ГРАФОВІ НЕЙРОННІ МЕРЕЖІ ДЛЯ ПРОГНОЗУВАННЯ ТРАНСПОРТНОГО ПОТОКУ: ІННОВАЦІЙНІ ПІДХОДИ, ПРАКТИЧНЕ ВИКОРИСТАННЯ ТА ПЕРЕВАГИ У ПРОСТОРОВО-ЧАСОВОМУ ПРОГНОЗУВАННІ

Traffic flow prediction remains a cornerstone of intelligent transportation systems (ITS), facilitating congestion mitigation, route optimization, and sustainable urban planning. Graph Neural Networks (GNNs) have revolutionized this domain by adeptly modeling the intricate graph-structured nature of traffic networks, where nodes represent sensors or intersections and edges denote spatial relationships. Recent years (2023–2025) have witnessed a surge in scientific innovation, with several novel approaches pushing the boundaries of traffic prediction accuracy and robustness. Notably, hybrid GNN-Transformer architectures have emerged, leveraging the spatial reasoning of GNNs and the temporal sequence modeling power of Transformers to capture long-range dependencies and complex spatiotemporal patterns. Physics-informed GNNs integrate domain knowledge, such as conservation laws and traffic flow theory, directly into the learning process, enhancing interpretability and generalization to unseen scenarios. Uncertainty-aware frameworks, including Bayesian GNNs and ensemble methods, provide probabilistic forecasts, crucial for risk-sensitive applications and adaptive traffic management in volatile urban environments. This article provides a comprehensive guide to implementing GNNs for traffic flow prediction, detailing best practices in data preparation (e.g., graph construction, feature engineering, handling missing data), model training (e.g., loss functions, regularization, hyperparameter tuning), and real-time deployment (e.g., edge computing, latency optimization). We critically compare GNNs to traditional statistical and deep learning methods, highlighting their superior ability to capture non-Euclidean spatial dependencies, adapt to dynamic and evolving network topologies, and seamlessly integrate multi-modal data sources such as weather, events, and sensor readings. Empirical evidence from widely used benchmarks, including PeMS and METR-LA, demonstrates that state-of-the-art GNN models achieve up to 15–20 % improvements in accuracy metrics such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) over conventional baselines. These gains are attributed to the models’ capacity for dynamic graph learning, attention-based feature selection, and robust handling of heterogeneous data. Drawing on these recent innovations, this synthesis highlights GNNs' pivotal role in fostering resilient, AI-driven traffic systems for future smart cities, setting the stage for next-generation ITS solutions that are adaptive, interpretable, and scalable. In addition to these advancements, the integration of real-time sensor data and external information sources has further improved the responsiveness of traffic prediction models. Modern GNN frameworks are capable of handling large-scale urban networks, making them suitable for deployment in metropolitan areas with complex road infrastructures. The use of transfer learning and domain adaptation techniques allows models trained in one city to be effectively applied to others, reducing the need for extensive retraining. Furthermore, explainable AI approaches within GNNs are gaining traction, enabling stakeholders to understand and trust model decisions in critical traffic management scenarios. Recent research also explores the fusion of GNNs with reinforcement learning, enabling adaptive control strategies for traffic signals and congestion pricing. The scalability of GNNs ensures that they can process data from thousands of sensors in real time, supporting city-wide traffic optimization. Advances in hardware acceleration, such as GPU and edge computing, have made it feasible to deploy these models in latency-sensitive environments. Collaborative efforts between academia, industry, and government agencies are driving the adoption of GNN-based solutions in smart city initiatives. As urban mobility continues to evolve, the ability of GNNs to incorporate emerging data modalities, such as connected vehicle telemetry and mobile device traces, will be crucial for future developments. The ongoing refinement of model architectures and training protocols promises even greater accuracy and robustness in traffic flow prediction. Ultimately, the convergence of GNNs with other AI technologies is set to transform intelligent transportation systems, paving the way for safer, more efficient, and sustainable urban mobility.