Authors :
Sternford Mavuchi; Tirivangani Magadza; Racheal Chikoore
Volume/Issue :
Volume 8 - 2023, Issue 11 - November
Google Scholar :
https://tinyurl.com/ye2xptr7
Scribd :
https://tinyurl.com/46r354ve
DOI :
https://doi.org/10.5281/zenodo.10877599
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.
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.
