Nowadays, the traffic congestion is one of the major problems in our country. The causes of traffic congestion are the rapid growth of the population and increase in the number of vehicles. It not only wastes our valuable time but also causes mental stress along with noise and environmental pollutio
Dynamic Traffic Management System
Nowadays, the traffic congestion is one of the major problems in our country. The causes of traffic congestion are the rapid growth of the population and increase in the number of vehicles. It not only wastes our valuable time but also causes mental stress along with noise and environmental pollution. Currently, the traffic lights switch colors on the basis of timers without taking into consideration the size of traffic on the junction in real time which results in the unnecessary delays for the commuters. This problem can be solved by the dynamic management of the traffic lights where the traffic lights switch colors after sensing the current volume of traffic in real time. We propose a system based on cameras. We count and detect vehicles with help of cameras and Microcontrollers to dynamically manage the traffic. And we also detect emergency vehicles. Machine learning techniques will be deployed to forecast the volume of traffic at certain times of the day and junctions of the city. Wireless communication among the traffic signals on various junctions is also proposed to synchronize their switching for better handling of the traffic. The commuters will also be provided with the real-time information about the current and upcoming status of the approaching traffic lights so that they may take better decision for optimizing their routes.
1: Better traffic management by dynamically switching lights.
2: Better traffic synchronization by communication among traffic lights.
3: Better user road experience by disseminating real time traffic an information via the commuters.
The project will be implemented in two phases.
In the first phase the decision to switch lights dynamically is based on the size of the traffic at each side of the junction. We count and detect vehicles with the help of cameras. Then we also do the communication among the adjacent traffic lights with the help of wireless communication. Because wireless communication is cheaper to install and maintain and also can be accessed from anywhere.
In the second phase we want to analyze traffic data at the signals and bring improvement with the help of machine learning algorithms to change the color of traffic lights. After that we want to provide information of traffic signals to the user so for this, we send data to the cloud and then user can access to the cloud with the help of mobile app.
1: Reduced delays to the commuters.
2: Improved air quality from reducing air pollution generated by slow moving traffic.
3: Updating and informing drivers of next traffic lights.
4: Improving road safety and reducing infrastructure damage
5: Reduce stress for you and for other road users around you.
6: Planning according to the traffic.
7: Environmental noise ends.
The final deliverable would be in the form of a model. We would be making a model of our project and implementing practically. So, for this we need some cars, cameras, controller and wireless communication setup.
| Item Name | Type | No. of Units | Per Unit Cost (in Rs) | Total (in Rs) |
|---|---|---|---|---|
| Raspberry pi | Equipment | 2 | 14500 | 29000 |
| USB cameras | Equipment | 8 | 559 | 4472 |
| LED | Equipment | 24 | 1 | 24 |
| Toy cars | Equipment | 20 | 300 | 6000 |
| Hard board | Equipment | 1 | 1000 | 1000 |
| Bread board | Equipment | 2 | 100 | 200 |
| Wires | Equipment | 50 | 20 | 1000 |
| Total in (Rs) | 41696 |
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