Trajectory representation learning aims to transform raw trajectory data into compact and low-dimensional vectors that are suitable for downstream analysis. However, most existing methods adopt either a free-space view or a road-network view during the learning process, which limits their ability to capture the complex, multi-view spatiotemporal features inherent in trajectory data. Moreover, these approaches rely on task-specific model training, restricting their generalizability and effectiveness for diverse analysis tasks. To this end, we propose GTR, a general, multi-view, and dynamic Trajectory Representation framework built on a pre-train and fine-tune architecture. Specifically, GTR introduces a multi-view encoder that captures the intrinsic multi-view spatiotemporal features. Based on the pre-train and fine-tune architecture, we provide the spatio-temporal fusion pre-training with a spatio-temporal mixture of experts to dynamically combine spatial and temporal features, enabling seamless adaptation to diverse trajectory analysis tasks. Furthermore, we propose an online frozen-hot updating strategy to efficiently update the representation model, accommodating the dynamic nature of trajectory data. Extensive experiments on two real-world datasets demonstrate that GTR consistently outperforms 15 state-of-the-art methods across 6 mainstream trajectory analysis tasks. All source code and data are available at https://github.com/ZJU-DAILY/GTR.

Trajectory similarity computation in road networks is crucial for data analytics. However, both non-learning-based and learning-based methods face challenges. First, they suffer from low accuracy due to manual parameter selection for model training and the omission of key spatio-temporal features in road networks. Second, they have low efficiency, stemming from the high time complexity of similarity computation and the time-consuming training process. Third, learning-based methods struggle with poor model generality due to the small size of available training samples. To address these challenges, we propose an effective and efficient trajectory similarity learning framework for road networks, called SimRN. To our knowledge, SimRN is the first deep reinforcement learning (DRL) approach for trajectory similarity computation. Specifically, SimRN consists of three key modules: the spatio-temporal prompt information extraction (STP) module, the trajectory representation based on DRL (TrajRL) module, and the graph contrastive learning (GCL) module. The STP module captures spatio-temporal features from road networks to improve the training of the trajectory representation. The TrajRL module automatically selects optimal parameters and enables parallel training, improving both trajectory representation and the efficiency of similarity computations. The GCL module employs a self-supervised contrastive learning paradigm to generate sufficient samples while preserving spatial constraints and temporal dependencies of trajectories. Extensive experiments on two real-world datasets, compared with three state-of-the-art methods, show that SimRN: (i) improves accuracy by 20%–40%, (ii) achieves speedups of 2–4x, and (iii) demonstrates strong generality, enabling effective similarity learning with very small sample sizes.

With the widespread use of GPS-enabled devices and services, trajectory data fuels services in a variety of fields, such as transportation and smart cities. However, trajectory data often contains errors stemming from inaccurate GPS measurements, low sampling rates, and transmission interruptions, yielding low-quality trajectory data with negative effects on downstream services. Therefore, a crucial yet tedious endeavor is to assess the quality of trajectory data, serving as a guide for subsequent data cleaning and analyses. Despite some studies addressing general-purpose data quality assessment, no studies exist that are tailored specifically for trajectory data. To more effectively diagnose the quality of trajectory data, we propose T-Assess, an automated trajectory data quality assessment system. T-Assess is built on three fundamental principles: i) extensive coverage, ii) versatility, and iii) efficiency. To achieve comprehensive coverage, we propose assessment criteria spanning validity, completeness, consistency, and fairness. To provide high versatility, T-Assess supports both offline and online evaluations for full-batch trajectory datasets as well as real-time trajectory streams. In addition, we incorporate an evaluation optimization strategy to achieve assessment efficiency. Extensive experiments on four real-life benchmark datasets offer insight into the effectiveness of T-Assess at quantifying trajectory data quality beyond the capabilities of state-of-the-art data quality systems.

Transportation mode classification, the process of predicting the class labels of moving objects’ transportation modes, has been widely applied to a variety of real-world applications, such as traffic management, urban computing, and behavior study. However, existing studies of transportation mode classification typically extract the explicit features of trajectory data but fail to capture the implicit features that affect the classification performance. In addition, most of the existing studies also prefer to apply RNN-based models to embed trajectories, which is only suitable for classifying small-scale data. To tackle the above challenges, we propose an effective and scalable framework for transportation mode classification over GPS trajectories, abbreviated Estimator. Estimator is established on a developed CNN-TCN architecture, which is capable of leveraging the spatial and temporal hidden features of trajectories to achieve high effectiveness and efficiency. Estimator partitions the entire traffic space into disjointed spatial regions according to traffic conditions, which enhances the scalability significantly and thus enables parallel transportation classification. Extensive experiments using eight public real-life datasets offer evidence that Estimator i) achieves superior model effectiveness (i.e., 99% Accuracy and 0.98 F1-score), which outperforms state-of-the-arts substantially; ii) exhibits prominent model efficiency, and obtains 7–40x speedups up over state-of-the-arts learning-based methods; and iii) shows high model scalability and robustness that enables large-scale classification analytics.

