← Back to all news
arXiv AI By Zibo Liu, Zhe Jiang, Zelin Xu, Tingsong Xiao, Zhengkun Xiao, Yupu zhang, Haibo Wang, Shigang Chen

Spatio-Temporal Partial Sensing Forecast for Long-term Traffic

Read the original on arXiv AI →

The paper introduces the Spatio-temporal Long-term Partial sensing Forecast model (SLPF) for predicting long-term traffic when sensors are only available at some locations. It tackles challenges such as unknown data distribution at unsensed sites, complex spatio-temporal correlations, and noise by employing a rank-based embedding, a spatial transfer matrix, and a multi-step training process. Experiments on real-world datasets show that SLPF outperforms existing methods.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

Related stories

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
benchmarkssafety
More like this →
arXiv Machine Learning
Aug 27

A Multi-View Coupled Tensor Decomposition for Lightweight Online Adaptive Traffic Prediction

The paper introduces a Multi-View Coupled Tensor Decomposition (MVCTD) model for online traffic prediction that handles imperfect multi-view data such as speed, flow, and occupancy. MVCTD builds a structured latent forecasting space by jointly modeling shared spatial structures across views and view‑specific temporal dynamics, and incorporates group sparse regularization to mitigate the impact of traffic anomalies. For streaming deployment, the method performs iterative refinement only on the current latent tensor, updating other variables with lightweight closed‑form steps based on summarized historical data, which reduces runtime while maintaining accuracy even under severe missingness.

By Quan Yu, Jie Ni, Yu-Hong Dai, Xiongjun Zhang
More like this →