HAG-MTF: Higher-Order Adaptive Generative Graph for Massive Traffic Forecasting in Industry 5.0

With the evolution of urban smart transportation, the complexity of urban traffic networks escalates, emphasizing the importance of large-scale traffic data prediction in traffic management and urban planning. Traditional spatiotemporal graph models, such as Graph-WaveNet and MTGCN, face exponentially increasing computational complexity as the spatial dimensions expand. To address this challenge, we propose a novel Higher-order Adaptive Generative graph for Massive Traffic Forecasting (HAG-MTF) approach, which utilizes generative AI and high-order graph structures to model the intricate spatial dependencies in large-scale traffic data. The HAG-MTF incorporates a high-order dimensionality reduction module to optimize traffic node processing, utilizing prior graph relationships to generate a fusion graph that dynamically incorporates neighborhood information for efficient, localized graph convolution. The model further incorporates the high-order spatiotemporal relationship extraction module (H-net), enhancing the capacity and speed of traffic data processing while boosting prediction accuracy for complex spatial structures. Furthermore, HAG-MTF introduces a fusion loss function that hierarchically balances multiple objectives, ensuring both precision and computational efficiency. HAG-MTF adaptively handles large-scale real-world traffic data, meeting the needs of traffic controllers and urban planners for predicting massive datasets in practical settings. It supports efficient, flexible interactions via parameter tuning and model outputs, ultimately integrating human insights into traffic analysis and decision-making. This dynamic human-machine collaboration differs from non-Industry 5.0 approaches, which rely on purely automated systems without human input. Those lead to inflexible, brittle conclusions and recommendations, neglecting shifts in traffic patterns driven by human behavior. Extensive experiments on real-world traffic datasets demonstrate that HAG-MTF significantly improves processing efficiency for high-complexity spatial data while delivering precise, human-informed predictions through generative AI-driven operations.