छात्रों ने विकसित किया AI और मशीन लर्निंग आधारित ट्रैफिक सिस्टम, जानिए कैसे करेगा काम

ETVBHARAT

गाजियाबाद के निजी इंजीनियरिंग कॉलेज के बीटेक के छात्रों द्वारा तैयार किया गया "Intelliflow" आर्टिफिशियल इंटेलिजेंस और मशीन लर्निंग पर आधारित है.

Transcript

00:00We have 8 vehicles in North Lane, East Lane and 7 vehicles in North Lane.

00:04As we have seen in the video, we have 9 vehicles in North Lane and 7 vehicles in North Lane.

00:08We have 9 vehicles in North Lane.

00:12We have 8 vehicles in North Lane.

00:16We can also see how much efficiency is in this video.

00:2080% of our efficiency is increased.

00:22Q-reduction is reduced by 16%

00:26We have a project in our system.

00:30Our group has prepared an intelligent traffic light system.

00:34The traffic light is minimum of the intersection.

00:38We have to wait for everyone.

00:42Q-A-A-I model is implemented in this model?

00:44We have implemented an AI model.

00:46The number of vehicles is detected on each road.

00:50It is assigned green light.

00:52WhenBOX no other vehicles are right,

00:54So we can't ampere waste many more…

00:56Without them, there is no longer time.

00:58Diaries and pollution only,

00:59This has increased with any problems.

01:01You will know that also goes into a581 vehicle…

01:03There will increase a satisfaction factor in the public.

01:05So we find that people watch more,

01:07The grid is publicly enfinned.

01:09As if we followed less too,

01:11The grid will keep us on our network,

01:13The crews can make us operated on every helicopter.

01:15But we do not recall.

01:16We need to monitor how much power?

01:17Could we engage with the car known as the prótom IoT?

01:20and then they will count that they will take the vehicle and take the vehicle.

01:28Yes sir, we have a very big problem in India.

01:31The traffic lights have a lot of congestion,

01:34and the emergency vehicle, like the ambulance,

01:36it goes back and goes back,

01:37so there is a lot of unfortunate things here.

01:40So, we have implemented an ambulance driver,

01:43which we will provide an app,

01:45so we can send our back-end system to the back-end system

01:49so that we will be able to reach this road.

01:51So, our AI will automatically change our priorities

01:55and when the ambulance comes to that area,

01:58we will give green light to the road

02:01so that the car will be passed

02:03and the ambulance will be easily passed

02:05and the deaths of the ambulance will be minimized.

02:09Our idea is that we thought that we will solve real-life problems

02:13so the main thing is in Delhi,

02:15our college, in Gaziabad,

02:16there is a lot of traffic.

02:18So, we thought that we can reduce the waiting time,

02:21and the main thing is that there is an ambulance.

02:24So, the death of the ambulance,

02:25how can we reduce the death of the ambulance,

02:26and how can we reduce the priority of the ambulance?

02:28I am now from the ELC department.

02:30My work is that,

02:32we have used this IoT model,

02:33which is the hardware presentation,

02:35which is my computer.

02:36We have used Arduino Mega,

02:38which we have shared live data.

02:40But, when we implement it in real-time,

02:42we will use ESP32,

02:45which we can send all the data through Bluetooth,

02:47through wirelessly.

02:49This is our team member, Aakash.

02:51Aakash Kumar Gupta,

02:52we are from the AI department.

02:53We have used ML model,

02:55which is detecting live cameras,

02:57how much number of cars,

02:58and how much is it,

02:59and how much is it,

03:00and how much is it,

03:01and how much is it,

03:02and how much is it,

03:03and how much is it,

03:04and how much is it.

03:05Yes, my name is Aakash Kumar Gupta,

03:07and I and Aakash,

03:08both of them,

03:09have integrated AI and ML

03:10to this system,

03:11and the model,

03:12like Yolo V8 model,

03:14and CNN model,

03:15and how we can implement it,

03:17and the back-end,

03:18all of us,

03:19as a team,

03:20have coordinated it.

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