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arXiv AI

Learning Higher-Order Interactions from Incomplete Spatiotemporal Data with Mixtures of Hypergraph Laplacians

The paper introduces MoHL, a mixture of hypergraph Laplacians that learns group-wise relational structure directly from incomplete spatiotemporal traffic sensor data. MoHL discovers overlapping sensor groups, constructs a Laplacian component for each interaction order, and learns their mixture through holdouts that mimic missingness patterns, sharing weights across group-induced couplings to pool evidence. Experiments on three traffic networks show that MoHL achieves the lowest error under day-long gaps, with far fewer learned parameters than deep imputers, demonstrating the benefit of higher-order groups and shared weights when relational evidence is scarce.

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STHMoE: Hypergraph-Enhanced Heterogeneous Dependency Coordination for LLM-Based Urban Traffic Data Forecasting

STHMoE is a Spatio‑Temporal Hypergraph‑Enhanced Mixture of Experts framework designed for urban traffic forecasting. It separates traffic dynamics into frequency‑, time‑, spatial‑, and higher‑order representations, each handled by a prompt‑guided expert built on a partially frozen large language model. The higher‑order expert uses an adaptive hypergraph module to learn evolving spatial structures, while an entropy‑aware router balances expert usage and fuses outputs, achieving competitive results on ten real‑world traffic benchmarks.

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Learning Local Heterogeneity and Cross-Region Context for Large-Scale Traffic Forecasting

The paper introduces LoReST, a Local-Region Spatial Temporal network designed for large-scale traffic forecasting. LoReST captures local spatial heterogeneity by relation-aware aggregation within node neighborhoods and incorporates cross-region context through mean pooling, inter-region attention, and broadcasting back to nodes. Experiments on the LargeST benchmark demonstrate significant improvements, reducing MAE, RMSE, and MAPE by 4.78%, 3.60%, and 5.75% respectively.

By Qi Feng, Zidong Wang, Bo Li, Xiaoguang Gao, Jiayu Zhang, Chenfeng Wang, Kaifang Wan
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arXiv AI
Sep 10

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

The paper introduces Mobility Stream-Structure Synergy (MoSS), a method that fuses two complementary views of mobility data—an hourly inflow/outflow Sequence view and a Structure view derived from zigzag persistence diagrams—to capture temporal dynamics and evolving regional connectivity. MoSS employs a synergy module that extracts higher‑order representations from the co‑occurrence of these views, moving beyond additive fusion. Experiments on New York City and Chicago demonstrate that MoSS outperforms existing baselines on three downstream tasks using only mobility data.

By Namwoo Kim, Jeeyun Chang, Kanghoon Lee, Yoonjin Yoon
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arXiv AI
Sep 18

FedeRICo: Federated Region-Influenced Coupling for Traffic Flow Prediction

FedeRICo is a federated traffic forecasting framework that addresses heterogeneity across client sensor subgraphs by combining gradient-level collaboration with boundary-aware residual communication. It uses a dual-branch architecture: a globally guided branch for transferable forecasting structure and a private residual branch that preserves client-specific corrections and incorporates boundary residual signals. Experiments on four real-world traffic benchmarks show that FedeRICo outperforms state‑of‑the‑art federated spatial‑temporal baselines while keeping training runtime competitive.

By Fermin Orozco, Man Luo, Johan Wahlstr\"om
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