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

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-temporal relationships.

STGFormer: Spatio-Temporal Graph Transformer for Traffic Flow Prediction in Sparse-Sensing Scenarios

An improved Transformer prediction model that integrates a graph convolutional network (GCN) and a self-attention mechanism is proposed for traffic flow prediction, combining temporal self-attention and learnable temporal encoding to capture both long-term traffic evolution patterns and sudden fluctuations.

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.

Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for 5G Traffic Forecasting

LSTE-OATD: Direction-Driven Online Anomalous Trajectory Detection Using Learnable Spatio–Temporal Embeddings

Cross-city few-shot spatiotemporal graph forecasting via masked pre-training and prompt tuning.

Spatiotemporal Graph (STG) forecasting holds great significance in the field of urban computing. However, the challenge of data scarcity poses significant obstacles to this task. While cross-city few-shot learning offers a promising solution, existing methods face two fundamental challenges: 1) insufficient extraction of meta-knowledge from data-rich source cities, and 2) limited generality of the knowledge transfer mechanism. In this paper, we propose a novel STG few-shot learning framework named ST-MPPT, which addresses both challenges through masked pre-training and prompt tuning. In the pre-training stage, we perform spatiotemporal-decoupled masked pre-training on source cities with abundant data, enabling the model to learn long-term spatiotemporal patterns more comprehensively. In the downstream forecasting stage, we leverage the pre-trained encoders to acquire robust spatial and temporal representations. These representations are then used to construct a graph structure and enhance the downstream spatiotemporal predictor. To achieve a more general knowledge transfer, we introduce a novel prompt network. Instead of rigid pattern retrieval, this network dynamically generates input-specific prompts to steer the pre-trained encoders to adapt to different data distributions across diverse cities. Extensive experiments on four real-world spatiotemporal datasets demonstrate the superiority of ST-MPPT over strong and representative baselines.