arXiv AI August 17, 2026 By Xuanmian He, Can Li, Wanjing Ma

Structure-Guided Spatiotemporal Attention Graph Neural Network for Traffic Flow Prediction

arXiv:2608. 14177v1 Announce Type: cross Abstract: Deep spatiotemporal models integrating graph convolutions and attention mechanisms have demonstrated excellent performance in network-level traffic flow prediction, owing to their exceptional ability to capture complex spatiotemporal dependencies.

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

arXiv AI

Jul 29

Eliminating Propagation Delay: Attention-Based Spatial-Temporal Fusion Graph Convolution Network for Traffic Flow Prediction

arXiv:2607. 24885v1 Announce Type: cross Abstract: Predicting traffic flow is crucial to optimizing transportation systems and improving urban mobility.

By Jinpeng Chen, Ziyu Yu, Tao Wang, Jun Ma, Hongbo Gao, Senzhang Wang, Zufeng Zhang, Kaimin Wei

arXiv Machine Learning

Jul 7

Deep Learning Network-Temporal Models For Traffic Prediction

arXiv:2603. 11475v2 Announce Type: replace Abstract: Accurate prediction of multivariate time series is essential for emerging network intelligent control, observability, and management functions.

By Yufeng Xin, Ethan Fan

llms fine-tuning

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 14

SWIFT: A Small-World Interaction Framework for Flow-Aware Trajectory Prediction in Autonomous Driving

arXiv:2607. 09741v1 Announce Type: cross Abstract: Accurate trajectory prediction in autonomous driving hinges on modeling dynamic and context-dependent interactions among traffic agents.

By Chengyue Wang, Bin Rao, Haicheng Liao, Bonan Wang, Chengzhong Xu, Zhenning Li

agents robotics

arXiv AI

Jun 29

hia-gat: A Heterogeneous Interaction-Aware Graph Attention Network For Frame-Level Traffic Conflict Risk Prediction On Freeways

arXiv:2606. 27577v1 Announce Type: cross Abstract: This paper formulates frame-level freeway risk assessment as a multi-agent scene graph-level binary classification problem, where each video or trajectory frame is labeled risky if any TTC- or PET-based conflict violates a specified severity threshold.

By Mahshid Malazizi, Seyedmehdi Khaleghian, Mina Sartipi, Toru Hirano, Yunfei Xu, Hoang H. Nguyen

agents benchmarks safety

arXiv AI

Jul 22

GUIDED Network-Agnostic Feature Initialization for Spatial Transferability in GNN-based Models

arXiv:2607. 19270v1 Announce Type: cross Abstract: The Traffic Assignment Problem is a fundamental but computationally expensive component of transportation planning.

By Alessandro Scalese, Santhanakrishnan Narayanan, Constantinos Antoniou

rag fine-tuning multimodal benchmarks

Related stories

Eliminating Propagation Delay: Attention-Based Spatial-Temporal Fusion Graph Convolution Network for Traffic Flow Prediction

Deep Learning Network-Temporal Models For Traffic Prediction

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

SWIFT: A Small-World Interaction Framework for Flow-Aware Trajectory Prediction in Autonomous Driving

hia-gat: A Heterogeneous Interaction-Aware Graph Attention Network For Frame-Level Traffic Conflict Risk Prediction On Freeways

GUIDED Network-Agnostic Feature Initialization for Spatial Transferability in GNN-based Models