TransMod解决了多模态交通预测难题，能在数据不足时从其他交通模式迁移知识，提升预测准确性。
TransMod框架通过构建共享区域级空间表示，将不同空间粒度的交通系统对齐到共同空间。该模型能从数据丰富的源模式中学习可迁移的时空模式，并适应数据稀缺的目标模式。在真实世界数据集上的实验表明，TransMod在有限目标数据下表现优于现有方法。
Learning to Transfer Across Modes: Towards Unified Urban Mobility Forecasting
Urban transportation systems consist of multiple mobility modes that coexist within the same city and exhibit complex interdependencies, leading to correlated demand dynamics across modes. However, forecasting demand jointly across different modes remains challenging due to substantial heterogeneity in space and the limited availability of historical data for emerging modes. Existing forecasting methods are largely developed for individual mobility modes and implicitly assume compatible spatial structures between source and target systems, which severely restricts their applicability in multi-modal settings. To address these challenges, we propose \textbf{TransMod}, a unified framework for urban mobility demand forecasting that enables effective knowledge transfer across heterogeneous mobility modes. TransMod constructs a shared zone-level spatial representation that aligns mobility systems with different spatial granularities into a common space, thereby reducing structural mismatch and distributional shift. Built on this unified representation, TransMod further learns transferable spatio-temporal patterns from data-rich source modes and adapts them to data-scarce target modes, alleviating the dependence on extensive target-domain histories. Extensive experiments on real-world datasets demonstrate that TransMod consistently outperforms existing approaches and provides robust forecasting performance under limited target data.