  This study develops FusionTransNet, a framework designed for
Origin-Destination (OD) flow predictions within smart and multimodal urban
transportation systems. Urban transportation complexity arises from the
spatiotemporal interactions among various traffic modes. Motivated by analyzing
multimodal data from Shenzhen, a framework that can dissect complicated
spatiotemporal interactions between these modes, from the microscopic local
level to the macroscopic city-wide perspective, is essential. The framework
contains three core components: the Intra-modal Learning Module, the
Inter-modal Learning Module, and the Prediction Decoder. The Intra-modal
Learning Module is designed to analyze spatial dependencies within individual
transportation modes, facilitating a granular understanding of single-mode
spatiotemporal dynamics. The Inter-modal Learning Module extends this analysis,
integrating data across different modes to uncover cross-modal
interdependencies, by breaking down the interactions at both local and global
scales. Finally, the Prediction Decoder synthesizes insights from the preceding
modules to generate accurate OD flow predictions, translating complex
multimodal interactions into forecasts. Empirical evaluations conducted in
metropolitan contexts, including Shenzhen and New York, demonstrate
FusionTransNet's superior predictive accuracy compared to existing
state-of-the-art methods. The implication of this study extends beyond urban
transportation, as the method for transferring information across different
spatiotemporal graphs at both local and global scales can be instrumental in
other spatial systems, such as supply chain logistics and epidemics spreading.
