Smart Traffic Management System

Published on 19 Apr 2022. Written by Jagmohan

Waiting in a traffic jam has always been a bad experience for everyone. In this project, you will develop a smart traffic management system. The traffic will be controlled based on the number of vehicles waiting. The lane with a greater number of vehicles will be allowed to move first.

Read more..

You will use Raspberry Pi as the brain of the system. LEDs to represent traffic lights. You will need a surveillance camera to record video. Using video processing algorithms, you will find the total number of cars waiting.

For performing video processing, you will use the OpenCV library.

Project Description:

Raspberry Pi 3: It is a third-generation, single-board computer. It contains a 64-bit quad-core processor. The clock speed is 1.5 GHz. An external micro SD card provides memory to Raspberry Pi. You can give instructions using the Raspbian OS.

Raspbian OS: It is similar to Windows or Ubuntu OS. You can code the Raspberry Pi using Python in the Raspbian OS. It is user-friendly and allows multi-tasking.

LED: Light Emitting Diode emits light when connected in forward bias. You will use LEDs to represent traffic lights.

Surveillance camera: It is a camera used for continuous surveillance in buildings. You can find surveillance cameras in almost all the major buildings. You will use it to capture the video of the incoming vehicles.

OpenCV: It is a library that allows image and video processing. You will use it to capture the front side image of the vehicle. Then that image can be altered as you desire.

Project implementation:

1. Place four cameras at the four sides of a junction.
2. Interface different coloured LEDs and the cameras with the Raspberry Pi.
3. Open Raspbian OS on your PC/laptop.
4. Begin with coding for the system. First, the cameras have to detect the number of cars in each lane.
5. For this, use the OpenCV library. Begin with converting the image into the required format.
6. Next, you will find the ROI (Region of Interest).
7. Change the perspective of the video as per need.
8. Detect the edges in the video by using various algorithms.
9. Draw contours to calculate the features of the video.
10. Finally, draw a red rectangle around all the vehicles. Calculate the number of red rectangles.
11. Now based on the number of vehicles, give proper traffic signals.
12. Optimize the system for better results.

Programming Language: Python

Kit required to develop Smart Traffic Management System:

• Raspberry Pi
• Surveillance Camera
• LED
• Python

Technologies you will learn by working on Smart Traffic Management System:

• Smart Systems

Any Questions?