arXiv:2606. 09392v1 Announce Type: new Abstract: Efficient acquisition, storage, and utilization of traffic data are critical challenges in spatio-temporal data management.
arXiv:2606. 21072v2 Announce Type: replace-cross Abstract: Traffic prediction is a core task in intelligent transportation systems and urban-scale decision making.
arXiv:2609.13878v1 Announce Type: new Abstract: Spatio-temporal traffic data are central to intelligent transportation systems, yet their heterogeneity poses significant challenges for large-scale mo...
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...
arXiv:2510. 03381v3 Announce Type: replace-cross Abstract: Interchanges are crucial nodes for vehicle transfers between highways, yet the lack of real-time ramp detectors creates blind spots in traffic prediction.
arXiv:2605. 18793v2 Announce Type: replace-cross Abstract: Accurate spatiotemporal pattern analysis is critical in fields such as urban traffic, meteorology, and public health monitoring.
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.
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.
arXiv:2608. 04075v1 Announce Type: cross Abstract: Accurate traffic forecasting is essential for proactive resource management in edge computing, where service demand evolves dynamically across both space and time.
arXiv:2310. 10196v3 Announce Type: replace-cross Abstract: Temporal data, including time series and spatio-temporal data, are pervasive in real-world applications.
arXiv:2512. 07854v2 Announce Type: replace Abstract: Traffic forecasting task is significant to modern urban management.
arXiv:2604. 16084v2 Announce Type: replace-cross Abstract: Traffic forecasting is a challenging spatio-temporal modeling task and a critical component of urban transportation management.
arXiv:2606. 07695v1 Announce Type: cross Abstract: Multi-Modality Spatio-Temporal Forecasting (MoSTF) extends traditional spatio-temporal forecasting by incorporating diverse traffic modalities.