Smart Deep Learning based Traffic & Congestion Prediction System

The title of our Project is ?Smart DL based Traffic Management and Congestion Prediction System?. In this project we will sort out the current traffic problem growing in the Twin cities. The Project will be based on Machine Learning (ML), Deep Learning (DL) and a Web based Interface

Project Title

Smart Deep Learning based Traffic & Congestion Prediction System

Project Area of Specialization

Artificial Intelligence

Project Summary

The title of our Project is “Smart DL based Traffic Management and Congestion Prediction System”. In this project we will sort out the current traffic problem growing in the Twin cities. The Project will be based on Machine Learning (ML), Deep Learning (DL) and a Web based Interface. In this Project we will reduce the problem of traffic Jam on peak hours by collecting the Real data and helping the people to get on alternate route.

Recently, the usage of cognitive computing in the Internet of Things (IoT) has gained wide popularity as self-learning algorithms can be injected into smart objects or things in order to simulate human thought process. This technology is called as Cognitive Internet of Things (CIoT). CIoT can revolutionize several applications in the years to come including transportation, healthcare, smart cities to name a few. Among all these applications, CIoT has been widely used in the transportation sector from the last many years.

In the current transportation applications, drivers take responsibility to control the vehicle in different unexpected situations such as controlling speed, change of lanes etc. However, human drivers may behave drowsy, drunked or drugged leading to irrational decision making such as road accident, wrong path selection etc. According to a study in it is reported that human ignorance is the foremost reason behind the majority of road accidents.

Our Goal is to is to predict the traffic congestion on roads and display the result via map.

Project Objectives

Our aim is to reduce the traffic problem on the minor roads of twin cities. To develop a DL based safe secure system for the audience so that people can reroute so that they can avoid traffic jam and opt another route.

System will predict and show the traffic congestion on the roads in twin cities and predict the traffic flow/congestion on roads in twin cities on the basis of historic traffic data.

The collected data will help system to apply deep learning algorithm over it so that it can predict the traffic flow/congestion on roads in twin cities. Users can check information using web base application so that they can reroute in time before they leave home.

Project Implementation Method

We use historic data in our project. Historic data is collected and then datasets are prepared.Data will be processed, normalized and optimized so that irrelevant data is excluded, and missing data is handled so that it can be used for system.Normalized data will be partitioned into two categories one for training the model and other to evaluate/test the model.In learning phase different algorithm is used on the dataset to train the system and when the system is trained, we use evaluation data to evaluate model.Models will be generated, and model with higher accuracy will be chosen.The system is checked to refine model.

Benefits of the Project

Smart Deep Learning Based Traffic Management & Congestion Prediction System is user friendly application eventually naive user also easily use this application. Simple, Smart Deep Learning Based Traffic Management & Congestion Prediction System interface is not overly complex, but instead is straightforward, providing quick access to common features or commands. Clean, Smart Deep Learning Based Traffic Management & Congestion Prediction System interface is well-organized, making it easy to locate different tools and options. Intuitive, Smart Deep Learning Based Traffic Management & Congestion Prediction System interface make sense to the average user and should require minimal explanation for how to use it. Reliable, an unreliable product is not user-friendly, since it will cause undue frustration for the user. Smart Deep Learning Based Traffic Management & Congestion Prediction System is reliable and does not malfunction or crash.  

Smart Deep Learning Based Traffic Management & Congestion Prediction System signifies a level of performance that describes using the least amount of input to achieve the highest amount of output and accuracy using different Machine Learning Algorithms.

Technical Details of Final Deliverable

 As traffic congestion is major growing day by day and due to immense increase in the vehicles day by day isn’t possible to handle manually So our purpose is to introduce a DL base system that will handle traffic congestion smartly.

Congestion is relatively easy to recognize roads filled with cars, trucks, and buses; sidewalks filled with pedestrians.

With the increasing population the problem of traffic control has become very acute in almost all the big cities in Pakistan. The increase in traffic has also increased the number of traffic accidents and our roads are growing dangerous day by day. Unfortunately, we have little traffic sense and perhaps no respect for the traffic rules. No doubt, bad roads and high-speed driving are the main causes of traffic accidents Traffic problem in Pakistan is getting worse with every coming day, especially in big cities like Karachi, Lahore, Rawalpindi, Islamabad, Quetta, Peshawar and Hyderabad. Government is doing little efforts to avoid traffic jams and other traffic problems in Pakistan.

Traffic congestion is one of the major growing problem in twin cities now a days in Pakistan it is due to rapid growth in vehicles productivity. Traffic jamming is one of the major causes of resource wastage such as time fuel energy etc. And in Pakistan traffic jamming is controlled manually which is hazardous and time wasting. People become exhausted as they have to wait longer period of time when they don’t need to. It creates a massacre when there is traffic jam.

 Our system is using machine learning and deep learning techniques to overcome this problem which is due to controlling traffic signals manually. Our system will control the signal according to flow of cars and manage the traffic accordingly by collecting real data via sensors.

Deep learning algorithm will predict the traffic flow/congestion on roads in twin cities after training machine over real data.

Will also help to tell users what the traffic flow rate in certain hours of time is. In this way user can save major resources like time and fuel and can also avoid problems which are caused due to traffic jam such as nausea, hypertension, migraine etc. Most of the traffic accidents are due to mismanagement and controlling signals manually automation will reduce such factors.

Final Deliverable of the Project

Software System

Core Industry

Transportation

Other Industries

Telecommunication

Core Technology

Artificial Intelligence(AI)

Other Technologies

NeuroTech, Others

Sustainable Development Goals

Decent Work and Economic Growth, Industry, Innovation and Infrastructure

Required Resources

If you need this project, please contact me on contact@adikhanofficial.com