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 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.

• Record URL:
  https://doi.org/10.1155/2021/8878011
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/5121625
• Supplemental Notes:
  • © 2021 Mahmuda Akhtar and Sara Moridpour.
• Authors:
  • Akhtar, Mahmuda
  • Moridpour, Sara
• Publication Date: 2021-1

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: Article ID 8878011
• Serial:
  • Journal of Advanced Transportation
  • Volume: 2021
  • Publisher: John Wiley & Sons, Incorporated
  • ISSN: 0197-6729
  • EISSN: 2042-3195
  • Serial URL: http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)2042-3195
• Publication flags:

  Open Access (libre)

Subject/Index Terms

• TRT Terms: Artificial intelligence; Forecasting; Machine learning; Mathematical prediction; Predictive models; Reviews; Traffic congestion
• Subject Areas: Highways; Operations and Traffic Management;

Filing Info

• Accession Number: 01766743
• Record Type: Publication
• Files: TRIS
• Created Date: Mar 12 2021 10:06AM