Traffic congestion in urban environments presents a persistent challenge, resulting in increased travel time, fuel consumption, and environmental degradation. Conventional traffic signal systems rely on fixed-time operations, which are often inadequate in responding to fluctuating traffic densities and emergencies. This study proposes an Intelligent Traffic Signal Optimization System leveraging YOLOv8, an advanced deep learning-based object detection algorithm, to enhance traffic management and emergency response. The system captures real-time traffic video footage and processes individual frames using OpenCV to improve the detection of emergency vehicles such as ambulances, fire engines, and police cars. YOLOv8 is employed to accurately identify these vehicles, enabling the dynamic adjustment of traffic signal durations based on real-time data. In emergency scenarios, the signal duration is extended automatically, ensuring the priority passage of essential services. This adaptive approach not only improves traffic flow efficiency but also reduces overall congestion. Integrating computer vision and artificial intelligence in this context highlights its potential contribution to sustainable urban planning.
Introduction
Urban traffic congestion causes delays, fuel waste, and pollution, partly due to fixed-time traffic signals that don’t adapt to real-time conditions or emergencies. This study presents an Intelligent Traffic Signal Optimization System using computer vision and deep learning (YOLOv8 and OpenCV) to detect vehicles, especially emergency responders (ambulances, fire trucks, police), from live video feeds. When an emergency vehicle is detected, the system dynamically extends green light durations to prioritize its passage, improving response times and overall traffic flow.
The system processes real-time video frames, detects vehicles with YOLOv8, classifies weather conditions from temperature data, and adjusts traffic signals accordingly without relying on physical sensors. It continuously optimizes signal timings based on traffic density when no emergencies are present. The model demonstrated high accuracy after training, particularly for ambulances.
Key benefits include real-time emergency prioritization, dynamic and adaptive signal control, cost efficiency by using existing CCTV infrastructure, and improved public safety. Future enhancements could integrate GPS for precise vehicle tracking, multi-camera coordination for wider coverage, and mobile app integration for real-time alerts.
Conclusion
This project presents a real-time system that utilizes the YOLOv8 model to detect vehicles from CCTV footage. It also simulates different weather conditions, such as fog, rain, and clear weather, by generating random temperature values to replicate these environments. The system enhances traffic management by adjusting traffic signal timings to prioritize emergency vehicles, ensuring quicker response times. This functionality improves road safety, raises awareness, and promotes smoother traffic flow. YOLOv8 provides fast and accurate vehicle detection, making the system particularly effective for highway monitoring. With further advancements, this system holds the potential to significantly lower response times, save lives, and improve road safety overall.
References
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