Intelligent Traffic Management System using Machine Learning | Machine Learning Projects 2023 2024

Summary

This video introduces a smart traffic management system utilizing YOLO V4 and MobileNet algorithms to address traffic congestion. The system, designed for urban conditions, uses real-time video tracking to count vehicles and intelligently allocate traffic signal timings based on vehicle density. It aims to reduce traffic delays and improve efficiency, offering a modern solution to traditional delay-based traffic light systems. The project is presented with a live demonstration, showcasing its capabilities and potential impact on urban traffic flow.

Takeaways

• 🚦 The project introduces a Smart Traffic Management System using YOLO V4 and MobileNet algorithm to address traffic congestion issues.
• 📅 The project is based on research from 2023 and was published in August 2023, highlighting its recency and relevance to current societal needs.
• 🌐 It aims to solve traffic problems in cities by intelligently managing traffic signals based on real-time vehicle counts per lane, rather than traditional delay-based techniques.
• 📈 The system works by processing live video feeds to count vehicles in each lane and then adjusting traffic light timings to optimize traffic flow.
• 🛣️ The project acknowledges that traditional traffic light systems can cause traffic jams by not accounting for varying vehicle densities across lanes.
• 🔍 The system uses deep learning to analyze video frames, detect vehicles, and count them to determine the optimal traffic signal timings.
• 💡 The project suggests that this approach can reduce overall traffic delays and improve the efficiency of traffic management in urban areas.
• 🔧 The system is designed to be flexible, allowing for adjustments in traffic specifications such as frame count and iteration timing to suit different traffic conditions.
• 📊 The project provides data visualization features to display traffic flow, vehicle counts, and traffic density, helping to analyze and understand traffic patterns.
• 🔗 The project is offered for purchase through ITW Expert.com, with the promise of providing all necessary project files and support.

Q & A

What is the main project discussed in the transcript?

The main project discussed is a Smart Traffic Management System using YOLO V4 and Mobilenet algorithm.

What is the purpose of the Smart Traffic Management System?

The purpose is to manage traffic efficiently by calculating the number of vehicles on each lane and allocating traffic signals accordingly, aiming to reduce traffic congestion.

How does the current traffic system operate as described in the transcript?

The current system operates on a delay-based technique where traffic lights cycle through fixed intervals, regardless of the actual vehicle count on each lane.

What are the issues with the existing traffic management systems mentioned in the transcript?

The issues include inefficiency in rain and shine, reliance on human control which is a huge manpower task, and the inaccuracy and failure-prone nature of sensor-based systems.

How does the proposed deep learning approach differ from traditional methods?

The deep learning approach uses real-time video tracking to count vehicles and predict traffic signals, thus overcoming the limitations of fixed timing and sensor failures.

What are the key components of the Smart Traffic Management System project?

The key components include real-time video tracking, vehicle counting using deep learning, and automatic traffic signal control based on vehicle density.

What is the significance of using YOLO V4 and Mobilenet in this project?

YOLO V4 and Mobilenet are used for object detection and classification, which are crucial for accurately counting vehicles and categorizing them for traffic signal allocation.

How does the project handle different weather conditions that might affect sensor-based systems?

The project uses a video-based deep learning approach that is less affected by weather conditions compared to sensor-based systems.

What is the process of running the Smart Traffic Management System as outlined in the transcript?

The process involves setting up input and output folders, specifying traffic parameters, running the main code, and then using the system to manage traffic based on real-time video input.

What are the future enhancements mentioned for the Smart Traffic Management System?

The future enhancements include applying armless prediction models and combining them with other concepts for better performance and accuracy.

How can one obtain the project and stay updated with similar projects?

One can contact iwexpert.com to obtain the project and subscribe to their channel for future projects and updates.

Outlines

00:00

🚦 Introduction to Smart Traffic Management System

The speaker introduces a project from ITW Expert.com aimed at addressing traffic congestion using a smart traffic management system. The project utilizes YOLO V4 and MobileNet algorithms and is designed to be particularly beneficial for current societal issues. It was published in August 2023 and is intended to tackle traffic problems in cities by intelligently managing traffic signals based on real-time vehicle counts per lane. The traditional delay-based traffic light system is critiqued for causing traffic jams, and the proposed deep learning approach promises to improve upon it by dynamically adjusting traffic signals according to vehicle density.

