Revolutionizing Urban Traffic Mobility With Graph Neural Networks-Driven Intelligent Transportation Systems (ITS)

ITS, an acronym for “Intelligent Transport Systems,” has the potential to significantly transform the administration and commute of cities. More and more people are opting for advanced ITS systems to make transportation safer, more efficient, and more environmentally friendly. The ability of Graph Neural Networks (GNNs) to process complex connections and unexpected input is driving their rising popularity. Intelligent transportation systems utilize graph neural networks.Using common baseline models and statistics in each transportation domain, urban planners and decision-makers design and assess GNN-based frameworks. “Graph Neural Networks for Intelligent Transportation Systems: A Survey” examines the advantages and disadvantages of GNNs and their application in ITS. Future studies find that geographically dispersed networks (GNNs) boost the sustainability, robustness, and efficiency of transportation networks. Enhanced efficiency is another perk.