Reference-Based Framework for Spatio-Temporal Trajectory Compression and Query Processing

The trajectory data of vessel AIS (automatic identification system) has important theoretical and application value for information supporting decisions. However, large sizes lead to difficulties in storing, querying, and processing. To solve the problems of high compression ratio and longtime consumption of the existing online trajectory compression algorithm, an SPM (scan-pick-move) trajectory data compression algorithm added sliding window is proposed. In order to better compress vessel trajectory data regarding compression efficiency, the sliding window is added to the classical SPM algorithm. In order to reduce trajectory data storage space, the maximum offset distance reference trajectory point is used as the criterion of whether the current trajectory point can be compressed. In this paper, the multi-dimensional space-time characteristics of trajectory data, such as distance error, compression ratio and compression time, are selected to evaluate the trajectory compression method from three levels: geometric characteristics, motion characteristics and compression efficiency. Compared with the existing SPM trajectory data compression algorithm, parallel experiments are conducted based on AIS data gathered over the duration of a month in the Japan Osaka Bay. The SPM trajectory compression algorithm added sliding window can significantly reduce the compression time and outperforms other existing trajectory compression algorithms in term of average compression error at high compression strengths. Also, the proposed method has high compression efficiency in the range of commonly used compression thresholds.

Road intersection is an important part of the road network, and it plays a key role in many intelligent transportation applications. Currently, the generation of road maps required expensive field surveying and labor-intensive mapping. With more and more public vehicles equipped with positioning devices, massive trajectory data are available. These trajectory data provides a new way to generate and update urban road networks in real time. Although numerous methods are currently available to reconstruct road maps from GPS trajectories, most of these methods focus on the generation of road segments (i.e., road centerlines or road lanes). Road intersections are simply represented by nodes, the generation of road intersections with detailed structures still remains a challenge. Therefore, this paper proposes a novel method for constructing road intersections from vehicle GPS trajectories. First, the boundaries of road intersections are detected by turning angles clustering. Then, the entrances and exits of each intersection are identified based on a newly developed method. Finally, the detailed geometric structures of intersections are reconstructed based on the detected entrances and exits, and turning rules are further extracted and assigned to each intersection. The experiments on real-world trajectory data show that the proposed method can generate the detailed geometric structures of road intersections as well as the turning rule semantic information successfully.

Urban Foundation Models (UFMs) are emerging as a transformative paradigm for understanding and managing complex urban systems, offering new capabilities to improve and transform urban environments. Trajectory data (e.g., vehicle movements) contains rich spatio-temporal patterns of human mobility and urban dynamics that are crucial for understanding city-wide movement behaviors, traffic patterns, and transportation system operations. Effectively modeling such trajectory data is crucial for UFMs, yet this poses significant challenges due to the high-dimensional nature of raw trajectories and their complex spatio-temporal dynamics. Trajectory representation learning addresses these challenges by transforming raw trajectory data into compact, meaningful representations that capture essential mobility patterns and facilitates the application to various urban tasks, such as trajectory similarity computation, travel time estimation, or destination prediction. This is achieved by learning low-dimensional representations from high-dimensional and raw trajectory data. However, existing methods for trajectory representation learning either rely on grid-based or road-based representations, which are inherently different and thus, could lose information contained in the other modality. Moreover, these methods overlook the dynamic nature of urban traffic, relying on static road network features rather than time-varying traffic patterns. In this paper, we propose TIGR , a novel model designed to integrate grid and road network modalities while incorporating spatio-temporal dynamics to learn rich, general-purpose representations of trajectories. We evaluate TIGR on three real-world datasets and demonstrate the effectiveness of combining both modalities by substantially outperforming state-of-the-art methods, i.e., up to 59.34% for trajectory similarity, up to 16.65% for travel time estimation, and up to 14.81% for destination prediction.

With the rapid development of location aware computing technologies, the research of data mining for spatial-temporal trajectories has attracted many scholars. As an important data source, through the GPS receivers taxis provide researchers with massive trajectory data, which are featured by their high quality, good continuity and wide distribution, making it suitable for travel pattern mining. In this paper, we study on urban residents’ trip attractive areas with trajectory data of about 10000 taxis in Chongqing city, to discover the characteristics of urban traffic distribution to serve for the construction of modern intelligent traffic system. Firstly, a framework of trajectory preprocessing, including data cleaning, extraction for the taxi passenger pick-up points and drop-off points, is presented to reduce the noise and redundancy in raw trajectory data. Secondly, in the light of the shortcomings of the existing mining methods for attractive areas, such as high complexity and poor scalability, we propose a grid density based clustering algorithm to mine the trip attractive areas in different periods. In the experiments, we take a performance comparison between our algorithm and DBSCAN algorithm, and discuss how to determine the optimal parameters of our algorithm. Then, we verify the practicability of our algorithm with real data.

