Smart Traffic Control System Project with Source Code
Traffic congestion is a pressing issue in urban areas, leading to increased travel time, fuel consumption, and environmental pollution. Traditional traffic management techniques struggle to cope with dynamic road conditions, necessitating the integration of artificial intelligence (AI) and machine learning (ML) for real-time traffic control. This paper proposes an intelligent traffic management system leveraging You Only Look Once (YOLO) and AlexNet for efficient vehicle detection, traffic flow optimization, and emergency vehicle prioritization. The system analyzes real-time traffic data using deep learning techniques to classify vehicles, predict congestion patterns, and optimize traffic signals. YOLO is used for real-time object detection, while AlexNet extracts traffic flow features for decision-making. Experimental results demonstrate improved vehicle detection accuracy and reduced congestion, making the system a viable solution for modern traffic management challenges.
Workflow of Smart Traffic Management System using Machine Learning:-
- Traffic Surveillance Input: Live video feeds are captured from CCTV cameras at intersections.
- YOLO-Based Object Detection: Vehicles and emergency vehicles are detected in real-time.
- AlexNet Feature Extraction: Extracts vehicle properties for congestion analysis.
- Traffic Signal Decision Model: Adjusts green light duration dynamically.
- Emergency Vehicle Prioritization: Clears lanes for ambulances and fire trucks.
- Performance Monitoring: Evaluates system performance based on real-time congestion levels.
AI-Powered Smart Traffic Management System Using YOLO and AlexNet for Real-Time Optimization
Software Requirements :-
- Coding Language : Python
- Implementation: Software Framework.
- Operating system : Windows 10 / 11.
- Graphical User Interface : Tkinter
Hardware Requirement:-
- Input Devices : Keyboard, Mouse.
- System : Pentium i3 Processor.
- Hard Disk : 500 GB.
- RAM : 4 GB.
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