Compressed Trajectories Research Articles

A Novel Context-Aware Douglas–Peucker (CADP) Trajectory Compression Method

Most traditional trajectory compression methods, such as the Douglas–Peucker (DP) method, consider only spatial characteristics and disregard contextual factors, including environmental context. This paper proposes a new way of trajectory formulation by considering all spatial, internal, environmental, and semantic contexts to capture all contextual aspects of moving objects. Then, we propose the Context-Aware Douglas–Peucker (CADP) method for trajectory compression. These facts are confirmed by experiments with real AIS data showing that, while CADP preserves the same computational efficiency of DP (i.e., at O(n2)), it outperforms DP and two-stage Context-Aware Piecewise Linear Segmentation (two-stage CPLS) methods in preserving agent movement behavior, obtaining compressed trajectories that are closer to the original ones and that are much more useful in base analyses such as trajectory prediction. Specifically, the LSTM-based models trained on CADP-compressed trajectories have relatively lower RMSEs than others compressed by either DP or two-stage CPLS. Therefore, CADP is more scalable and efficient, thus making it more practical for large-scale engineering applications; with the improvement in trajectory analysis accuracy achieved by the suggested method, a wide range of critical engineering applications can be potentially improved, such as collision avoidance and route planning. Future work will focus on spatial auto-correlation and uncertainty to extend the robustness and applicability of the approach.

A pattern accumulated compression method for trajectories constrained by urban road networks

In an edge computing framework, a moving object in an urban road network can collect both its own and others’ trajectories to answer spatial–temporal queries. How to store the ever increasing amount of trajectories with limited storage space is a great challenge. We observe that the traces of moving objects in urban road networks usually exhibit great repetition in terms of spatial and velocity distributions. So we propose two strategies, Pattern Accumulated Compression based on Average Velocity (PAC-AV) and Pattern Accumulated Compression based on Representative Velocity (PAC-RV), to compress trajectories online on the mobile devices. The former mainly exploits the path pattern to compress trajectories while the latter exploits both the path pattern and the velocity pattern to compress trajectories. Both PAC-AV and PAC-RV compress the trajectories by two stages. First, we extract the spatial as well as velocity components according to a velocity deviation threshold. Second, we compress the components into a series of identifiers leveraging a Path Pattern Dictionary (PPD) and a Velocity Pattern Dictionaries (VPD). Our method not only greatly cuts down the storage space but also directly supports the velocity-related queries on the compressed trajectories. Experimental results demonstrate the effectiveness and efficiency of our method.

Development of denoising and compression algorithms for AIS-based vessel trajectories

Ship navigation information derived from the Automatic Identification System (AIS) is widely used in the shipping industry. As AIS reports are featured with huge volume and noises, preprocessing of AIS data is essential before its further application. This study aims to develop effective algorithms to denoise and compress raw trajectories derived from AIS reports. Specifically, an effective noise detection method based on statistical theory and sliding window is first proposed to identify glitches in a given trajectory. Linear interpolation is further used to rectify the glitches detected. Then, two modes of algorithms are proposed for trajectory compression: static mode with preset threshold for compression and dynamic mode considering the distance between trajectory points and the coastline in a real-time manner. Numerical experiments show that the noise detection and rectification algorithms and the trajectory compression algorithms are accurate and highly efficient considering compression rate, information loss, and computation time. The main innovation of this research includes developing an accurate and robust trajectory glitch detection and rectification algorithm, proposing two modes of trajectory compression algorithms, and combining the two tasks in a holistic robust framework. Especially, the dynamic compression mode can overcome a major problem encountered in static compression where the compressed trajectories may go across land. It can also deal with more flexible compression requirements and thus should be more applicable in practice.

