Synergistic Fusion of Topological Structure and Temporal Semantics of Mobility for Urban Region Embedding

2026-09-08

AI Summary

该研究通过提出MoSS方法，结合移动数据的时间动态和拓扑结构，解决了城市区域嵌入中未充分利用时间动态的问题。

Abstract

Urban region embeddings have shown promising results in diverse urban sensing tasks such as crime, income, and service-call prediction. Recent methods improve representation quality by integrating mobility data with auxiliary modalities, using cross-view attention or contrastive objectives to align heterogeneous features into a unified region representation. However, leveraging the temporal dynamics of human mobility remains under-explored. Regional inflow and outflow fluctuate throughout the day, and inter-region connections emerge, persist, and dissolve over time. Moreover, prevailing fusion strategies combine views additively and miss the joint signal that emerges only when views co-occur. To address these gaps, we propose Mobility Stream-Structure Synergy (MoSS), which derives complementary views from mobility data: a Sequence view that preserves each region's hourly inflow/outflow profile, and a Structure view based on zigzag persistence diagrams that capture how regional connectivity emerges, persists, and dissolves over time. A synergy module then extracts emergent representations from the co-occurrence of these views through multi-degree interactions, explicitly capturing higher-order signal across views. Extensive experiments on New York City and Chicago show that MoSS achieves state-of-the-art performance across three downstream tasks using mobility data alone, outperforming baselines that rely on auxiliary modalities.

Problem

• Urban Region Embedding
• Temporal Dynamics
• Human Mobility
• Cross-View Attention
• Contrastive Objectives

Innovation

• Mobility Stream-Structure Synergy
• Sequence view
• Structure view
• synergy module
• multi-degree interactions