort | Mihaita, Adriana-Simona |
| collection | Directory of Open Access Books |
| description | Traffic congestion has long been a problem for many cities and commuters around the world, which causes long commuting hours, increases traffic crash rates and results in significant economic and productivity losses. Correctly predicting traffic congestion can help alleviate several problems that traffic congestion causes on a recurrent basis. With the advances in data collection, artificial intelligence (AI) becomes an ideal tool for short-term and long-term congestion forecasting. This chapter reviews the latest developments in machine learning and deep learning methodologies for traffic congestion prediction in a systematic way, covering literature over the last decade. The main findings are structured based on different AI methodologies, datasets and prediction time periods. The chapter also discusses the advantages and drawbacks of current AI methodologies and describes the research gaps that must be overcome to enable real-world implementation of AI methodologies for traffic congestion prediction. |
| format | Online |
| id | doab-20.500.12854ir-173417 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2026 |
| publishDateRange | 2026 |
| publishDateSort | 2026 |
| publisher | Edward Elgar Publishing |
| publisherStr | Edward Elgar Publishing |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1734172026-03-10T13:55:56Z Chapter 5: Using machine learning and deep learning for traffic congestion prediction: a review Mihaita, Adriana-Simona Li, Zhulin Singh, Harshpreet Sharma, Nabin Tuo, Mao Ou, Yuming Artificial Intelligence in Transport; Deep Learning in Transport; Artificial Intelligence Ethics in Transport; Short-term Traffic Forecasting; Congestion Prediction; Data Analytics in Transport; Disruptive Innovations in Transport UYQ TRT KNG GBC Traffic congestion has long been a problem for many cities and commuters around the world, which causes long commuting hours, increases traffic crash rates and results in significant economic and productivity losses. Correctly predicting traffic congestion can help alleviate several problems that traffic congestion causes on a recurrent basis. With the advances in data collection, artificial intelligence (AI) becomes an ideal tool for short-term and long-term congestion forecasting. This chapter reviews the latest developments in machine learning and deep learning methodologies for traffic congestion prediction in a systematic way, covering literature over the last decade. The main findings are structured based on different AI methodologies, datasets and prediction time periods. The chapter also discusses the advantages and drawbacks of current AI methodologies and describes the research gaps that must be overcome to enable real-world implementation of AI methodologies for traffic congestion prediction. Published 2026-03-10T13:55:54Z 2026-03-10T13:55:54Z 2023-10-13 chapter 9781803929545 https://directory.doabooks.org/handle/20.500.12854/173417 eng image/jpeg Attribution-NonCommercial-NoDerivatives 4.0 International https://www.e-elgar.com/shop/gbp/handbook-on-artificial-intelligence-and-transport-9781803929538.html https://www.elgaronline.com/edcollchap-oa/book/9781803929545/book-part-9781803929545-11.xml Edward Elgar Publishing Edward Elgar Publishing 10.4337/9781803929545.00011 10.4337/9781803929545.00011 01ceac28-75b4-492a-8eec-f9b98bc6b28c https://creativecommons.org/licenses/by-nc-nd/4.0/ 9781803929545 Edward Elgar Publishing Cheltenham, UK open access |
| spellingShingle | Artificial Intelligence in Transport; Deep Learning in Transport; Artificial Intelligence Ethics in Transport; Short-term Traffic Forecasting; Congestion Prediction; Data Analytics in Transport; Disruptive Innovations in Transport UYQ TRT KNG GBC Mihaita, Adriana-Simona Li, Zhulin Singh, Harshpreet Sharma, Nabin Tuo, Mao Ou, Yuming Chapter 5: Using machine learning and deep learning for traffic congestion prediction: a review |
| title | Chapter 5: Using machine learning and deep learning for traffic congestion prediction: a review |
| title_full | Chapter 5: Using machine learning and deep learning for traffic congestion prediction: a review |
| title_fullStr | Chapter 5: Using machine learning and deep learning for traffic congestion prediction: a review |
| title_full_unstemmed | Chapter 5: Using machine learning and deep learning for traffic congestion prediction: a review |
| title_short | Chapter 5: Using machine learning and deep learning for traffic congestion prediction: a review |
| title_sort | chapter 5 using machine learning and deep learning for traffic congestion prediction a review |
| topic | Artificial Intelligence in Transport; Deep Learning in Transport; Artificial Intelligence Ethics in Transport; Short-term Traffic Forecasting; Congestion Prediction; Data Analytics in Transport; Disruptive Innovations in Transport UYQ TRT KNG GBC |
| topic_facet | Artificial Intelligence in Transport; Deep Learning in Transport; Artificial Intelligence Ethics in Transport; Short-term Traffic Forecasting; Congestion Prediction; Data Analytics in Transport; Disruptive Innovations in Transport UYQ TRT KNG GBC |
| url | https://directory.doabooks.org/handle/20.500.12854/173417 |
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