A Review of Traffic Congestion Prediction Using Artificial Intelligence

• Published in: Journal of advanced transportation Vol. 2021; pp. 1 - 18
• Main Authors: Akhtar, Mahmuda, Moridpour, Sara
• Format: Journal Article
• Language: English
• Published: London Hindawi 2021; John Wiley & Sons, Inc; Wiley
• Subjects: 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; United States
• ISSN: 0197-6729; 2042-3195
• DOI: 10.1155/2021/8878011

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.