Publication Date: 2024/03/26
Abstract: Traffic congestion is a major problem in urban areas, leading to increased travel time, economic losses, and environmental pollution. By analyzing traffic data from traffic cameras, we can detect and predict traffic congestion with high accuracy. In this survey, we explore the use of deep learning techniques for traffic congestion detection. Deep learning models, such as convolutional neural networks and recurrent neural networks, have shown promising results in traffic congestion detection. We also discuss the challenges and future directions of this field, including the need for high-quality data and the development of real-time traffic management systems.
Keywords: Traffic Congestion, Deep Learning, Machine Learning, Artificial Neural Networks, Computer Vision, Traffic Cameras, Traffic Data, Traffic Management, Real- Time Systems, Convolutional Neural Networks, Recurrent Neural Networks, Data Analysis, Pattern Recognition, And Image Processing.
DOI: https://doi.org/10.5281/zenodo.10877599
PDF: https://ijirst.demo4.arinfotech.co/assets/upload/files/IJISRT23NOV1483.pdf
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