International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 10 Issue: 04 | Apr 2023
p-ISSN: 2395-0072
www.irjet.net
Congestion Control System Using Machine Learning Zainab Pevekar1, Anas Mukri2, Muaviya Momin3, Vijay Kholia4, Vivek Pandey5 1234 Computer Engineering Student, ARMIET, Shahapur, India 5 Professor, ARMIET, Shahapur, India
---------------------------------------------------------------------***-------------------------------------------------------------------management. This is often caused by factors such as Abstract - In today's fast-paced world, technology and malfunctioning traffic lights or a higher number of vehicles on one side of the road than the other during rush hours. Therefore, there is a need for intelligent traffic management solutions, which can be addressed using Machine Learning-based object detection techniques.
population growth are increasing at an unprecedented rate. As a result, governments are increasingly interested in managing and developing their road networks, especially in densely populated countries where traffic is a major issue. However, traffic officers may not always be able to handle large volumes of traffic, particularly during festivals or other peak periods. Therefore, we conducted research to address these issues, specifically focusing on how to automate traffic management and how to ensure that ambulances can navigate through heavy traffic. This paper presents a solution to these road congestion problems by leveraging the new technology of Machine Learning (ML). We used ML algorithms, datasets, and mathematical calculations, programmed in Python, to conduct our research. Our focus was on developing automated traffic management solutions that could handle large volumes of traffic and ensure that emergency vehicles such as ambulances can quickly move through congested roads. Python is a programming language that offers a versatile platform for performing a variety of operations, including object detection, image processing, video processing, and more We have designed some algorithms which can handles larger traffic. The design also has second proposed system for helmet discovery and license plate recognition to detect and identify the twowheeler riders without helmet and thereby penalizing them. Object discovery utilizing YOLOV4 is the main concept. Object discovery is acted at various levels to label the headgear regulation lawbreaker and their license plate number. Using a License Plate Recognition API, the license plate number is before gleaned. A database is maintained that consists of details of two- wheeler possessors. An dispatch conforming of violation details and a link to pay the penalty is transferred to the helmet law violators. An interface is developed using Tkinter that can be used by executive officer to check for the violators in the vids handed as an input and also to cover the entire process.
The statistics show that the primary obstacle faced by emergency vehicles, such as ambulances, is navigating through traffic, particularly during peak hours. Currently, there is no dedicated monitoring system in India, except for CCTV footage, and over 56% of accidents occur while transporting patients. To reduce the number of fatalities, there is a need to create a cost-effective smart traffic management system that leverages unconventional technologies. The proposed system caters to the needs of emergency vehicles, such as ambulances, fire trucks, and police vehicles, by creating a smart traffic light system that can communicate with each other through relay signals via microcontrollers. The system employs a Compact Prediction Tree (CPT) algorithm, which is a derivative of Deep Neural Network (DNN), to perform computations at the same rate as regular deep learning algorithms. CPT is a recurrent neural network algorithm that supports lossless compression of the training data while retaining all relevant information for each prediction. The system also aims to distinguish between riders with and without helmets by detecting their faces in video frames, extracting the area of the rider's head, and classifying whether the rider is wearing a helmet. Additionally, the system proposes an automated system for detecting high-priority vehicles and giving them priority on the road. This paper proposes a method to detect helmetless riders using pre-recorded videos that could be further developed into a continuous surveillance system for motorcyclists. Furthermore, the system proposes an automated system for detecting high-priority vehicles and providing them with priority on the road. The system also includes an algorithm for retrieving motorcycle number plates from CCTV footage, generating emails, and storing violator details with minimal human intervention.
Keywords: Alex Net, COCO, Python, TensorFlow, YOLO.
1.INTRODUCTION
2. LITERATURE SURVEY
The urban areas are equipped with advanced technology, including various electronic devices, sensors, and big data management systems. Among these technologies, the development of city roads stands out. However, the most common challenge faced by these cities is traffic
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[1] The objective of the paper "Employing Cyber-Physical Systems-Dynamic Traffic Light Control at Road Intersections", published in the IEEE Internet of Things
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