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.
By Zibo Liu, Zhe Jiang, Zelin Xu, Tingsong Xiao, Zhengkun Xiao, Yupu zhang, Haibo Wang, Shigang Chen
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...
By Yue Wang, Areg Karapetyan, Djellel Difallah, Samer Madanat
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.
By Weijiang Xiong, Robert Fonod, Nikolas Geroliminis
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
The paper introduces DF-LLM, a Dynamic Fusion Large Language Model designed for traffic flow prediction. It combines a spatiotemporal embedding module, a fusion module that uses graph convolution to capture spatial topology and dynamic dependencies, and an LLM backbone with differentiated parameter adaptation and context aggregation attention. Experiments on four datasets demonstrate that DF-LLM outperforms existing methods in predictive accuracy.
By Xue Qiu, Jianli Xiao
arXiv:2507. 02961v2 Announce Type: replace-cross Abstract: Modern transportation network modeling increasingly involves the integration of diverse methodologies including sensor-based forecasting, reinforcement learning, classical flow optimization, and demand modeling that have traditionally been developed in isolation.
By Xuesong Zhou, Taehooie Kim, Mostafa Ameli, Henan Zhu, Yudai Honma, Ram M. Pendyala