A Review of Traffic Congestion Prediction Using Artificial Intelligence
In recent years, traffic congestion prediction has led to a growing research area, especially of machine learning of artificial intelligence (AI). With the introduction of big data by stationary sensors or probe vehicle data and the development of new AI models in the last few decades, this research...
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| Published in | Journal of advanced transportation Vol. 2021; pp. 1 - 18 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
London
Hindawi
2021
John Wiley & Sons, Inc Wiley |
| Subjects | |
| Online Access | Get full text |
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| Abstract | In recent years, traffic congestion prediction has led to a growing research area, especially of machine learning of artificial intelligence (AI). With the introduction of big data by stationary sensors or probe vehicle data and the development of new AI models in the last few decades, this research area has expanded extensively. Traffic congestion prediction, especially short-term traffic congestion prediction is made by evaluating different traffic parameters. Most of the researches focus on historical data in forecasting traffic congestion. However, a few articles made real-time traffic congestion prediction. This paper systematically summarises the existing research conducted by applying the various methodologies of AI, notably different machine learning models. The paper accumulates the models under respective branches of AI, and the strength and weaknesses of the models are summarised. |
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| AbstractList | In recent years, traffic congestion prediction has led to a growing research area, especially of machine learning of artificial intelligence (AI). With the introduction of big data by stationary sensors or probe vehicle data and the development of new AI models in the last few decades, this research area has expanded extensively. Traffic congestion prediction, especially short-term traffic congestion prediction is made by evaluating different traffic parameters. Most of the researches focus on historical data in forecasting traffic congestion. However, a few articles made real-time traffic congestion prediction. This paper systematically summarises the existing research conducted by applying the various methodologies of AI, notably different machine learning models. The paper accumulates the models under respective branches of AI, and the strength and weaknesses of the models are summarised. |
| Audience | Academic |
| Author | Akhtar, Mahmuda Moridpour, Sara |
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| ContentType | Journal Article |
| Copyright | Copyright © 2021 Mahmuda Akhtar and Sara Moridpour. COPYRIGHT 2021 John Wiley & Sons, Inc. Copyright © 2021 Mahmuda Akhtar and Sara Moridpour. 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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| Snippet | In recent years, traffic congestion prediction has led to a growing research area, especially of machine learning of artificial intelligence (AI). With the... |
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| SubjectTerms | Algorithms Analysis Artificial intelligence Big data Cable television broadcasting industry Clustering Computational linguistics Data collection Data mining Datasets Deep learning Energy consumption Fuzzy sets Language processing Learning algorithms Machine learning Natural language interfaces Predictions Principal components analysis Traffic Traffic congestion Traffic control Traffic flow Transportation |
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| Title | A Review of Traffic Congestion Prediction Using Artificial Intelligence |
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