Intelligent Traffic Management System using Machine Learning | Machine Learning Projects 2023 2024
Summary
TLDRThis 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
🚦 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.
🛣️ 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.
🔧 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.
📊 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.
📈 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.
Mindmap
Keywords
💡Machine Learning
💡YOLO V4
💡MobileNet
💡Traffic Management System
💡Real-time Video Tracking
💡Vehicle Tracking
💡Deep Learning
💡Traffic Signal Optimization
💡Congestion
💡Literature Survey
💡Data Visualization
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
everything while you are purchasing
so in the meantime please subscribe our
channel so we do follow video update
regularly most useful videos for python
deep learning Ulsa embedded technology
so okay most recent projects most recent
Technologies working videos everything
video upload so don't forget to follow
our Channel we do release coupons for
project purchasing also so if you are
following our Channel means you would
get coupon updates also regularly so
don't forget to follow uh subscribe our
Channel okay
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
Channel also for future projects okay
thank you
浏览更多相关视频
Intelligent Transport Systems made in KOREA English Version 22'
Artificial Intelligence and Machine Learning for 5G Network Monitoring – COMARCH
Karakteristik Arus Lalu Lintas Makro : Grafik Greenshield (1)
Mobile Lösungen für den Verkehr der Zukunft | Shift
Seminar Topic- Intelligent Transportation System || Priyanka Sahu (5th sem, Civil)
Why Bangkok Has "Good" Public Transportation but Horrible Traffic
5.0 / 5 (0 votes)