Prediction of Traffic Congestion with Artificial Intelligence: A Review

Javad Gholizadehledari¹

1) 1. Bachelor s degree in Civil Engineering, Islamic Azad University, Tehran Science and Research Branch, Tehran, Iran,

Publication : 9th.International Conference on Researches in Science & Engineering & 6th.International Congress on Civil, Architecture and Urbanism in Asia(9icrsie.com)
Abstract :
In recent years, there has been a surge in research focused on predicting traffic congestion, particularly leveraging artificial intelligence (AI) and machine learning techniques. This interest has been fueled by the availability of big data from stationary sensors and probe vehicle data, alongside advancements in AI models. The research in this field has grown significantly, with a particular emphasis on short-term traffic congestion prediction through the analysis of various traffic parameters. While many studies have primarily relied on historical data for forecasting congestion patterns, a few have delved into real-time prediction methods. Furthermore, the utilization of artificial intelligence (AI) in traffic congestion prediction has gained traction due to its ability to process vast amounts of data efficiently and identify complex patterns that may contribute to congestion. Machine learning models, such as neural networks, support vector machines, and decision trees, have been extensively employed to analyze historical traffic data and predict future congestion scenarios. This paper provides a comprehensive overview of existing research efforts that employ different AI methodologies, notably various machine learning models. It categorizes these models according to their respective branches within AI and offers insights into their strengths and weaknesses.
Keywords : Traffic congestion prediction Artificial intelligence (AI) Machine learning models Real-time prediction