05:01

🛣️ Project Overview and Setup

The speaker provides an overview of the project, which includes a hybrid approach for better performance and accuracy. The system processes video inputs to calculate vehicle density and control traffic signals accordingly. The project's flow diagram is explained, highlighting the conversion of video into frames, object detection, and traffic signal control. The speaker mentions the use of Python and Anaconda for implementation and outlines the project requirements, emphasizing the advantage of using live camera feeds for real-time traffic management. The speaker also encourages viewers to subscribe to their channel for updates on Python, deep learning, and other technologies.

10:03

🔧 Demonstration of Traffic Signal Control

The speaker demonstrates the project by setting up input and output folders and configuring traffic specifications. They explain the process of testing the algorithm with a single image to ensure it can accurately detect and count vehicles. The system then uses this data to control traffic signals, prioritizing lanes with higher vehicle counts. The speaker runs the 'Intelligent Traffic Manager' to show how it predicts and manages traffic in real-time, adjusting traffic light timings based on vehicle density rather than fixed delays.

15:03

📊 Data Analysis and Visualization

The speaker discusses the data analysis capabilities of the project, which include creating datasets for traffic flow and density. They show how the system automatically updates traffic data and can generate Excel files for detailed reports. The speaker also demonstrates data visualization features, which include graphs showing the average number of vehicles per lane over time, traffic flow on each lane, and comparisons with existing traffic systems. The visualizations highlight the system's effectiveness in reducing traffic and waiting times compared to traditional methods.

20:04

📈 Conclusion and Future Enhancements

The speaker concludes by summarizing the project's success in reducing traffic and improving traffic light management. They mention future enhancements, such as incorporating armless prediction models and other concepts. The speaker invites interested parties to contact iwexpert.com for project details and encourages viewers to subscribe to their channel for updates on future projects.

Keywords

💡Machine Learning

Machine learning is a subset of artificial intelligence that provides systems the ability to learn and improve from experience without being explicitly programmed. In the context of the video, machine learning is central to the development of the Smart Traffic Management System, which uses algorithms to analyze traffic patterns and make informed decisions about traffic signal timings.

💡YOLO V4

YOLO (You Only Look Once) V4 is an advanced, real-time object detection algorithm that is used in the project to identify and count vehicles in each lane. It is a key component of the intelligent traffic management system described in the video, as it enables the system to 'see' and understand traffic conditions in real-time.

💡MobileNet

MobileNet is a type of neural network architecture designed to be efficient and lightweight, making it suitable for mobile and edge devices. In the video, MobileNet is mentioned as part of the algorithms used in the traffic management system, likely to ensure that the system can operate effectively on various devices, including those with limited processing power.

💡Traffic Management System

A Traffic Management System is a technology-based solution designed to optimize traffic flow within a transportation network. In the video, the Smart Traffic Management System using YOLO V4 and MobileNet is presented as a project that aims to alleviate traffic congestion by dynamically adjusting traffic signal timings based on real-time vehicle counts.

💡Real-time Video Tracking

Real-time video tracking refers to the process of analyzing video feeds continuously as they are captured, allowing for immediate response to the observed data. In the video script, real-time video tracking is essential for the traffic management system to function, as it enables the system to count vehicles and adjust traffic signals in real-time.

💡Vehicle Tracking

Vehicle tracking involves monitoring the position and movement of vehicles, which is crucial for traffic management. The video discusses a system that uses deep learning to track vehicles in real-time, which helps in making data-driven decisions for traffic signal control.

💡Deep Learning

Deep learning is a branch of machine learning that uses neural networks with many layers to model and understand complex patterns. In the video, deep learning is applied to process video frames and count vehicles, which is a core functionality of the proposed traffic management system.

💡Traffic Signal Optimization

Traffic signal optimization is the process of adjusting traffic light timings to improve the flow of traffic. The video describes a system that uses deep learning to analyze vehicle counts and optimize traffic signal timings, aiming to reduce traffic congestion and waiting times.

💡Congestion

Congestion refers to the overcrowding of a traffic network, leading to slow movement or standstills. The video's project aims to address the issue of congestion by implementing an intelligent traffic management system that can predict and alleviate traffic buildups.

💡Literature Survey

A literature survey is a comprehensive review of existing literature on a particular topic. In the context of the video, a literature survey is conducted to understand the current state of traffic management systems, identify存在的问题, and justify the need for the proposed intelligent system.

💡Data Visualization

Data visualization is the graphical representation of information and data. The video mentions using data visualization to display the number of vehicles on each lane and traffic flow, which helps in understanding traffic patterns and the effectiveness of the traffic management system.

Highlights

Introduction to a smart traffic management system using YOLO V4 and Mobilenet algorithm.