Efficient trajectory compression and range query processing

Nowadays, there are ubiquitousness of GPS sensors in various devices collecting, transmitting and storing tremendous trajectory data. However, such an unprecedented scale of GPS data has posed an urgent demand for not only an effective storage mechanism but also an efficient query mechanism. Line simplification in online mode, searving as a mainstream trajectory compression method, plays an important role to attack this issue. But for the existing algorithms, either their time cost is extremely high, or the accuracy loss after the compression is completely unacceptable. To attack this issue, we propose $\epsilon \_$Region based Online trajectory Compression with Error bounded (ROCE for short), which makes the best balance among the accuracy loss, the time cost and the compression rate. The range query serves as a primitive, yet quite essential operation on analyzing trajectories. Each trajectory is usually seen as a sequence of discrete points, and in most previous work, a trajectory is judged to be overlapped with the query region R iff there is at least one point in this trajectory falling in R. But this traditional criteria is not suitable when the queried trajectories are compressed, because there may be hundreds of points discarded between each two adjacent points and the points in each compressed trajectory are quite sparse. And many trajectories could be missing in the result set. To address this, in this paper, a new criteria based on the probability and an efficient Range Query processing algorithm on Compressed trajectories RQC are proposed. In addition, an efficient index \emph{ASP\_tree} and lots of novel techniques are also presented to accelerate the processing of trajectory compression and range queries obviously. Extensive experiments have been done on multiple real datasets, and the results demonstrate superior performance of our methods.

TRACE

The deployment of vehicle location services generates increasingly massive vehicle trajectory data, which incurs high storage and transmission costs. A range of studies target offline compression to reduce the storage cost. However, to enable online services such as real-time traffic monitoring, it is attractive to also reduce transmission costs by being able to compress streaming trajectories in real-time. Hence, we propose a framework called TRACE that enables compression, transmission, and querying of network-constrained streaming trajectories in a fully online fashion. We propose a compact two-stage representation of streaming trajectories: a speed-based representation removes redundant information, and a multiple-references based referential representation exploits subtrajectory similarities. In addition, the online referential representation is extended with reference selection, deletion and rewriting functions that further improve the compression performance. An efficient data transmission scheme is provided for achieving low transmission overhead. Finally, indexing and filtering techniques support efficient real-time range queries over compressed trajectories. Extensive experiments with real-life and synthetic datasets evaluate the different parts of TRACE, offering evidence that it is able to outperform the existing representative methods in terms of both compression ratio and transmission cost.

Trajectory Data Compression Algorithm Based on Motion State Changing

The trajectory information generated by the moving object plays an important role in studying the object movement. In this paper, a trajectory data compression algorithm based on the motion state changing is proposed to reduce trajectory data storage space and increase compression speed, which can accurately show the motion state and trajectory characteristics. This study has certain significance for the exploration of mass traffic data and the planning of traffic network. Combining the angle threshold with the velocity threshold of a moving object, the key data points are found and the redundant information is removed. Subsequently, the compressed trajectory is obtained. The experimental results show that the new algorithm can help to improve compression efficiency. The compressed trajectory has high similarity with the original trajectory in movement tendency.

A Contextual Hybrid Model for Vessel Movement Prediction

Predicting the movement of the vessels can significantly improve the management of safety. While the movement can be a function of geographic contexts, the current systems and methods rarely incorporate contextual information into the analysis. This paper initially proposes a novel context-aware trajectories' simplification method to embed the effects of geographic context which guarantees the logical consistency of the compressed trajectories, and further suggests a hybrid method that is built upon a curvilinear model and deep neural networks. The proposed method employs contextual information to check the logical consistency of the curvilinear method and then, constructs a Context-aware Long Short-Term Memory (CLSTM) network that can take into account contextual variables, such as the vessel types. The proposed method can enhance the prediction accuracy while maintaining the logical consistency, through a recursive feedback loop. The implementations of the proposed approach on the Automatic Identification System (AIS) dataset, from the eastern coast of the United States of America which was collected, from November to December 2017, demonstrates the effectiveness and better compression, i.e. 80% compression ratio while maintaining the logical consistency. The estimated compressed trajectories are 23% more similar to their original trajectories compared to currently used simplification methods. Furthermore, the overall accuracy of the implemented hybrid method is 15.68% higher than the ordinary Long Short-Term Memory (LSTM) network which is currently used by various maritime systems and applications, including collision avoidance, vessel route planning, and anomaly detection systems.

