UniTE: A Survey and Unified Pipeline for Pre-training ST Trajectory Embeddings

Spatio-temporal (ST) trajectories are sequences of timestamped locations, which enable a variety of analyses that in turn enable important real-world applications. It is common to map trajectories to vectors, called embeddings, before subsequent analyses. Thus, the qualities of embeddings are very important. Methods for pre-training embeddings, which leverage unlabeled trajectories for training universal embeddings, have shown promising applicability across different tasks, thus attracting considerable interest. However, research progress on this topic faces two key challenges: a lack of a comprehensive overview of existing methods, resulting in several related methods not being well-recognized, and the absence of a unified pipeline, complicating the development new methods and the analysis of methods. To overcome these obstacles and advance the field of pre-training of trajectory embeddings, we present UniTE, a survey and a unified pipeline for this domain. In doing so, we present a comprehensive list of existing methods for pre-training trajectory embeddings, which includes methods that either explicitly or implicitly employ pre-training techniques. Further, we present a unified and modular pipeline with publicly available underlying code, simplifying the process of constructing and evaluating methods for pre-training trajectory embeddings. Additionally, we contribute a selection of experimental results using the proposed pipeline on real-world datasets.

STD-LLM: Understanding Both Spatial and Temporal Properties of Spatial-Temporal Data with LLMs

Spatial-temporal forecasting and imputation are important for real-world dynamic systems such as intelligent transportation, urban planning, and public health. Most existing methods are tailored for individual forecasting or imputation tasks but are not designed for both. Additionally, they are less effective for zero-shot and few-shot learning. While large language models (LLMs) have exhibited strong pattern recognition and reasoning abilities across various tasks, including few-shot and zero-shot learning, their development in understanding spatial-temporal data has been constrained by insufficient modeling of complex correlations such as the temporal correlations, spatial connectivity, non-pairwise and high-order spatial-temporal correlations within data. In this paper, we propose STD-LLM for understanding both spatial and temporal properties of \underline{S}patial-\underline{T}emporal \underline{D}ata with \underline{LLM}s, which is capable of implementing both spatial-temporal forecasting and imputation tasks. STD-LLM understands spatial-temporal correlations via explicitly designed spatial and temporal tokenizers as well as virtual nodes. Topology-aware node embeddings are designed for LLMs to comprehend and exploit the topology structure of data. Additionally, to capture the non-pairwise and higher-order correlations, we design a hypergraph learning module for LLMs, which can enhance the overall performance and improve efficiency. Extensive experiments demonstrate that STD-LLM exhibits strong performance and generalization capabilities across the forecasting and imputation tasks on various datasets. Moreover, STD-LLM achieves promising results on both few-shot and zero-shot learning tasks.

Combining Optimal Transport and Embedding-Based Approaches for More Expressiveness in Unsupervised Graph Alignment

Unsupervised graph alignment finds the one-to-one node correspondence between a pair of attributed graphs by only exploiting graph structure and node features. One category of existing works first computes the node representation and then matches nodes with close embeddings, which is intuitive but lacks a clear objective tailored for graph alignment in the unsupervised setting. The other category reduces the problem to optimal transport (OT) via Gromov-Wasserstein (GW) learning with a well-defined objective but leaves a large room for exploring the design of transport cost. We propose a principled approach to combine their advantages motivated by theoretical analysis of model expressiveness. By noticing the limitation of discriminative power in separating matched and unmatched node pairs, we improve the cost design of GW learning with feature transformation, which enables feature interaction across dimensions. Besides, we propose a simple yet effective embedding-based heuristic inspired by the Weisfeiler-Lehman test and add its prior knowledge to OT for more expressiveness when handling non-Euclidean data. Moreover, we are the first to guarantee the one-to-one matching constraint by reducing the problem to maximum weight matching. The algorithm design effectively combines our OT and embedding-based predictions via stacking, an ensemble learning strategy. We propose a model framework named \texttt{CombAlign} integrating all the above modules to refine node alignment progressively. Through extensive experiments, we demonstrate significant improvements in alignment accuracy compared to state-of-the-art approaches and validate the effectiveness of the proposed modules.