Spatio-temporal trajectory analytics are useful in diversified applications such as urban planning, infrastructure development, and vehicular networks. Trajectory similarity measure, which aims to evaluate the distance between two trajectories, is a fundamental functionality of trajectory analytics. In this paper, we propose a comprehensive survey that investigates all the most common and representative spatio-temporal trajectory measures. First, we provide an overview of spatio-temporal trajectory measures in terms of three hierarchical perspectives: Non-learning versus Learning, Free Space versus Road Network, and Standalone versus Distributed. Next, we present an evaluation benchmark by designing five real-world transformation scenarios. Based on this benchmark, extensive experiments are conducted to study the effectiveness, robustness, efficiency, and scalability of each measure, which offers guidelines for trajectory measure selection among multiple techniques and applications such as trajectory data mining, deep learning, and distributed processing. Specifically, i) Effectiveness: In terms of trajectory length, DFD and Seg-Frechet are length-sensitive, while OWD and Hausdorff always return same results when varying query trajectory length. In terms of trajectory shape, LCRS and LORS are able to effectively find similar trajectories for query trajectories with different shapes; ii) Robustness: Learning based measures are more robust compared with non-learning based ones. Among non-learning based measures, DFD, Hausdorff, OWD and Seg-Frechet are relatively non-sensitive to noises and different sampling rates; and iii) Efficiency& Scalability: Compared to non-learning based measures, learning based and distributed based measures are more efficient and scalable.

The ubiquitous GPS sensors collect massive trajectory data from moving objects, which is useful in data mining applications. However, trajectory data is enormous in volume, and thus, directly storing and processing the raw data is expensive. Using trajectory simplification, a trajectory can be reduced to a set of continuous line segments with acceptable data loss, which is an efficient method. Although many algorithms are proposed, they still suffer from the following issues including (i) non-data driven capability as most studies rely on human-crafted rules or pre-defined parameters, (ii) bound with error measures that yield high computational cost, and (iii) focusing only on the local information preservation in trajectories, but failing in capturing the global mobility patterns for trajectory compression.To address the above issues, we propose a Seq2Seq2Seq framework, abbreviated S3, which consists of two chained Seq2Seq. With differentiable reconstruction learning, S3 enables self-supervised trajectory simplification in a lightweight manner. Besides, we deploy S3 over the graph neural architecture to capture the context-aware mobility patterns and enhance the representation paradigm of trajectories with geographical semantics, where a context-aware distance measure is designed for quality evaluation. An online extension of S3 is also developed to enable streaming trajectory simplifications. Finally, extensive experiments using two real-world datasets in both offline and online scenarios show that S3 achieves much higher efficiency (e.g., it achieves up to one order of magnitude speed-up gains) and comparable compression quality, compared with both non-learning and state-of-the-art learning-based methods.

Deep learning based trajectory similarity computation holds the potential for improved efficiency and adaptability over traditional similarity computation. However, existing learning-based trajectory similarity learning solutions prioritize spatial similarity over temporal similarity, making them suboptimal for time-aware analyses. To this end, we propose ST2Vec, a representation learning based solution that considers fine-grained spatial and temporal relations between trajectories to enable spatio-temporal similarity computation in road networks. Specifically, ST2Vec encompasses two steps: (i) spatial and temporal modeling that encode spatial and temporal information of trajectories, where a generic temporal modeling module is proposed for the first time; and (ii) spatio-temporal co-attention fusion, where two fusion strategies are designed to enable the generation of unified spatio-temporal embeddings of trajectories. Further, under the guidance of triplet loss, ST2Vec employs curriculum learning in model optimization to improve convergence and effectiveness. An experimental study offers evidence that ST2Vec outperforms state-of-the-art competitors substantially in terms of effectiveness and efficiency, while showing low parameter sensitivity and good model robustness. Moreover, similarity involved case studies including top-k querying and DBSCAN clustering offer further insight into the capabilities of ST2Vec.