Thesis Abstract

Abstract
The rapid growth of urbanization and the increasing number of vehicles on the road have led to significant challenges in traffic management and control systems. In response to this, the development of intelligent traffic control systems utilizing machine learning algorithms has gained attention as a promising solution. This thesis presents a comprehensive study on the development of an Intelligent Traffic Control System (ITCS) using various machine learning algorithms to optimize traffic flow, reduce congestion, and enhance overall road safety. The research begins with a detailed investigation into the current state of traffic control systems and the existing challenges faced in urban traffic management. By exploring the background of the study, the significance of implementing an ITCS becomes evident in addressing these challenges effectively. The problem statement highlights the inefficiencies of traditional traffic control systems and emphasizes the need for intelligent solutions to improve traffic flow and reduce travel time for commuters. The objectives of the study are outlined to guide the development and evaluation of the ITCS. These objectives include designing a machine learning-based traffic prediction model, developing an adaptive traffic signal control system, and assessing the performance of the ITCS through simulation and real-world testing. The limitations and scope of the study are also discussed to provide a clear understanding of the research boundaries and potential implications. Through an extensive literature review, the thesis explores various machine learning algorithms and their applications in traffic control systems. The review covers topics such as traffic prediction models, adaptive signal control systems, and optimization techniques for traffic flow management. The analysis of existing research provides valuable insights into the best practices and methodologies for implementing intelligent traffic control systems. The research methodology section details the process of developing and evaluating the ITCS. It includes data collection methods, algorithm selection criteria, system design architecture, simulation techniques, and performance evaluation metrics. By outlining these research methodologies, the thesis aims to ensure the reliability and validity of the study outcomes. The discussion of findings chapter presents the results of the ITCS implementation, including the performance of the traffic prediction model, the effectiveness of the adaptive signal control system, and the overall impact on traffic flow efficiency. The analysis of these findings offers insights into the practical implications of using machine learning algorithms in traffic management and the potential benefits for urban transportation systems. In conclusion, the thesis summarizes the key findings, implications, and future research directions for the development of intelligent traffic control systems. The study demonstrates the feasibility and effectiveness of utilizing machine learning algorithms to optimize traffic flow and enhance road safety in urban environments. By integrating innovative technologies and data-driven solutions, the ITCS represents a significant step towards improving the efficiency and sustainability of urban transportation systems.

Thesis Overview