This project implements an IoT-based Traffic Management System that utilizes ESP32-CAM modules and ultrasonic sensors to dynamically adjust traffic light timings. The system optimizes traffic flow, reduces congestion, and improves safety by making real-time decisions based on the data gathered.
- ESP32-CAM: Captures real-time traffic images and transmits them to a server over WiFi for further processing.
- Ultrasonic Sensors: Measures the distance between vehicles to assess traffic density and flow.
- ESP32-CAM-MB Programmer: Programs the ESP32-CAM modules for efficient data collection.
- Ultrasonic Sensors continuously measure the distance between vehicles, generating real-time data.
- ESP32-CAM Modules capture images of traffic at regular intervals.
- The data is sent to the server every 30 seconds via WiFi for further processing.
- The Server processes vehicle distance data to determine traffic density.
- OpenCV is used to analyze images captured by the ESP32-CAM to count the number of vehicles.
- Sensor data and image processing results are combined to assess traffic congestion.
- Traffic light timings are dynamically adjusted based on vehicle count and density:
- Red: High density or many vehicles.
- Orange: Moderate density or vehicle count.
- Green: Low density and fewer vehicles.
- Displays real-time traffic data, including live images from the ESP32-CAM.
- Provides an interface to review historical traffic data for analysis and future improvements.
- Optimized Traffic Flow: Real-time adjustments to traffic lights reduce congestion and ensure smooth traffic movement.
- Reduced Emissions 🌱: By minimizing idle times, the system lowers fuel consumption and reduces pollution.
- Enhanced Safety 🛡️: The dynamic control system reduces the likelihood of accidents, improving overall road safety.
- Better User Experience 👥: Provides real-time traffic information, enabling informed decision-making for commuters.
To run the system, the following packages are required for the OpenCV algorithm:
- IoT: Real-time communication between devices using WiFi (ESP32-CAM).
- Computer Vision: Image processing and object detection using OpenCV and YOLOv3.
- Embedded Systems: Integration of hardware (ESP32-CAM, ultrasonic sensors) for real-time data capture.
- Web Development: Real-time dashboard displaying traffic data.