Скачать или смотреть AI + IoT based smart Traffic management system SIH 25050

description: Problem Context
Urban traffic congestion is a major issue in India’s fast-growing cities. Current traffic systems depend on fixed-timer signals that can’t adjust to real-time traffic levels. This leads to wasted time, fuel, and productivity. Emergency vehicles like ambulances and fire trucks also face delays due to signal wait times, which can cost lives. Moreover, violations of road rules, such as running red lights, changing lanes improperly, and speeding, raise the risk of accidents.

Proposed Solution
We suggest a Smart Traffic Management System powered by AI and IoT that responds to real-time conditions using sensors, computer vision, and affordable hardware.

Key Features:
Adaptive Traffic Lights (IoT-driven):
Traffic signals use IR and ultrasonic sensors to monitor vehicle density in real-time.
The duration of green lights adjusts based on congestion levels, reducing idle time.
Emergency Vehicle Priority (IoT override):
Ambulances and fire trucks have RFID tags or a manual override button.
When they are detected, signals switch to green immediately for their route, saving important response time.
Violation Detection (AI-driven):
Cameras integrated with OpenCV can spot red-light running, riding without helmets, and other infractions.
Data can be recorded and sent to traffic authorities for fines and awareness campaigns.
Hybrid Demonstration:
A working hardware prototype (Arduino or ESP32, sensors, LEDs, RFID) automates traffic lights.
A computer vision model for violation detection, scalable to smart city systems.

Technical Approach
Hardware Components: Arduino or ESP32, IR and ultrasonic sensors, LEDs, RFID modules, push buttons.
Software Components: Arduino IDE for programming microcontrollers, Python and OpenCV for detecting rule-breaking.
Architecture:
Sensor nodes gather data on traffic density.
The microcontroller adjusts signal timing.
Emergency overrides trigger immediate clearance.
The camera and AI module detect violations.
Prototype Link: Innovastra TrafficSense Demo

Innovation & Uniqueness
Hybrid Model: Combines an IoT hardware demo with an AI vision system, unlike many one-sided projects.
Scalability: Functions as a lab-scale prototype and can expand to smart city deployments.
Cost-effective: Utilizes affordable components like Arduino and IR sensors.
Modular Design: Each feature, such as adaptive signals, emergency overrides, and rule detection, operates independently, ensuring flexibility.
Feasibility & Viability
Prototype-ready: Low-cost hardware is already available.
City-level Scaling: Can integrate with existing CCTV networks and municipal systems.
Energy Efficient: Has minimal additional power consumption and can operate on solar-based controllers.
Maintenance-friendly: Modular units are easy to replace or upgrade.

Impact & Benefits
Reduced Congestion: Smarter signals lower waiting times and vehicle queues.
Life-saving Response: Emergency vehicles can pass without delays.
Enhanced Road Safety: Automated detection of rule violations cuts down the need for human monitoring.
Environmental Benefits: Less idling leads to reduced fuel waste and emissions.
Scalable Nationwide: Can work with India’s Smart City Mission.