Graph Neural Networks for Traffic Prediction and Smart City Applications

With the growing urbanization and increasing traffic challenges in smart cities, accurate traffic prediction has become a critical component for efficient urban planning and intelligent transportation systems. This research paper explores the application of Graph Neural Networks (GNNs) for traffic prediction and its implications in various smart city applications. GNNs offer a promising approach to model the complex relationships and interactions inherent in traffic networks, making them well-suited for traffic prediction tasks. The paper presents a com- prehensive literature review on traffic prediction techniques, highlighting the evolution of GNNs in this domain. Furthermore, a proposed framework out- lines the steps involved in designing and implementing GNN models for traffic prediction in smart cities. The performance evaluation and comparison section demonstrates the effectiveness of GNNs against traditional methods, emphasiz- ing their accuracy and interpretability. Several case studies showcase real-world implementations of GNNs in smart city applications, such as urban planning, intelligent transportation systems, and real-time traffic management. Finally, the research paper discusses the challenges and future directions in leveraging GNNs for traffic prediction and smart city advancement.