arXiv:2607. 12462v2 Announce Type: replace Abstract: Existing traffic forecasting models commonly focus on extracting spatial dependencies, particularly global spatial information, which characterizes the representations obtained through interactions between each node and all nodes across the traffic network.
By Qihang Zhang, Siyao Zhang, Letao Kang, Wenzhe Liang, Miao Zhang, Zhao Zhang
arXiv:2606. 09872v1 Announce Type: cross Abstract: Traffic forecasting is a fundamental component of intelligent transportation systems, yet remains challenging in real-world settings due to irregular sensor distributions and the high computational cost of modeling large-scale spatiotemporal dependencies.
By Jichao Li, Xuanming Shi
arXiv:2607. 13108v1 Announce Type: cross Abstract: Real-world traffic data exhibit heterogeneous spatial correlations and nonlinear temporal dynamics, posing substantial challenges for accurate spatio-temporal forecasting.
By Sicong Lai, Yuehong Hu, Siru Zhong, Si Qiao, Yuxuan Liang, Guangyin Jin
arXiv:2606. 07695v1 Announce Type: cross Abstract: Multi-Modality Spatio-Temporal Forecasting (MoSTF) extends traditional spatio-temporal forecasting by incorporating diverse traffic modalities.
By Yongchao Li, Yang Li, Zhuoxuan Li, Jun Chen, Chu Zhang, Jinde Cao, Leszek Rutkowski
arXiv:2607. 06614v1 Announce Type: cross Abstract: Accurate station-level demand forecasting is essential for the efficient operation of bike-sharing systems, yet it remains challenging due to complex spatio-temporal dependencies and the large scale of urban networks.
By Ye Zihao
arXiv:2512. 07854v2 Announce Type: replace Abstract: Traffic forecasting task is significant to modern urban management.
By Yongyao Wang, Xie Yu, Jingyuan Wang, Jiahao Ji, Chao Li