OPTIMIZING ROAD DESIGN THROUGH MACHINE LEARNING-BASED TRAFFIC PATTERN ANALYSIS

Abstract

Traffic congestion is increasingly recognized as a global challenge. This study examines the application of data mining and machine learning technologies in addressing both direct and indirect traffic issues that impact society. The collected insights are valuable for traffic research organizations, software developers, and governmental traffic authorities, as they inform the development of effective strategies for traffic management and control. By reviewing existing literature, this study provides a comprehensive analysis of how data-driven approaches are utilized in traffic research and enterprise solutions. Furthermore, the study introduces a novel methodology for traffic management, highlighting the potential of machine learning to enhance road infrastructure planning, optimize traffic flow, and support evidence-based decision-making in transportation systems.Machine Learning