arXiv Machine Learning
A Multi-Resolution Multi-Domain Pre-Training Framework for Universal Traffic Forecasting
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DSFNet: Learning Dual-Domain Spectral Operators for Multi-Modality Spatio-Temporal Forecasting in Urban Transportation Systems
arXiv:2606. 07695v1 Announce Type: cross Abstract: Multi-Modality Spatio-Temporal Forecasting (MoSTF) extends traditional spatio-temporal forecasting by incorporating diverse traffic modalities.
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UniST-Pred: A Robust Unified Framework for Spatio-Temporal Traffic Forecasting in Transportation Networks Under Disruptions
arXiv:2602.14049v2 Announce Type: replace-cross Abstract: Spatio-temporal traffic forecasting is a core component of intelligent transportation systems, supporting various downstream tasks such as si...
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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.
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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.
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Learning to Transfer Across Modes: Towards Unified Urban Mobility Forecasting
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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.