PLM4Traj: Cognizing Movement Patterns and Travel Purposes from Trajectories with Pre-trained Language Models

Spatio-temporal trajectories play a vital role in various spatio-temporal data mining tasks. Developing a versatile trajectory learning approach that can adapt to different tasks while ensuring high accuracy is crucial. This requires effectively extracting movement patterns and travel purposes embedded in trajectories. However, this task is challenging due to limitations in the size and quality of available trajectory datasets. On the other hand, pre-trained language models (PLMs) have shown great success in adapting to different tasks by training on large-scale, high-quality corpus datasets. Given the similarities between trajectories and sentences, there is potential in leveraging PLMs to enhance the development of a versatile and effective trajectory learning method. Nevertheless, vanilla PLMs are not tailored to handle the unique spatio-temporal features present in trajectories and lack the capability to extract movement patterns and travel purposes from them. To overcome these obstacles, we propose a model called PLM4Traj that effectively utilizes PLMs to model trajectories. PLM4Traj leverages the strengths of PLMs to create a versatile trajectory learning approach while addressing the limitations of vanilla PLMs in modeling trajectories. Firstly, PLM4Traj incorporates a novel trajectory semantic embedder that enables PLMs to process spatio-temporal features in trajectories and extract movement patterns and travel purposes from them. Secondly, PLM4Traj introduces a novel trajectory prompt that integrates movement patterns and travel purposes into PLMs, while also allowing the model to adapt to various tasks. Extensive experiments conducted on two real-world datasets and two representative tasks demonstrate that PLM4Traj successfully achieves its design goals. Codes are available at https://github.com/Zeru19/PLM4Traj.

Micro-Macro Spatial-Temporal Graph-based Encoder-Decoder for Map-Constrained Trajectory Recovery

Recovering intermediate missing GPS points in a sparse trajectory, while adhering to the constraints of the road network, could offer deep insights into users' moving behaviors in intelligent transportation systems. Although recent studies have demonstrated the advantages of achieving map-constrained trajectory recovery via an end-to-end manner, they still face two significant challenges. Firstly, existing methods are mostly sequence-based models. It is extremely hard for them to comprehensively capture the micro-semantics of individual trajectory, including the information of each GPS point and the movement between two GPS points. Secondly, existing approaches ignore the impact of the macro-semantics, i.e., the road conditions and the people's shared travel preferences reflected by a group of trajectories. To address the above challenges, we propose a Micro-Macro Spatial-Temporal Graph-based Encoder-Decoder (MM-STGED). Specifically, we model each trajectory as a graph to efficiently describe the micro-semantics of trajectory and design a novel message-passing mechanism to learn trajectory representations. Additionally, we extract the macro-semantics of trajectories and further incorporate them into a well-designed graph-based decoder to guide trajectory recovery. Extensive experiments conducted on sparse trajectories with three different sampling intervals that are respectively constructed from two real-world trajectory datasets demonstrate the superiority of our proposed model.

Diff-RNTraj: A Structure-aware Diffusion Model for Road Network-constrained Trajectory Generation

Trajectory data is essential for various applications as it records the movement of vehicles. However, publicly available trajectory datasets remain limited in scale due to privacy concerns, which hinders the development of trajectory data mining and trajectory-based applications. To address this issue, some methods for generating synthetic trajectories have been proposed to expand the scale of the dataset. However, all existing methods generate trajectories in the geographical coordinate system, which poses two limitations for their utilization in practical applications: 1) the inability to ensure that the generated trajectories are constrained on the road. 2) the lack of road-related information. In this paper, we propose a new problem to meet the practical application need, \emph{i.e.}, road network-constrained trajectory (RNTraj) generation, which can directly generate trajectories on the road network with road-related information. RNTraj is a hybrid type of data, in which each point is represented by a discrete road segment and a continuous moving rate. To generate RNTraj, we design a diffusion model called Diff-RNTraj. This model can effectively handle the hybrid RNTraj using a continuous diffusion framework by incorporating a pre-training strategy to embed hybrid RNTraj into continuous representations. During the sampling stage, a RNTraj decoder is designed to map the continuous representation generated by the diffusion model back to the hybrid RNTraj format. Furthermore, Diff-RNTraj introduces a novel loss function to enhance the spatial validity of the generated trajectories. Extensive experiments conducted on two real-world trajectory datasets demonstrate the effectiveness of the proposed model.

