Multimodal Passenger Flow Forecasting at Hub Airports

Multi-modal Passenger Flow Forecasting at Hub Airports aims to predict the short-term origin-destination (OD) flow of outbound passengers from airports to various urban regions and its distribution across multiple transport modes. Accurate prediction is essential for efficient airport operations and for maintaining the stability of surrounding transportation systems. While recent deep learning approaches have shown potential in OD forecasting tasks, most focus on single-mode flow and neglect heterogeneous data sources, limiting their ability to model passenger flow and modal allocation in hub airports. To address these limitations, we propose M2F-Net, a Multi-Source and Multi-Modal Flow Forecasting Network that integrates a cross-modal encoder with a Universal Opportunity Model (UOM)–based decoder. Within the M2F-Net, a time-aware allocation matrix jointly models the spatiotemporal and modal flow distribution, while the encoder learns short-term temporal patterns and regional traffic states from historical OD data and road speeds. The decoder generates a dynamic travel-probability matrix to mask the encoder output, mitigating dynamic sparsity and guiding flow prediction. We construct a comprehensive benchmark based on real-world datasets collected from Beijing Capital International Airport and the Beijing Municipal Commission of Transport (June–August 2023), including total outbound flow, multi-modal OD flow, and roadnetwork speed. Experiments on this real-world dataset show that M2F-Net consistently outperforms strong baselines, and ablation results confirm the benefits of multi-source integration and our architectural design.