学习跨模式迁移：面向统一的城市交通预测

中文摘要 AI 辅助

城市交通系统包含同一城市内共存的多种交通模式，且表现出复杂的相互依赖关系，导致不同模式间的需求动态具有相关性。然而，由于空间层面存在显著异质性，且新兴模式的历史数据有限，联合预测不同模式的需求仍具有挑战性。现有预测方法大多针对单一交通模式开发，且隐含假设源系统与目标系统之间的空间结构兼容，这严重限制了其在多模式场景下的适用性。为应对这些挑战，我们提出TransMod，这是一个用于城市交通需求预测的统一框架，可实现跨异构交通模式的有效知识迁移。TransMod构建了一个共享的区域级空间表示，将具有不同空间粒度的交通系统对齐到同一空间，从而减少结构不匹配和分布偏移。基于该统一表示，TransMod进一步从数据丰富的源模式中学习可迁移的时空模式，并将其适配到数据稀缺的目标模式，缓解了对大量目标域历史数据的依赖。在真实世界数据集上的大量实验表明，TransMod始终优于现有方法，且在目标数据有限的情况下能提供稳健的预测性能。

英文摘要

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 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.

发表机构

• University of Exeter（埃克塞特大学）

机构由 AI 辅助整理，请以论文原文为准。