Research on Airport Traffic Situation Based on Multi-Source Data

With the rapid development of the aviation industry, the complexity and uncertainty of airport traffic situations have increased significantly. Existing prediction methods for airport operations often rely on single data sources, which fail to comprehensively and accurately reflect dynamic changes in airport operations. This study proposes a multi-source data fusion framework for airport traffic situations and establishes an intelligent prediction model based on the fused data to achieve precise forecasting. Furthermore, optimization strategies for airport operations, including resource allocation and flight scheduling, are proposed based on prediction results. The effectiveness of these strategies is validated through simulation experiments. The results demonstrate that the intelligent prediction method based on multi-source data fusion significantly improves the accuracy and reliability of airport operation forecasting, providing a scientific basis for airport management and enhancing operational efficiency and service quality. The innovation of this research lies in proposing a hierarchical fusion architecture that combines deep learning with attention mechanisms to address spatiotemporal alignment challenges of heterogeneous data, as well as employing multi-objective optimization algorithms to balance resource utilization and passenger satisfaction metrics.