Smart Traffic Management System Using Real Time Analysis
Smart traffic management system using real time analysis is the design of a density based smart traffic system in which time of traffic signal changes after observing the density of traffic present on the road. The construction of this intelligent traffic control method enables us to overcome traffi
2025-06-28 16:35:54 - Adil Khan
Smart Traffic Management System Using Real Time Analysis
Project Area of Specialization Electrical/Electronic EngineeringProject SummarySmart traffic management system using real time analysis is the design of a density based smart traffic system in which time of traffic signal changes after observing the density of traffic present on the road. The construction of this intelligent traffic control method enables us to overcome traffic congestion in populated areas. By using the technique of image processing, we detects the vehicles on a four way junction road with the help of Open CV and Matlab and by using raspberry pi controller. We develop a system which performs execution based on density of vehicles i.e. counting of vehicles, using raspberry-Pi as a microcontroller. It concludes that video processing is a better technique for calculation of traffic density and controlling the state change of traffic light also use of OpenCV library for video processing is good tool as a software .So by calculating the density of traffic at each junction, the duration of each signal is changed automatically according to density of vehicles present on the roads. This automatic controlled traffic management system is far better than the timer based conventional traffic system because of its real time nature. The application of this project is to overcome the traffic congestion on the roads by allocating more time to the busy roads and safe time.This project also enables us to learn about different new techniques of object detections and specialized algorithms for making this intelligent traffic control system.
Project Objectives• The main objective of this project is to adjust and control the traffic in order to fulfill the needs of vehicle flow and to reduce the waiting time and also designed to improve the standard of driving living at the city.
• Implementing new techniques to build an intelligent control system that controls the traffic more efficiently as compared to previous implementing methods.
• The objective behind this proposal is to limit the stoppage time and also regulate the traffic flow by means of the introduction to the sensors and controllers at four way junction traffic signals.
• The proposal aims at reducing the traffic jams in order to reduce traffic congestion, optimize traffic flow and help pro-actively traffic conditions.
We proposed a system consist of four way junction road. Small digital cameras are attached at each junction to capture the images and real time videos of the vehicle. Raspberry Pi controller is used for object detection by using image processing. With the help of Open CV, we can observed the density of traffic at each road. This intelligent traffic control system changed the values of traffic light after observing the density of traffic available on the road. This is a real time process and this will allow us to reduce the traffic congestion issues by allotting more time to the busy roads. So image processing through open CV involves the following terms
Image Acquisition
At the initial stage, cameras are installed at junctions. For every ten seconds, a new video is captured. Finally, the captured real-time video is converted into frames. Out of which the vehicle less road is taken as the reference frame and other frames are taken as the captured frames.
RGB-gray scale conversion
The captured images and the reference image are then converted to gray scale conversion. Generally, the gray scale image ranges from 0-255 pixels. In order to find the pixel value and to get clear clarity of the image, we have converted the images from RGB-gray scale.
Image Enhancement
The method of adjusting the intensity of the image through certain image enhancement techniques. The proposed system uses a Wiener filter for noise cancellation. Similarly, by varying the pixel range the image enhancement is done for improving the threshold of the image.
Foreground detection.
Foreground detection is one of the main tasks in computer vision and image processing technique. For a good foreground detection system should possess the following features.
- Need to develop a background estimate model.
- Repetitive movements and long-term movement should be there.
In case of conventional traffic control system, fixed timings are allotted at each end of traffic signal irrespective to amount or density of vehicles present on the road. So our aim is to design a density based traffic management system which reduces the impact of traffic congestion and saves time for the peoples. Our designed traffic management system is based on real time data analysis which can intelligently change the timing of the traffic signals after observing and calculating the amount of traffic present on the roads. So our designed system includes the following benefits as
- It can handle and reduce the traffic congestion very efficiently and saves more fuel consumption.
- Prevents different health issues due to traffic congestion and saves time of the peoples.
- It does not required any traffic control incharge to control traffic because of its intelligent control nature.
- As networks become faster, and latency lowers, the ability to make instantaneous, automated decisions for driver and pedestrian safety will increase.
Technically , the approach to this design is realized through the design and implementation of its input subsystem, control unit (control program) and output subsystem The input subsystem is made of sensors, programmed and implemented using some already existing principles to achieve optimum performance. The control unit is realized by a microcontroller-based control program, which interprets the input and qualifies it to produce a desired output.
This system was first developed using sensors, but since sensors have a complicated hardware and implementation , the project was developed using OpenCV and Matlab, which made the project comparatively easy to implement and understand, also there were changes in the hardware such as the microcontroller used was Raspberry Pi. We are implementing this project using the python language. Speaking about the feasibility, since we are using OpenCV as the software, the entire cost of the project is minimized
Final Deliverable of the Project HW/SW integrated systemCore Industry TransportationOther Industries IT Core Technology OthersOther Technologies Internet of Things (IoT)Sustainable Development Goals Industry, Innovation and InfrastructureRequired Resources| Item Name | Type | No. of Units | Per Unit Cost (in Rs) | Total (in Rs) |
|---|---|---|---|---|
| Total in (Rs) | 26900 | |||
| Raspberry Pi Controller 4GB | Equipment | 1 | 14800 | 14800 |
| Camera | Equipment | 1 | 650 | 650 |
| HDMI Cable | Miscellaneous | 1 | 350 | 350 |
| Pi Heat sink | Miscellaneous | 1 | 300 | 300 |
| Fan | Miscellaneous | 1 | 250 | 250 |
| Charger | Miscellaneous | 1 | 500 | 500 |
| SD Card | Equipment | 1 | 850 | 850 |
| Web Camera | Equipment | 4 | 1800 | 7200 |
| Fuel | Miscellaneous | 1 | 2000 | 2000 |