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.

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.

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

Sep 15

STHMoE: Hypergraph-Enhanced Heterogeneous Dependency Coordination for LLM-Based Urban Traffic Data Forecasting

STHMoE is a Spatio‑Temporal Hypergraph‑Enhanced Mixture of Experts framework designed for urban traffic forecasting. It separates traffic dynamics into frequency‑, time‑, spatial‑, and higher‑order representations, each handled by a prompt‑guided expert built on a partially frozen large language model. The higher‑order expert uses an adaptive hypergraph module to learn evolving spatial structures, while an entropy‑aware router balances expert usage and fuses outputs, achieving competitive results on ten real‑world traffic benchmarks.

By Jiawen Chen, Qi Shao, Yongjian Chang, Mingtong Zhou, Duxin Chen, Wenwu Yu

llms benchmarks

arXiv AI

3d ago

UniST-Pred: A Robust Unified Framework for Spatio-Temporal Traffic Forecasting in Transportation Networks Under Disruptions

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

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 Machine Learning

Sep 1

ButterMamba: Butterworth-Enhanced Spatial-Temporal Mamba for Efficient Traffic Flow Prediction

arXiv:2608.29658v1 Announce Type: cross Abstract: Accurate traffic flow prediction is fundamental to intelligent transportation systems, playing a pivotal role in urban mobility optimization and smar...

By Limiao Zhang, Yuhui Lu, Jie Gao, Hao Jiang, Haiping Ma, Xingyi Zhang

benchmarks

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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

STHMoE: Hypergraph-Enhanced Heterogeneous Dependency Coordination for LLM-Based Urban Traffic Data Forecasting

UniST-Pred: A Robust Unified Framework for Spatio-Temporal Traffic Forecasting in Transportation Networks Under Disruptions

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

ButterMamba: Butterworth-Enhanced Spatial-Temporal Mamba for Efficient Traffic Flow Prediction