An index for moving objects with constant-time access to their compressed trajectories

ABSTRACT As the number of vehicles and devices equipped with GPS technology has grown explosively, an urgent need has arisen for time- and space-efficient data structures to represent their trajectories. The most commonly desired queries are the following: queries about an object’s trajectory, range queries, and nearest neighbor queries. In this paper, we consider that the objects can move freely and we present a new compressed data structure for storing their trajectories, based on a combination of logs and snapshots, with the logs storing sequences of the objects’ relative movements and the snapshots storing their absolute positions sampled at regular time intervals. We call our data structure ContaCT because it provides Constant- time access to Compressed Trajectories. Its logs are based on a compact partial-sums data structure that returns cumulative displacement in constant time, and allows us to compute in constant time any object’s position at any instant, enabling a speedup when processing several other queries. We have compared ContaCT experimentally with another compact data structure for trajectories, called GraCT, and with a classic spatio-temporal index, the MVR-tree. Our results show that ContaCT outperforms the MVR-tree by orders of magnitude in space and also outperforms the compressed representation in time performance.

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

The pervasiveness of GPS-enabled devices and wireless communication technologies results in massive trajectory data, incurring expensive cost for storage, transmission, and query processing. To relieve this problem, in this paper we propose a novel framework for compressing trajectory data, REST ( Re ference-based S patio- t emporal trajectory compression), by which a raw trajectory is represented by concatenation of a series of historical (sub-)trajectories (called reference trajectories) that form the compressed trajectory within a given spatio-temporal deviation threshold. In order to construct a reference trajectory set that can most benefit the subsequent compression, we propose three kinds of techniques to select reference trajectories wisely from a large dataset such that the resulting reference set is more compact yet covering most footprints of trajectories in the area of interest. To address the computational issue caused by the large number of combinations of reference trajectories that may exist for resembling a given trajectory, we propose efficient greedy algorithms that run in the blink of an eye and dynamic programming algorithms that can achieve the optimal compression ratio. Compared to existing work on trajectory compression, our framework has few assumptions about data such as moving within a road network or moving with constant direction and speed, and better compression performance with fairly small spatio-temporal loss. In addition, by indexing the reference trajectories directly with an in-memory R-tree and building connections to the raw trajectories with inverted index, we develop an extremely efficient algorithm that can answer spatio-temporal range queries over trajectories in their compressed form. Extensive experiments on a real taxi trajectory dataset demonstrate the superiority of our framework over existing representative approaches in terms of both compression ratio and efficiency.

PPQ-trajectory

We present PPQ-trajectory, a spatio-temporal quantization based solution for querying large dynamic trajectory data. PPQ-trajectory includes a partition-wise predictive quantizer (PPQ) that generates an error-bounded codebook with autocorrelation and spatial proximity-based partitions. The codebook is indexed to run approximate and exact spatio-temporal queries over compressed trajectories. PPQ-trajectory includes a coordinate quadtree coding for the codebook with support for exact queries. An incremental temporal partition-based index is utilised to avoid full reconstruction of trajectories during queries. An extensive set of experimental results for spatio-temporal queries on real trajectory datasets is presented. PPQ-trajectory shows significant improvements over the alternatives with respect to several performance measures, including the accuracy of results when the summary is used directly to provide approximate query results, the spatial deviation with which spatio-temporal path queries can be answered when the summary is used as an index, and the time taken to construct the summary. Superior results on the quality of the summary and the compression ratio are also demonstrated.

A Turning Contour Maintaining Method of Trajectory Data Compression

As the dramatical improvement of Global Position System (GPS) sensors in recent years, the research of trajectory dataset has become a hotspot in the field of geographic information systems (GIS). Pretreatment is very important for extracting useful information from massive trajectory dataset. One of key problems is trajectory data compression. Aiming at preserving crucial feature of trajectories while compressing, this paper puts forward a method for compressing trajectory data that combines distance, angle, and velocity while maintaining the contour of the trajectory, especially the turning corners and U-turns in the compressed trajectory and the original trajectory. So, the compressed trajectory can be very useful for trajectory mining and road network update. The new method also shows applicability and stability through experiments on different datasets.

