ABSTRACT
Rapid transit and transportation networks are essential to any country's economic progress. Long wait times, lost fuel, and financial losses are the outcomes of poor management and traffic congestion. For national development, a quick, affordable, and effective traffic control system is essential. Monitoring and managing urban traffic is becoming a significant issue due to the increasing number of vehicles on the road. The Traffic Monitoring Authority needs to develop innovative solutions to address this issue. One approach is to utilize automation and intelligent control techniques to improve traffic flow and safety. The project incorporates sound sensors that detect approaching emergency vehicles, allowing for prioritized and streamlined traffic management at signalized intersections. Additionally, the system features a dynamically triggered barrier that enhances pedestrian safety by ensuring pedestrians are securely clear of traffic before signals change.
OBJECTIVE
1. To reduce traffic congestion by optimizing signal timing based on real-time road conditions and vehicle density.
2. To prioritize emergency vehicles by detecting their presence using sound sensors and dynamically adjusting traffic signals to provide a clear path.
3. To enhance pedestrian safety through the use of automated barriers that activate during signal transitions, ensuring safe crossing.
4. To minimize manual traffic control dependency by incorporating automation and intelligent decision-making into the traffic management system.
5. To enable quicker response times at intersections during peak hours and emergencies, improving overall traffic flow.
6. To design a scalable and adaptable system that can be integrated with future smart city technologies and expanded to other urban areas facing similar challenges.
• Demo Video
• Complete project
• Full project report
• Source code
• Complete project support by online
• Life time access
• Execution Guidelines
• Immediate (Download)
Software Requirements:
1. Python 3.7 and Above
2. NumPy
3. OpenCV
4. Scikit-learn
5. TensorFlow
6. Keras
Hardware Requirements:
1. PC or Laptop
2. 500GB HDD with 1 GB above RAM
3. Keyboard and mouse
4. Basic Graphis card
1. Immediate Download Online
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