The project aims to address societal traffic problems, particularly in urban areas.

Traditional traffic light systems operate on a delay-based technique, which can cause traffic congestion.

Proposing a deep learning approach to calculate vehicle counts and allocate traffic signals accordingly.

The project is titled 'Intelligent Staffing System for Urban Conditions using Real-Time Video Tracking'.

The system uses video-based deep learning to predict traffic signal timings based on vehicle density.

Existing traffic reduction systems face issues like sensor failure and inaccuracy in various conditions.

The project overcomes limitations of sensor-based and human-controlled traffic systems.

The system processes live video, converting it into frames to extract vehicle counts and types.

The project agenda includes combining a video processing model with a traffic light control module.

The system is designed to be flexible, allowing for the use of any video input.

The project is implemented using Python and run with Anaconda ID.

The system can reduce overall traffic delays and improve traffic flow.

Future enhancements include applying armless prediction models and other concepts.

The project provides a detailed demonstration of how to set up and run the traffic management system.

The system automatically updates traffic light timings based on real-time vehicle counts.

Data visualization features show traffic flow, vehicle counts, and optimization over time.

The project concludes with a comparison of traffic reduction and waiting time improvements over existing systems.

Contact information provided for those interested in purchasing the project.

Transcripts

hello everyone welcome to ITW expert.com

so we are providing best machine

learning project at a better price okay

today we are going to see some of the

Innovative project very much useful for

the current Society so this is the

project which we are going to talk about

today smart traffic management system

using YOLO V4 and mobilenet algorithm

this is the project we are going to

discuss about today this project based

on

2023i okay this project published on

recent years okay so as soon as you can

see it's published on August 2023 very

much recent project this project

completely useful for society problem

okay nowadays each and every city even

which is a small City or bigger city

okay everyone facing traffic problem

here why because we are facing traffic

problem means

uh somewhat Road contains four lane

Alpha Helene and all means the traffic

light which used to reroute the all the

vehicles it operates on the delay based

technique delay based means for example

we are taking four signal means party

signal will work for 30 seconds next

signal will work for 30 seconds another

signal will work for 30 seconds same

okay it's the same Target how much

vehicle each lane contains it will

operate based on the delay it completely

operates on the delay only okay for

example if you are taking four lane

means

I do explain with the image okay

for example if you are talking four lane

roads means this is the four lane roads

in roads you can see this Lane having

this Lane having very much lower number

of vehicles this Lane and this line

having high number of vehicles this line

having very much low number of vehicles

but the traffic system will give 30

seconds for this length 30 seconds for

this length 30 seconds for this length

then 30 seconds

very much worst scenario okay too much

of traffic will happen due to this so

what we are going to do means we are

going to propose completely deep

learning post approach which can able to

calculate how much Vehicles available on

each lane depends upon the vehicle count

it will allocate the traffic signal

that's the system we are going to do

today okay this is the project PPD which

you are going to explain today so this

is the project PPT I will explain that

so we are given modified titled as a

intelligent Staffing system for urban

condition using real-time video tracking

vehicle tracking this is the project

title module title we are given this is

the project abstract so congestion was a

very much serious issue nowadays so to

fix the congestion issue what we are

going to do means we have to enable the

real-time traffic controlling system we

have time Intelligent Traffic management

system a project the completely based on

the video based approach if you are

giving video output of the link means it

will cash it how much weights available

on the each lane using deep learning

depends upon the vehicle code

automatically will predict the traffic

signal then it will automatically after

the traffic signal as a traffic lights

also that's what you are going to do

today so main objective was we have to

reduce the overall traffic so for that

we are going to use traffic prediction

system

so in introduction we can mentioned that

various problem faced by traffic this is

the literature survey of the project we

have taken recent literatures okay then

what's the major problem in the

literature we are given overall existing

system some of the traffic reduction

system uses

sensor burst approach they have placed

some sensor on the roads the sensor will

estimate how much vehicle passed by

using sensing operation the major

problem was in rainy condition and told

it won't work in item it won't work so

much of problems are there

also some human based systems also

available some humans able to come

control the traffic some length they

will give higher priority sampling they

will give lower variety like that that's

also huge Manpower task okay that is a

major drawback in existing system

traffic police facing too much of a

problem as well as in sensor based

approach less accuracy we are getting

sensors are prone to failure too much of

failure to larger on the sensor to

overcome this only what you are going to

do means we are going to use deep

learning that means a based model so

here this project if you are giving live

video means it will calculate all the it

will convert all the videos into frames