Age influences domestic dog cognitive performance independent of average breed lifespan

Across mammals, increased body size is positively associated with lifespan. However, within species, this relationship is inverted. This is well illustrated in dogs (Canis familiaris), where larger dogs exhibit accelerated life trajectories: growing faster and dying younger than smaller dogs. Similarly, some age-associated traits (e.g., growth rate and physiological pace of aging) exhibit accelerated trajectories in larger breeds. Yet, it is unknown whether cognitive performance also demonstrates an accelerated life course trajectory in larger dogs. Here, we measured cognitive development and aging in a cross-sectional study of over 4000 dogs from 66 breeds using nine memory and decision-making tasks performed by citizen scientists as part of the Dognition project. Specifically, we tested whether cognitive traits follow a compressed (accelerated) trajectory in larger dogs, or the same trajectory for all breeds, which would result in limited cognitive decline in larger breeds. We found that all breeds, regardless of size or lifespan, tended to follow the same quadratic trajectory of cognitive aging-with a period of cognitive development in early life and decline in later life. Taken together, our results suggest that cognitive performance follows similar age-related trajectories across dog breeds, despite remarkable variation in developmental rates and lifespan.

Storage Efficient Trajectory Clustering and k-NN for Robust Privacy Preserving Spatio-Temporal Databases

The need to store massive volumes of spatio-temporal data has become a difficult task as GPS capabilities and wireless communication technologies have become prevalent to modern mobile devices. As a result, massive trajectory data are produced, incurring expensive costs for storage, transmission, as well as query processing. A number of algorithms for compressing trajectory data have been proposed in order to overcome these difficulties. These algorithms try to reduce the size of trajectory data, while preserving the quality of the information. In the context of this research work, we focus on both the privacy preservation and storage problem of spatio-temporal databases. To alleviate this issue, we propose an efficient framework for trajectories representation, entitled DUST (DUal-based Spatio-temporal Trajectory), by which a raw trajectory is split into a number of linear sub-trajectories which are subjected to dual transformation that formulates the representatives of each linear component of initial trajectory; thus, the compressed trajectory achieves compression ratio equal to M : 1 . To our knowledge, we are the first to study and address k-NN queries on nonlinear moving object trajectories that are represented in dual dimensional space. Additionally, the proposed approach is expected to reinforce the privacy protection of such data. Specifically, even in case that an intruder has access to the dual points of trajectory data and try to reproduce the native points that fit a specific component of the initial trajectory, the identity of the mobile object will remain secure with high probability. In this way, the privacy of the k-anonymity method is reinforced. Through experiments on real spatial datasets, we evaluate the robustness of the new approach and compare it with the one studied in our previous work.

A Novel Representation and Compression for Queries on Trajectories in Road Networks

Recording and querying time-stamped trajectories incurs high cost of data storage and computing. In this paper, we explore several characteristics of the trajectories in road networks, which have motivated the idea of coding trajectories by associating timestamps with relative spatial path and locations. Such a representation contains a large number of duplicate information to achieve a lower entropy compared with the existing representations, thereby drastically cutting the storage cost. We propose several techniques to compress spatial path and locations separately, which can support fast positioning and achieve better compression ratio. For locations, we propose two novel encoding schemes such that the binary code can preserve distance information, which is very helpful for LBS applications. In addition, an unresolved question in this area is whether it is possible to perform a search directly on the compressed trajectories, and if the answer is yes, then how. Here, we show that directly querying compressed trajectories based on our encoding scheme is possible and can be done efficiently. We design a set of primitive operations for this purpose, and propose index structures to reduce query response time. We demonstrate the advantage of our method and compare it against existing ones through a thorough experimental study on real trajectories in road network.

