Trajectory representation learning transforms complex spatio-temporal features of trajectories into dense, low-dimensional embeddings, enabling applications in intelligent transportation systems. With advances in this field and the availability of large-scale traffic data, intelligent urban systems have been widely deployed in major cities. However, existing methods heavily rely on large volumes of trajectory data, limiting their transferability to cities with sparse data, especially small or less-developed ones. Moreover, most current approaches learn representations within a single city, overlooking the shared travel patterns across regions and cities with similar geographic contexts. To address these issues, we propose MetaTRL, a self-supervised cross-city trajectory representation learning method based on meta-learning. Specifically, we introduce a Shared and Private Parameterized Cross-city Meta-learning Framework to support knowledge sharing and transfer across cities. We further design a Meta-knowledge Enhanced Road Segment Encoder and a Trajectory Encoder that integrates private and shared knowledge to learn and fuse spatio-temporal trajectory features. Extensive experiments on two real-world datasets and multiple downstream tasks demonstrate the significant superiority of MetaTRL over state-of-the-art baselines and achieves a remarkable average improvement of 134.66% in Macro-F1 on destination prediction task.

With the widespread use of location-tracking technologies, large volumes of trajectory data are continuously generated. Trajectory similarity computation is a core task in trajectory mining with broad applications. However, existing methods still face two key challenges: (1) the difficulty of balancing efficiency and representation quality, and (2) the reliance on a single training paradigm, which limits the ability to capture both pairwise similarity and batch-level coherence. To address these challenges, we propose a trajectory similarity computation framework named TrajAgg. Specifically, our framework incorporates a novel Aggregation Transformer that efficiently aggregates GPS and grid features through two stages of direct interaction and enhances the expressiveness of the resulting trajectory embeddings. In addition, by integrating two distinct training paradigms, our model captures both fine-grained pairwise relationships and global structural consistency. We further analyze its effectiveness from the perspective of mutual information. Extensive experiments on three publicly available datasets show that TrajAgg consistently outperforms state-of-the-art baselines. Our method achieves average improvements of 15.11%, 16.49%, 10.41%, and 40.15% in HR@1 under four distance measures across three datasets, respectively.

With the rise of social media and Location-Based Social Networks (LBSN), check-in data across platforms has become crucial for User Identity Linkage (UIL). These data not only reveal users' spatio-temporal information but also provide insights into their behavior patterns and interests. However, cross-platform identity linkage faces challenges like poor data quality, high sparsity, and noise interference, which hinder existing methods from extracting cross-platform user information. To address these issues, we propose a Correlation-Attention Masked Transformer for User Identity Link age Network (MT-Link), a transformer-based framework to enhance model performance by learning spatio-temporal co-occurrence patterns of cross-platform users. Our model effectively captures spatio-temporal co-occurrence in cross-platform user check-in sequences. It employs a correlation attention mechanism to detect the spatio-temporal co-occurrence between user check-in sequences. Guided by attention weight maps, the model focuses on co-occurrence points while filtering out noise, ultimately improving classification performance. Experimental results show that our model significantly outperforms state-of-the-art baselines by 12.92%-17.76% and 5.80%-8.38% improvements in terms of Macro-F1 and Area Under Curve (AUC).

Trajectory-user linking (TUL) aims to match anonymous trajectories to the most likely users who generated them, offering benefits for a wide range of real-world spatio-temporal applications. However, existing TUL methods are limited by high model complexity and poor learning of the effective representations of trajectories, rendering them ineffective in handling large-scale user trajectory data.In this work, we propose a novel Scalable Trajectory-User Linking with dual-stream representation networks for large-scale TUL problem, named ScaleTUL Specifically, ScaleTUL generates two views using temporal and spatial augmentations to exploit supervised contrastive learning framework to effectively capture the irregularities of trajectories. In each view, a dual-stream trajectory encoder consisting of a long-term encoder and a short-term encoder is designed to learn the unified representations of trajectories that fuses different temporal-spatial dependencies. Then, a TUL layer is used to associate the trajectories with the corresponding users in the representation space using a two-stage training model.Experimental results on check-in mobility datasets from three real-world cities and the nationwide U.S. demonstrate the superiority of ScaleTUL over state-of-the-art baselines for large-scale TUL tasks.

Sea surface temperature (SST) serves as a crucial indicator for balancing marine energy levels and assessing the thermal content of ocean waters. The diverse effects prompted by oceanic temperature fields significantly impact human activities on a large scale. Efficient and accurate prediction of SST is of great significance. However, due to the complex spatial and temporal relationships between sea areas, how to model the spatial information of sea areas is a challenging problem. In this article, we propose an adaptive spatiotemporal graph recurrent network (ASTGRN) model to capture the hidden spatial corrections between sea areas for SST forecasting. First, we propose an adaptive node representation learning (ANRL) module to capture the trend of temperature changes in different sea areas. Second, we use a graph structure learning (GSL) module to learn the dynamic spatial corrections between different sea areas without predefined graph adjacency matrices. Finally, the prediction results are obtained through a spatiotemporal recurrent network. Experimental evaluations of the selected data from the South China Sea (SCS), Bohai Sea, and the East China Sea (ESC) demonstrate the superiority of the proposed ASTGRN compared with state-of-the-art baselines in SST forecasting.