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A Review of Traffic Congestion Prediction Using Artificial Intelligence
Author(s) -
Mahmuda Akhtar,
Sara Moridpour
Publication year - 2021
Publication title -
journal of advanced transportation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.577
H-Index - 46
eISSN - 2042-3195
pISSN - 0197-6729
DOI - 10.1155/2021/8878011
Subject(s) - traffic congestion , computer science , big data , traffic congestion reconstruction with kerner's three phase theory , artificial intelligence , focus (optics) , strengths and weaknesses , machine learning , transport engineering , data mining , engineering , philosophy , physics , optics , epistemology
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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