Adaptive dynamic graph learning for forecasting urban multimodal flow

The increasing diversity and integration of transportation modes is changing urban mobility, resulting in complex spatiotemporal urban flow patterns. The current forecasting models, which typically rely on static or manually defined graph structures, are inadequate for capturing the dynamic spatial heterogeneity and complex cross-modal interactions that are present in real urban systems. To address these limitations, this study introduces a multimodal dynamic graph neural network (MM-DyGNN), a novel deep learning model that is designed for urban multimodal flow prediction. MM-DyGNN introduces three key innovations: (i) a time-varying multimodal graph learning module based on Tucker decomposition that adaptively constructs mode- and time-specific diffusion graphs; (ii) a sparse cross-modal interaction module that employs a top-k strategy to capture the most relevant region–mode dependencies; and (iii) an adaptive multitask learning strategy with uncertainty weighting to balance heterogeneous modal objectives. Comprehensive experiments conducted on real-world urban mobility datasets demonstrate that the MM-DyGNN significantly outperforms the baseline models in terms of forecasting accuracy. Ablation studies further validate the effectiveness of each component and demonstrate the ability of the model to interpret dynamic spatiotemporal dependencies and cross-modal interactions. This work provides a methodological foundation for understanding and managing the evolving complexities of urban mobility.