Abstract: Persistent congestions of varying intensities and durations within dense transportation networks provide the biggest obstacle to sustainable mobility. This type of congestion is beyond the scope of traditional Adaptive Traffic Signal Control. In order to enhance decision-making regarding traffic length estimates, deep learning-based algorithms have demonstrated their importance in predicting adjective outcomes. This work shows that depending on the length of the vehicle, DL models can effectively alleviate traffic congestion by only permitting traffic to pass through a signal.

Keywords: Traffic, Image Processing, YOLO, Deep Learning.

Cite:
B. Haritha, Ch. Venkata Yamuna, M. Alfa Chandrika, A. Chandi Priya,"Intelligent Traffic System for Urban Conditions Using Real-time Vehicle Tracking", IARJSET International Advanced Research Journal in Science, Engineering and Technology, vol. 11, no. 3, 2024, Crossref https://doi.org/10.17148/IARJSET.2024.11334.

| DOI: 10.17148/IARJSET.2024.11334