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

• URL: http://arxiv.org/abs/2609.08268v1
• Date: Tue, 08 Sep 2026 05:22:58 GMT
• Title: Synergistic Fusion of Topological Structure and Temporal Semantics of Mobility for Urban Region Embedding
• Abstract summary: Urban region embeddings have shown promising results in diverse urban sensing tasks such as crime, income, and service-call prediction.<n>We propose Mobility Stream-Structure Synergy (MoSS), which derives complementary views from mobility data.<n>MoSS achieves state-of-the-art performance across three downstream tasks using mobility data alone, outperforming baselines that rely on auxiliary modalities.
• Score: 5.609514491713238
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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