The popularity of mobile terminals has given rise to an extremely large number of trajectories of moving objects. As a result, it is critical to provide effective and efficient query operations on large-scale trajectory data for further retrieval and analysis. Considering data partition has a great influence on processing large-scale data, we present a time-based partitioning technique on trajectory data. This partitioning technique can be applied on the distributed framework to improve the performance of range queries on massive trajectory data. Furthermore, the proposed method adopts time-based hash strategy to ensure both the partition balancing and less partitioning time. Especially, existing trajectory data are not required to be repartitioned when new data arrive. Extensive experiments on three real data sets demonstrated that the performance of the proposed technique outperformed other partitioning techniques.

With the increasing development of GPS‐equipped mobile devices such as smart phones and vehicle navigation systems, the trajectories containing valuable spatiotemporal information are recorded. Typically, plenty of trajectory records are generated and stored, making the device memory suffer a heavy storage pressure. Thus, it is a vital issue to compress the trajectories. The trajectory semantics are usually ignored or reduced in traditional trajectory compression techniques. In addition, most of existing trajectory compression algorithms only concern the position errors rather than the velocity errors of trajectories. This paper proposes a velocity‐preserving trajectory compression algorithm based on retrace point detection (VPTC‐RP) that can compress a set of trajectories by removing unnecessary redundancy points, while the skeleton of these trajectories is maintained as much as possible. In VPTC‐RP, the retrace points and the velocity errors are taken to reflect the speeds and directions attached with the points. VPTC‐RP first determines the retrace points based on the changed movement directions, and then, the retrace points are extracted from the original trajectories. Especially, the retrace points are put in a buffer, and the subtrajectories in the buffer are compressed according to the measured velocity errors. Simulations are carried out on the Geolife trajectory dataset, and the simulation results indicate that VPTC‐RP can achieve a preferable tradeoff among the compression error, compression ratio, and running time.

In a cloud environment, resources should be acquired quickly and automatically released at runtime. Traditional trajectory data partitions, indexing and query processing techniques are extended, so that they can take advantage of the cloud of large clusters of highly parallel processing capabilities. There are ways with trajectory data in the cloud database query processing. The advanced sensing techniques available today caused the existence of many types of trajectory datasets whose study can form a strong basis for decision making regarding a data context. Trajectory data generally includes trajectory classification, trajectory clustering, and trajectory associations and so on. All these tools need trajectory similarity measures to get comparisons in trajectory data. Some of the popular existing methods of trajectory similarity include Euclidean distance, semantic distance and there near variants. Each variant of these techniques exhibits notable differences in measurement of similarity and computational difficulty. This paper targeted to deal with this situation with the goal of lower computational efforts in trajectory similarity computations. A two‐phase trajectory similarity measure is defined. The first phase does point level analysis of trajectory points representing all trajectories and the following phase use the analysis to find the similarities. Using a hybrid similarity framework, new means for trajectory clustering and trajectory query processing are developed. From trajectory analysis followed by trajectory clustering, evaluation of cluster quality and processing of trajectory query are undertaken in this paper in different ways from the existing methods. All the proposed developments are tested with trajectory datasets with multiple attributes and the results are more favour to proposed framework than the existing ones.

Query processing is the most common operation in a DBMS. Sophisticated query processing has been mainly targeted at a single enterprise environment providing centralized control over data and metadata. Submitting queries by anonymous users on the Web is different in such a way that load balancing or DBMS access control becomes the key issue. This paper provides a solution by introducing a framework for WWW query processing. The success of this framework lies in the utilization of query optimization techniques and the ontological approach. This methodology has proved to be cost effective at the NASA Goddard Space Flight Center Distributed Active Archive Center (GDAAC).

Mining moving object gathering pattern based on Resilient Distributed Datasets and R-tree index

Massive trajectory data with spatio-temporal information are generated from various devices everyday. Due to a wide variety of devices and data collection methods, these data differ in terms of frequency and precision, which brings challenges to data management and analysis. This paper presents TrajMan, a cloud-assisted framework for trajectory data management and analysis, which adds an edge layer between clients and cloud servers. With the assistance of edge devices, TrajMan supports fast data acquisition of extensive trajectory data, and reduces the WAN bandwidth and computation overhead for cloud servers by calibrating and compressing data locally. Cloud servers focus on data interpolation based on trajectory similarity to handle data in various frequencies, deriving more accurate trajectory features. To demonstrate the feasibility and performance of TrajMan, a preliminary case study of commute behavior analysis is conducted using real dataset, which shows 88% of the calculated commute distance has the relative deviation less than 20% and 80% of the calculated commute time deviates less than 10 minutes.

