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Smart city article details

Title	Traffic Density Detection And Signal Optimization Using Yolov5 And Ant Colony Optimization
ID_Doc	58557
Authors	Kumaran K.; Sri K.K.; Sharvani Alies Supriya B.; Hema Swethaa V.T.
Year	2025
Published	2025 International Conference on Data Science, Agents and Artificial Intelligence, ICDSAAI 2025
DOI	http://dx.doi.org/10.1109/ICDSAAI65575.2025.11011701
Abstract	Urban traffic jam leads to delays, wastage of fuel and air pollution, and fixed-time traffic signals do not react to real-time scenarios. The findings of this study recommend detection of cars using YOLOv5, regression methods for finding the optimum duration of green light, and reinforcement learning (DQN) for making adaptive signal control decisions. Traffic flow prediction is improved by LSTM models. Performance is quantified in the form of vehicle clearance rate, waiting time reduction, and traffic flow rate. Experiments show that the adaptive system outperforms standard fixed-timing methods greatly, with YOLOv5 detection being 60% accurate, DQN lowering waiting times by 37.2%, vehicle clearance rates increasing by 42.8% during rush hours, and carbon emissions lowering by 27.4%, hence a scalable model for smart cities. © 2025 IEEE.
Author Keywords	Dynamic Signal Control; Intelligent Transportation Systems; IoT-based Traffic Control; Machine Learning in Traffic; Real-time Traffic Monitoring; Smart Traffic Lights; Smart Traffic Management; Traffic Congestion Reduction; Urban Mobility; Vehicle Detection

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