The real-time road traffic flow optimization system aims to enhance traffic management at intersections by employing deep learning techniques for dynamic signal adjustments. Traditional traffic control methods that rely on vehicle count often fail to account for the varying sizes of vehicles, which can result in inefficient signal timing. In contrast, the proposed system calculates the total area occupied by different vehicle types, including cars, buses, trucks, two-wheelers, and auto-rickshaws, within each lane. By fine-tuning a YOLO model specifically for these categories, it enables accurate detection and classification of vehicles from live traffic video feeds. The comprehensive measure of congestion, based on the total occupied area, ensures that traffic light durations are adjusted according to the actual traffic conditions in each lane, rather than just the number of vehicles. The proposed traffic system introduces a dynamic and intelligent framework for traffic signal management that outperforms traditional methods relying on fixed-timer signals or basic vehicle counting. Unlike conventional systems, the traffic signal control algorithm adapts signal durations in real-time, considering traffic density to reduce congestion and waiting times effectively. The mode-finding algorithm further enhances functionality by detecting ambulances or priority vehicles in specific lanes, ensuring immediate passage through adaptive signal adjustments. Additionally, the duration-finding algorithm calculates the optimal green light duration based on vehicle density and waiting time, enabling balanced traffic flow. Compared to static or less responsive traditional systems, the proposed method significantly improves traffic efficiency, minimizes delays offering a smarter and more responsive solution for urban traffic challenges.
Real-Time Road Traffic Flow Optimization using Deep learning Techniques
09.04.2025
382954 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Real-Time Traffic Flow Optimization
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