from the frames it will extract the

images okay from the images it will

calculate how much buses available how

much cars available how much bikes

available based on the vehicle count it

will automatically operate the traffic

signal okay due to this we can overcome

most traffics okay this is the project

over agenda so in our foreign

System including with that we are going

to add on our next version 3 combined

combiningly we are creating hybrid

approach for better performance as well

as better accuracy for this project this

is the main thing here okay it's the

overall flow diagram we are giving vdr

images means it will calculate the get

direction but by using that it will

calculate the vehicle density from using

that it will operate the traffic signals

okay so a major question model you can

use any video here the processing model

will convert those videos into frame

format from that it will calculate that

how much trucks various object direction

we are going to apply here okay some

from that it will capture the count data

from that count light control module on

after like so this is the overall modest

of the project we are going to use

Python language for that we are going to

run using Anaconda ID so this is the

project requirements so major advantage

because we are going to use live camera

here uh you can use any number of videos

32 40 frames twice again I need contrast

video local to see anything you can use

Okay so is a major conclusion of the

project by using this a model we can

reduce overall delay in reaching your

destination okay so in future we can

apply armless prediction model some

other concept also including with this

this is the overall reference of the

project so without wasting much more

further time I'm going to run the

project

so for that this project report also

available once you are purchasing means

we do give everything okay so I am going

to use some videos so this is my video

folder you can use I as I mentioned

earlier you can use any number of videos

here nothing issue

we can use any number of videos

the example I am going to use

one of the video

so this is the video I'm going to use

so this video This is the first Lane

video second lane then third line fourth

link showing here

different lens videos this is another

main Lane

this is also another leg

this is also another line

so those are the lane videos I'm going

to use here okay different angle layers

so what I'm going to do means I'm going

to run my main coding this is my project

my main coding I'm going to run my

project main code so to run my main code

what I'm going to do is I'm going to

copy the project location

in that you have to open your project

command prompt project terminal you have

to use

those installation links installation

instruction everything included with the

project you will get everything

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yeah

so I am going to open my project

terminal

so in the project terminal you have to

copy the location of the project you

have to paste using TD Space Project

location then enter you have to run

main.poi so what you have to do means

you have to return space

main.py you have to run with this python

space main.viewer

once you are running means it will

create your page so this is your project

location page so intelligent traffic

manager first of all what you have to do

means you have to set your input folder

you have to Output folder I am going to

set my input folder my video input

folder

I'm going to set my video input folder

so it's available on python scripted

Intelligent Traffic management system

so input I am going to set input

then I am going to set output also so

output also same mobile uh they are only

available python script Intelligent

Traffic management system then I have to

set output folder that's it everything

run after that you have to set your

traffic specification in traffic

specification I am going to consider 100

number of frames that means 100 minutes

of traffic I have to consider you can

change your timing whatever I'm going to

give iteration as a one how much uh any

outer run so I am going to apply

iteration as a one that's if you here

you can change the lane with road with

everything you can change in cycle

timing everything you can change okay

just you okay that's it so now it's

updated data set size as well as

everything updated so I'm going to check

with the one single images

work or not so what I'm going to do

means I'm going to check with one image

just click test email process algorithm

it will check with one images

so see that it perfectly working so this

is the image I am going to give see that

images the video converted into frames

you can see videos converted into the

frames

multiple frames from that randomly it

will take one images see that this is

the input image we are going to give if

this is the output image you can see

clearly everything okay here car car bus

bus everything detector automatically so

the overall vehicle detected count was

70 okay which is very much best okay

it's working perfectly

so just give back

so once vehicle counted means what you

have to do means we have to use run

Intelligent Traffic manager okay this

will run the traffic data as per that so

now I am going to run

traffic Intelligent Traffic manager just

run that

see that it's predicting the data car

bus bus bus how much cars available how

much bus available on each and every

lane it automatically predict everything

okay see that

buses cars everything it will predict

automatically

so now the traffic light also opened

okay you can see first priority will be

the lane one you can see 19 Vehicles

period uh predicted on the lane one just

a click Start Now traffic lights will

start to work okay just click Start here

see that traffic light enabled for the

lane one okay so if now uh orange color

then it began to green color okay the

overall saturation Pro for the lane one

will be 180.

so time given for the traffic lane one

was 34

.

