arXiv AI June 10, 2026 By Jichao Li, Xuanming Shi

PatchSTG: Scalable Spatiotemporal Graph Transformers for Traffic Forecasting on Irregular Sensor Networks

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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Aug 6

Spatiotemporal Graph Transformer for Traffic Intelligence in Edge Computing

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.

By Laha Ale, Letian Lin, Na Cao, Zheng Ma, Peng Yu

llms

arXiv AI

Jul 9

STAGformer: A Spatio-temporal Agent Graph Transformer for Micro Mobility Demand Forecasting

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

llms agents benchmarks

arXiv AI

Jun 9

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.

By Yongchao Li, Yang Li, Zhuoxuan Li, Jun Chen, Chu Zhang, Jinde Cao, Leszek Rutkowski

benchmarks

arXiv AI

Jul 16

STKAN: Kolmogorov-Arnold Networks for Spatio-Temporal Forecasting

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

benchmarks

arXiv AI

Aug 3

SqLinear: Balanced Square Partitioning Makes Linear Interaction Sufficient for Large-Scale Traffic Forecasting

arXiv:2606. 21072v2 Announce Type: replace-cross Abstract: Traffic prediction is a core task in intelligent transportation systems and urban-scale decision making.

By Yongfeng Su, Hongwen Li, Zijian Zhang, Ziquan Fang, Lu Chen, Christian S. Jensen, Hong Gao, Yinjun Han

llms

arXiv Machine Learning

Jun 30

HieraMix: A Hierarchical MLP-Mixer for Large-Scale Traffic Forecasting

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

benchmarks

Related stories

Spatiotemporal Graph Transformer for Traffic Intelligence in Edge Computing

STAGformer: A Spatio-temporal Agent Graph Transformer for Micro Mobility Demand Forecasting

DSFNet: Learning Dual-Domain Spectral Operators for Multi-Modality Spatio-Temporal Forecasting in Urban Transportation Systems

STKAN: Kolmogorov-Arnold Networks for Spatio-Temporal Forecasting

SqLinear: Balanced Square Partitioning Makes Linear Interaction Sufficient for Large-Scale Traffic Forecasting

HieraMix: A Hierarchical MLP-Mixer for Large-Scale Traffic Forecasting