LEVERAGING GRAPH MACHINE LEARNING FOR PREDICTING TRAFFIC CONGESTION AND OPTIMIZING VEHICLE ROUTING

Abstract

Traffic congestion in urban areas is a persistent problem with economic and environmental implications. To address this challenge, advanced technologies are increasingly being employed, and one of the most promising approaches involves graph machine learning. This article explores the application of graph machine learning in predicting traffic congestion and optimizing vehicle routing in urban traffic networks. We discuss how graph representations of road networks, combined with historical and real-time data, can be harnessed to develop machine learning models that predict congestion and optimize vehicle routes. By employing this approach, urban traffic management can become more efficient and responsive, leading to reduced congestion and improved transportation systems.