The bright students of Shiv Nadar School have developed an 'intelligent traffic light' system which they say may not only help reduce commute time but also lower air pollution. Their innovation, as they claim, makes the existing traffic light system responsive to real-time traffic intensity. For a record, it's the first system which is tailor-made for Indian roads. As per the project paper drafted by the team of four, it will function accurately keeping in mind the traffic situation in the country.
The system was created by four standard 11 students namely:
"Based on our research, we found that most traffic signals work on preset algorithms and timers that are probably customised for each junction but stay static once configured," Shelat, one of the students said.
"On close observation, we realised that the lopsided nature of the traffic leads to a large portion of the time of the commuters being wasted at each junction. Using a simple camera and a microcontroller, we now have a way to calculate the amount of traffic present on the road using a camera and then modify the red/green distribution time at each junction dynamically," he added.
Students project report reads:
If the recent reports released by The Boston Consulting Group is to go by, commuters in metro cities namely Delhi, Mumbai, Bengaluru, and Kolkata spend 1.5 hours more than their counterparts in other Asian cities during peak traffic times. Not only this, Vehicles are a major contributor to air pollution but after the implication of the project, commuters will spend less time on the roads as compared to earlier due to primitive traffic management system.
"The best part about the innovation is that it is compatible with the existing traffic lights, thus cutting down the costs and time required for implementation. The system will cost only Rs 20,000 as compared to Rs eight lakh spent on other systems", the students said.
A prototype has been developed and tested in live traffic conditions. It has also been approved for a longer trial by the local authorities. This system uses a camera to sense the changing traffic patterns around by capturing an image. The captured image is then sent to a microprocessor, which uses image processing and edge detection algorithms to detect edged black and white pixels. By various real-time observations and analysis under different traffic density scenarios, the students determined the amount of maximum and minimum time required for different traffic densities to clear.
(With inputs from PTI)