Thesis Abstract

Abstract
The rapid increase in urbanization and population growth has resulted in a corresponding increase in road traffic congestion and accidents. To address these challenges, this thesis proposes the design and implementation of a real-time traffic monitoring system using Internet of Things (IoT) and Machine Learning techniques. The system aims to provide accurate and timely traffic information to help optimize traffic flow, improve road safety, and enhance overall transportation efficiency. The project will involve the development of a network of IoT devices, such as sensors and cameras, deployed at strategic locations along roads and highways to collect real-time traffic data. These devices will be interconnected to form a comprehensive traffic monitoring system capable of capturing various parameters, including vehicle speed, density, and traffic flow patterns. Machine Learning algorithms will then analyze the collected data to predict traffic congestion, identify potential bottlenecks, and suggest alternative routes for drivers. The thesis will begin with a detailed introduction, providing background information on the challenges of urban traffic management and the potential benefits of implementing a real-time traffic monitoring system. The problem statement will highlight the current limitations of existing traffic monitoring systems and the need for a more advanced and efficient solution. The objectives of the study will outline the specific goals and outcomes expected from the project, while the limitations and scope of the study will define the boundaries and constraints within which the research will be conducted. A comprehensive literature review will examine existing research and technologies related to IoT, Machine Learning, and traffic monitoring systems. The review will highlight the strengths and weaknesses of previous approaches and identify gaps in the literature that the current study aims to address. The research methodology section will provide a detailed explanation of the data collection process, sensor deployment strategy, and Machine Learning model development. The discussion of findings chapter will present the results of the study, including the performance evaluation of the real-time traffic monitoring system in various scenarios. The chapter will analyze the accuracy and effectiveness of the system in predicting traffic patterns and optimizing traffic flow. Finally, the conclusion and summary chapter will summarize the key findings of the research, discuss the implications of the results, and suggest recommendations for future work and improvements to the system. Overall, this thesis aims to contribute to the field of traffic management by proposing an innovative and practical solution for real-time traffic monitoring using IoT and Machine Learning techniques. The system has the potential to revolutionize urban transportation systems, reduce traffic congestion, and enhance road safety for the benefit of both drivers and the community at large.

Thesis Overview