so now it's running for the Lan one next

you move for the another line

so Lane one going to off it moved to the

Lane 2 now okay Lane 2 going to run

so if it's now which will give less time

for the length to

then it will move for the another Lane

see that now time was very low only now

consider the other point now time was

very low then move to the Lane 3

now it moved to the lane four

so it will operate based on the vehicle

density automatically okay based on it

will automatically visual density okay

it won't work with the delay one two

three like that using in the existing

system

see that our lane completed now go back

check Road View

Road view means it will give data for

the view for the all the four lanes okay

see that this is the lane structure you

can see

four lanes Lane one contains 22 images

Lane 2 contains 19 uh sorry Lane on

Country 22 Vehicles Lane 2 contains 19

Vehicles Lane 3 contains 20 Vehicles

Lane 4 contains 18 Vehicles so it will

automatically operate based on the next

Point traffic light will work on the

each and every frames depends upon the

vehicle from it will automatically

update you don't need to worry about

that it will give priority based on the

vehicle's count okay once vehicle passed

means it will automatically update okay

then go back you can check create data

set just give create data set

it will give the data set for all

devices see that

Lane one first to frame Lane 129 Lane

237 Lane 342 Lane 4 19. so the overall

traffic for the each and every lane next

next set of traffic 17 33 3 Vehicles 24.

the traffic density data

Lane three plane for 27 okay this is the

overall traffic flow it will

automatically upload update those data

so just Eclipse give save here then go

to back

now you can check display data

so this is your overall data set okay

here you can get much number of details

okay much number of details okay so this

is the overall data set created by our

project in the data set you can check

okay number of vehicles on Lane one

number of vehicles on lane two number of

eggs and Lane three number of Vacation

Lane four how much traffic density and

Lane one how much time it is

how much traffic density

given for Lane one lane two lane three

lane four how much time driven for each

and every lens so overall Lane diameter

Lane uh Lane length for the each lane

you can radius or increase depends upon

the coding okay so this is the priority

for each and every lens okay this is the

priority ratio

so this is the priority ratio for each

and every line so this is the

Optimum allocated resources for the each

lane okay is the optimum allocated

resource for each lane so this is the

data for all the frames that means we

are given 100 timing in now 100 minutes

so 100 minutes means it will create all

the data for 100 minutes okay you can

check all the details details okay

so 100 minutes each and every minute it

will allocate the traffic okay each and

every minute you can give 1 000 minutes

60 Minutes anytime you can give so we

are given 100 minutes to run this so

this is the traffic data for each and

every 100 minutes just to close this

here those datas will save on here also

you can check this data it will save on

here also

so automatically it will create the

Excel file also you can show the Excel

file also to your guide Center so okay

clear details everything will be given

clearly okay so total time to complete

this traffic okay how much time we have

saved everything given here

so then what you have to do means you

have go to next PATH in the next part

just to give data visualization

in data visualization just to go to

number of vehicles on each lane

just click this

so this is the average number of

vehicles present on the each lane this

is the average number of vehicles

present on the each lane for the 100

Millions okay this is the first minute

second minute third minute hundred

minutes you can check for example in

first minute in Lane 1 30 Vehicles

nearly in Lane 2 nearly 40 vehicles in

Lane

three nearly 45 Acres but in lane four

nearly 20 Vehicles this is the traffic

density for each and every lane for 100

minutes I given 100 minutes in the

initial running itself you can change

that also then close then go to traffic

flow on each link

so this is the traffic flow

so how much traffic we are getting under

each lane for the 100 minutes it will

generate a graph for each and every lane

for 100 minutes okay

then

traffic flow ratio on each lane

total traffic flow how much traffic you

are reduced okay in first minute the

project started no that 10 huge traffic

happened

on 70 minutes you can see this is 70. in

70 minutes also we have faced a huge

traffic Peak number of traffic otherwise

traffic was very much low on the other

time so this is the big traffic period

on the project

then go to Optimum cycle time so Optimum

cycle time means how much

priority are given for each and every

lens how much 120 that means 12 seconds

that is 8 seconds each and every second

how much traffic periodic is given for

each and every things okay how much

traffic you have reduced well compared

to the existing system how much traffic

you have reduced this is the graph for

the comparison how much traffic you have

reduced oil company to the existing

system

so this is the

which is how much vehicles

uh weighted how much time the vehicles

are weighted on the each leg how much

time vacants are weighted on the each

leg the waiting time also reduced while

comparing to the exiting system the

waiting time also reduced this is the

major advantage of this product

is the complete project you can purchase

everything you

okay

so to get this project please contact

iwexpert.com we do provide this project

at best price okay please subscribe our

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