Enhancing traffic flow monitoring with machine learning integration on cloud data warehousing

Abstract

With urbanization and rising vehicle ownership rates, global traffic congestion and accidents have become pressing issues. This paper delves into leveraging cloud data warehousing and machine learning to tackle traffic flow monitoring and prediction, along with their potential in intelligent transportation systems. Through comprehensive analysis and case studies, it highlights how modern technology can enhance urban traffic management and services. Initially, the paper underscores the importance of traffic monitoring and prediction, identifying shortcomings in traditional approaches and advocating for machine learning solutions. It then reviews traditional traffic monitoring methods and explores machine learning's role in traffic flow prediction, illustrating its widespread application and evolving trends. Subsequently, the paper delves into the pivotal role of data warehousing in machine learning, encompassing data integration, management, cleaning, and multidimensional analysis. Additionally, it discusses the significance of relation matrices in graph convolution and presents experimental designs and model results for traffic flow prediction. Finally, the paper summarizes research findings, emphasizing the significance and future prospects of machine learning model design and cloud data warehousing in intelligent transportation systems.