GenSTL: General Sparse Trajectory Learning via Auto-regressive Generation of Feature Domains

Trajectories are sequences of timestamped location samples. In sparse trajectories, the locations are sampled infrequently; and while such trajectories are prevalent in real-world settings, they are challenging to use to enable high-quality transportation-related applications. Current methodologies either assume densely sampled and accurately map-matched trajectories, or they rely on two-stage schemes, yielding sub-optimal applications. To extend the utility of sparse trajectories, we propose a novel sparse trajectory learning framework, GenSTL. The framework is pre-trained to form connections between sparse trajectories and dense counterparts using auto-regressive generation of feature domains. GenSTL can subsequently be applied directly in downstream tasks, or it can be fine-tuned first. This way, GenSTL eliminates the reliance on the availability of large-scale dense and map-matched trajectory data. The inclusion of a well-crafted feature domain encoding layer and a hierarchical masked trajectory encoder enhances GenSTL's learning capabilities and adaptability. Experiments on two real-world trajectory datasets offer insight into the framework's ability to contend with sparse trajectories with different sampling intervals and its versatility across different downstream tasks, thus offering evidence of its practicality in real-world applications.

Time-Series Contrastive Learning against False Negatives and Class Imbalance

As an exemplary self-supervised approach for representation learning, time-series contrastive learning has exhibited remarkable advancements in contemporary research. While recent contrastive learning strategies have focused on how to construct appropriate positives and negatives, in this study, we conduct theoretical analysis and find they have overlooked the fundamental issues: false negatives and class imbalance inherent in the InfoNCE loss-based framework. Therefore, we introduce a straightforward modification grounded in the SimCLR framework, universally adaptable to models engaged in the instance discrimination task. By constructing instance graphs to facilitate interactive learning among instances, we emulate supervised contrastive learning via the multiple-instances discrimination task, mitigating the harmful impact of false negatives. Moreover, leveraging the graph structure and few-labeled data, we perform semi-supervised consistency classification and enhance the representative ability of minority classes. We compared our method with the most popular time-series contrastive learning methods on four real-world time-series datasets and demonstrated our significant advantages in overall performance.

Building Category Graphs Representation with Spatial and Temporal Attention for Visual Navigation

Given an object of interest, visual navigation aims to reach the object's location based on a sequence of partial observations. To this end, an agent needs to 1) learn a piece of certain knowledge about the relations of object categories in the world during training and 2) look for the target object based on the pre-learned object category relations and its moving trajectory in the current unseen environment. In this paper, we propose a Category Relation Graph (CRG) to learn the knowledge of object category layout relations and a Temporal-Spatial-Region (TSR) attention architecture to perceive the long-term spatial-temporal dependencies of objects helping the navigation. We learn prior knowledge of object layout, establishing a category relationship graph to deduce the positions of specific objects. Subsequently, we introduced TSR to capture the relationships of objects in temporal, spatial, and regions within the observation trajectories. Specifically, we propose a Temporal attention module (T) to model the temporal structure of the observation sequence, which implicitly encodes the historical moving or trajectory information. Then, a Spatial attention module (S) is used to uncover the spatial context of the current observation objects based on the category relation graph and past observations. Last, a Region attention module (R) shifts the attention to the target-relevant region. Based on the visual representation extracted by our method, the agent can better perceive the environment and easily learn superior navigation policy. Experiments on AI2-THOR demonstrate our CRG-TSR method significantly outperforms existing methods regarding both effectiveness and efficiency. The code has been included in the supplementary material and will be publicly available.

Local-Global History-aware Contrastive Learning for Temporal Knowledge Graph Reasoning

Temporal knowledge graphs (TKGs) have been identified as a promising approach to represent the dynamics of facts along the timeline. The extrapolation of TKG is to predict unknowable facts happening in the future, holding significant practical value across diverse fields. Most extrapolation studies in TKGs focus on modeling global historical fact repeating and cyclic patterns, as well as local historical adjacent fact evolution patterns, showing promising performance in predicting future unknown facts. Yet, existing methods still face two major challenges: (1) They usually neglect the importance of historical information in KG snapshots related to the queries when encoding the local and global historical information; (2) They exhibit weak anti-noise capabilities, which hinders their performance when the inputs are contaminated with noise.To this end, we propose a novel \blue{Lo}cal-\blue{g}lobal history-aware \blue{C}ontrastive \blue{L}earning model (\blue{LogCL}) for TKG reasoning, which adopts contrastive learning to better guide the fusion of local and global historical information and enhance the ability to resist interference. Specifically, for the first challenge, LogCL proposes an entity-aware attention mechanism applied to the local and global historical facts encoder, which captures the key historical information related to queries. For the latter issue, LogCL designs four historical query contrast patterns, effectively improving the robustness of the model. The experimental results on four benchmark datasets demonstrate that LogCL delivers better and more robust performance than the state-of-the-art baselines.