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Conference Open access Sep 2026

ASTPKEFormer: Adaptive Spatiotemporal Prior Knowledge Embedding-Induced Transformers for Traffic Data Forecasting

ASTPKEformer is proposed, a prior knowledge-guided Trans-former framework for traffic prediction that outperforms state-of-the-art (SOTA) baselines, validating its effectiveness and enhancing the representation capability.

Wenfeng Zhou, Xiao-Yun Xia, Xiang-Jie Kong et al. · 0 citations
Open access Jul 2026

ULSTM: Multi-Scale and Full-Level Temporal Consistency for Traffic Anomaly Detection

Experimental results demonstrate that the ULSTM framework significantly outperforms frame-independent generative models by suppressing high-frequency reconstruction noise, providing a robust solution for real-world smart city deployments.

Borja Pérez, Mario Resino, Jaime Godoy et al. · 0 citations
Open access Aug 2026

Federated spatio-temporal graph neural network for privacy-preserving vehicle trajectory prediction in autonomous driving

A Federated Spatio-Temporal Synchronous Dynamic Graph Neural Network (Federated STSDGNN) framework for privacy-preserving and adaptive trajectory prediction, which achieves approximately 23.5% lower RMSE compared to the centralized STSDGNN baseline in the conducted experiments, and has the potential to support future p...

Aditi Joshi, Amrutha P, Vaidehi Prajapati et al. · 0 citations
Preprint Aug 2026

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

The Structure-Guided Spatiotemporal Attention Graph Neural Network is proposed, offering a mechanistic account of the model's decision-making process while ensuring robust forecasting by aligning attention-based reasoning with identified macroscopic dependencies and preventing over-reliance on ephemeral local noise.

Xuan He, Can Li, Wan-Jing Ma · 0 citations
Open access Jul 2026

A Hybrid Spatiotemporal Framework with Memory and Diffusion Convolution for Traffic Flow Prediction

A novel spatiotemporal forecasting framework, termed Memory-augmented Diffusion Convolutional LSTM network (MDC-LSTM), which integrates a MemBART-inspired memory mechanism, diffusion convolution, regional attention, and LSTM-based temporal modeling and effectively captures both localized spatial patterns and deep inter...

Xi Chen, Jiajia Chen · 0 citations
Aug 2026

Physics-Informed Spatiotemporal Disentangling for Unsupervised Anomaly Detection in Earth Observation Sensing Nodes

Long-term health monitoring of unattended sensing nodes is essential for remote Earth observation networks (EONs); yet, it remains challenging because anomaly-induced response drifts often resemble genuine geophysical variations in both spectral and morphological characteristics. This ambiguity makes sensor degradation...

Luoyao Chen, Fang Ye, Jinhui Cai · 0 citations

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