The widespread use of locatable devices leads to a sharp increase in the storage of trajectory data, and redundant storage of similar trajectories wastes a large amount of storage resources. The state-of-the-art multiple trajectory compression algorithms are developed to strip the partial information of trajectory; however, these algorithms have low compression efficiency because they do not eliminate the redundancy within a single trajectory as much as possible, as well as high time overhead due to matching of reference sub-trajectories. In this study, we propose a new spatio-temporal trajectory compression technique, consisting of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">intra-trajectory error balancing</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">inter-trajectory feature point clustering</i>. Intra-trajectory error balancing is achieved through retaining high score (an aggregated metric) trajectory points (i.e. feature points). Furthermore, inter-trajectory feature point clustering realizes the fusion of similar trajectories and extracts the commonality between trajectories. Experiments are performed on five real trajectory datasets, including two road datasets, one airline dataset, and one walking dataset. Compared with the state-of-the-art methods, our compression technique improves the compression ratio by an average of 24.9% under the same error, and reduces the time overhead by at least an order of magnitude.

Modern navigation systems and logistics services rely heavily on digital road networks, which can be accurately constructed from crowd-sourced GPS trajectories. Yet, most existing trajectory-based road network generators project GPS coordinates to 2D grid, followed by semantic segmentation to obtain road heatmap, and further processed to road network graph via heuristic processing. The data through this pipeline involve multiple cross-modal heuristic conversions, which cause serious information loss and harm overall robustness. In this paper, we propose a novel end-to-end learnable deep learning approach, named GraphWalker, to generate accurate road network graphs directly from raw trajectories with minimal information loss. In particular, GraphWalker is a latent diffusion model (LDM) consisting of two key components: the <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Trajectory-to-Walks Diffusion Transformer</b> (T2W-DiT) generates <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">walks</i> with trajectories as conditions, and the <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Walks-to-Graph Variational Auto-Encoder</b> (W2G-VAE) reconstructs road network graphs from walks. GraphWalker keeps the data in geographical space throughout the pipeline to retain all information within raw trajectories. Additionally, the end-to-end learnable design removes the heuristic processing and enhances the robustness and generalizability. Extensive experiments on multiple datasets demonstrate that the proposed GraphWalker can effectively generate high-quality road networks from noisy and sparse trajectories, showcasing significant improvements of up to 55.43% over state-of-the-art.

Spatial analytical queries on road networks typically perform hundreds of thousands to several millions of shortest distance computations in the process of producing results. These queries require architectures that can compute a large number of network distances. Two architectures are evaluated on a variety of spatial analytical queries on road networks. The first architecture is a widely used hybrid architecture that uses a database to store spatial datasets, a road network distance computing module, and an analysis tool to tie them together into a single query processing pipeline. The second architecture uses of a distance oracle representation of a road network. This architecture stores the spatial datasets and the distance oracle inside the database, and the query processing is completely handled by the database. A detailed evaluation of the two architectures for a variety of analytical query processing tasks such as region, KNN, distance matrix and trajectory queries is presented and the lessons learned are discussed.

Predictive spatio-temporal queries are crucial in many applications. Traffic management is an example application, where predictive spatial queries are issued to anticipate jammed areas in advance. Also, location-aware advertising is another example application that targets customers expected to be in the vicinity of a shopping mall in the near future. In this paper, we introduce Panda?, a generic framework for supporting spatial predictive queries over moving objects in Euclidean spaces. Panda? distinguishes itself from previous work in spatial predictive query processing by the following features: (1) Panda? is generic in terms of supporting commonly-used types of queries, (e.g., predictive range, KNN, aggregate queries) over stationary points of interests as well as moving objects. (2) Panda? employees a prediction function that provides accurate prediction even under the absence or the scarcity of the objects' historical trajectories. (3) Panda? is customizable in the sense that it isolates the prediction calculation from query processing. Hence, it enables the injection and integration of user defined prediction functions within its query processing framework. (4) Panda? deals with uncertainties and variabilities in the expected travel time from source to destination in response to incomplete information and/or dynamic changes in the underlying Euclidean space. (5) Panda? provides a controllable parameter that trades low latency responses for computational resources. Experimental analysis proves the scalability of Panda? in evaluating a massive volume of predictive queries over large numbers of moving objects.

&lt;p&gt;Data annotation is the categorization and labelling of data for applications, such as machine learning, artificial intelligence, and data integration. The categorization and labelling are done to achieve a specific use case in relation to solving problems. Existing data annotation systems and modules face imperfections such as knowledge and annotation not being formally integrated, narrow application range, and difficulty to apply on existing database management applications. To analyze and process annotated data, obtain the relationship between different annotations, and capture metainformation in data provenance and probabilistic databases, in this paper, we design a back-end query processing framework as a supplementary interface for the database management system to extend operation to datasets and boost efficiency. The framework utilizes Java language and the MVC model for development to achieve lightweight, cross-platform, and high adaptability identities. The contribution of this paper is mainly reflected in two aspects. The first contribution is to implement query processing, provenance semiring, and semiring homomorphism over annotated data. The second contribution is to combine query processing and provenance with SQL statements in order to enable the database manager to invoke operations to annotation.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;