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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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2021/8878011 |
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| _version_ | 1832546855361183744 |
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| author | Mahmuda Akhtar Sara Moridpour |
| author_facet | Mahmuda Akhtar Sara Moridpour |
| author_sort | Mahmuda Akhtar |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-161af90fb8414c9ba807001f250851c8 |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-161af90fb8414c9ba807001f250851c82025-02-03T06:46:42ZengWileyJournal of Advanced Transportation0197-67292042-31952021-01-01202110.1155/2021/88780118878011A Review of Traffic Congestion Prediction Using Artificial IntelligenceMahmuda Akhtar0Sara Moridpour1Department of Civil and Infrastructure Engineering, RMIT University, Melbourne, VIC 3000, AustraliaDepartment of Civil and Infrastructure Engineering, RMIT University, Melbourne, VIC 3000, AustraliaIn 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.http://dx.doi.org/10.1155/2021/8878011 |
| spellingShingle | Mahmuda Akhtar Sara Moridpour A Review of Traffic Congestion Prediction Using Artificial Intelligence Journal of Advanced Transportation |
| title | A Review of Traffic Congestion Prediction Using Artificial Intelligence |
| title_full | A Review of Traffic Congestion Prediction Using Artificial Intelligence |
| title_fullStr | A Review of Traffic Congestion Prediction Using Artificial Intelligence |
| title_full_unstemmed | A Review of Traffic Congestion Prediction Using Artificial Intelligence |
| title_short | A Review of Traffic Congestion Prediction Using Artificial Intelligence |
| title_sort | review of traffic congestion prediction using artificial intelligence |
| url | http://dx.doi.org/10.1155/2021/8878011 |
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