COMPRESS

More and more advanced technologies have become available to collect and integrate an unprecedented amount of data from multiple sources, including GPS trajectories about the traces of moving objects. Given the fact that GPS trajectories are vast in size while the information carried by the trajectories could be redundant, we focus on trajectory compression in this article. As a systematic solution, we propose a comprehensive framework, namely, COMPRESS ( <underline>Com</underline>prehensive <underline>P</underline>aralleled <underline>R</underline>oad-Network-Based Trajectory Compr<underline>ess</underline>ion ), to compress GPS trajectory data in an urban road network. In the preprocessing step, COMPRESS decomposes trajectories into spatial paths and temporal sequences, with a thorough justification for trajectory decomposition. In the compression step, COMPRESS performs spatial compression on spatial paths, and temporal compression on temporal sequences in parallel. It introduces two alternative algorithms with different strengths for lossless spatial compression and designs lossy but error-bounded algorithms for temporal compression. It also presents query processing algorithms to support error-bounded location-based queries on compressed trajectories without full decompression. All algorithms under COMPRESS are efficient and have the time complexity of O (| T |), where | T | is the size of the input trajectory T . We have also conducted a comprehensive experimental study to demonstrate the effectiveness of COMPRESS, whose compression ratio is significantly better than related approaches.

Review on trajectory data compression

The popularity of mobile terminals and the development of GPS positioning technology produce a mass of mobile trajectory data.Based on the data,a lot of location-based services(LBS)provide services for people.However,the increment of trajectory data brings many challenges:huge data volume,long query latency and data redundancy.Hence the trajectory compression plays an important role in providing better LBS.The purpose of trajectory compression is to minimize the size of trajectory as far as possible,which satisfies the threshold of similarity between compressed trajectory and original trajectory.This paper aims at illustrating useful trajectory compression methods,including line simplification methods,map-matching based compression methods and semantic compression methods,and introducing query processing of compressed trajectories and trajectory management systems.

SO6C: Compressed Trajectories in Air Traffic Management

Air traffic management is facing a tremendous increase in the amount of available flight data. Parallel to the decreasing time and cost necessary to produce ATM data, computational requirements for storage and analysis of the bulk of data are steeply increasing. Compression is a key technology to deal with this challenge – often referred to as big data science.In this paper,we propose a new technique for compressing 4D-trajectories of the Demand Data Repository provided by EUROCONTROL. While standard compression algorithms compress such a trajectory file as a large chunk of consecutive text, we propose to look at different streams in the trajectory files. Changing the traversal scheme with a focus on streams already improves compression rates beyond state-of-the-art. Based on the notion of streams, we design encoding techniques for each stream separately, by analyzing potential dependencies and redundancies in the SO6 files, following the principle: if it can be computed, then it does not need to be stored explicitly.In our evaluation we achieve a compression ratio of approx. 40:1 for all traffic over European airspace within one week, a reduction of more than 80 percent compared to the best standard compression technique 7zip. In addition, our new compression technique is almost four times faster than 7zip, while being only slightly slower than other out-of-the-box compression tools. Therefore, we are convinced that efficient, yet fast, compression of 4D-trajectories is possible on modern hardware and should be exploited.

PNN query processing on compressed trajectories

Trajectory compression is widely used in spatial-temporal databases as it can notably reduce (i) the computation/communication load of clients (GPS-enabled mobile devices) and (ii) the storage cost of servers. Compared with original trajectories, compressed trajectories have clear advantages in data processing, transmitting, storing, etc. In this paper, we investigate a novel problem of searching the Path Nearest Neighbor based on Compressed Trajectories (PNN-CT query). This type of query is conducted on compressed trajectories and the target is to retrieve the PNN with the highest probability (lossy compression leads to the uncertainty), which can bring significant benefits to users in many popular applications such as trip planning. To answer the PNN-CT query effectively and efficiently, a two-phase solution is proposed. First, we use the meta-data and sample points to specify a tight search range. The key of this phase is that the number of data objects/trajectory segments to be processed or decompressed should be kept as small as possible. Our efficiency study reveals that the candidate sets created are tight. Second, we propose a reconstruction algorithm based on probabilistic models to account for the uncertainty when decompressing the trajectory segments in the candidate set. Furthermore, an effective combination strategy is adopted to find the PNN with the highest probability. The complexity analysis shows that our solution has strong advantages over existing methods. The efficiency of the proposed PNN-CT query processing is verified by extensive experiments based on real and synthetic trajectory data in road networks.