Trajectory Compression Methods Research Articles

Trajectory Compression with Spatio-Temporal Semantic Constraints

Most trajectory compression methods primarily focus on geometric similarity between compressed and original trajectories, lacking explainability of compression results due to ignoring semantic information. This paper proposes a spatio-temporal semantic constrained trajectory compression method. It constructs a new trajectory distance measurement model integrating both semantic and spatio-temporal features. This model quantifies semantic features using information entropy and measures spatio-temporal features with synchronous Euclidean distance. The compression principle is to retain feature points with maximum spatio-temporal semantic distance from the original trajectory until the compression rate is satisfied. Experimental results show these methods closely resemble each other in maintaining geometric similarity of trajectories, but our method significantly outperforms DP, TD-TR, and CascadeSync methods in preserving semantic similarity of trajectories. This indicates that our method considers both geometric and semantic features during compression, resulting in the compressed trajectory becoming more interpretable.

A Self-Adaptive Compression Method for Ship Trajectories without Threshold Setting

Addressing the shortcomings of existing ship trajectory compression methods that rely on the empirical setting of a fixed threshold and face challenges in controlling the spatial similarity before and after compression, this paper proposes a self-adaptive compression method for ship trajectories that does not require threshold setting. Initially, a hierarchical and sequential tree structure based on the trajectory characteristics is constructed to determine the importance of a ship’s trajectory points. Subsequently, the dynamic time warping (DTW) operator is introduced to assess the spatial similarity of the trajectory before and after compression, exploring the laws governing similarity variations in the step changes during the compression process from lower to higher levels of the hierarchical and sequential sequence. Finally, the trajectory point that causes the largest step change in similarity within the hierarchical and sequential sequence is identified, and points at lower levels than this point are discarded, thus achieving the objective of self-adaptive adjustment of the level of compression. Our case study results demonstrate that, compared with existing ship trajectory compression methods based on empirically set thresholds, the proposed method has the following advantages: (1) it does not require presetting a fixed threshold and adaptively determines the degree of compression by identifying the trajectory point that leads to the largest step change in similarity, and (2) under the condition of a similar data compression rate, the DTW calculation value is reduced by approximately 25%, significantly enhancing the similarity of the trajectory before and after compression.

An online method for ship trajectory compression using AIS data

Abstract Vessel trajectories from the Automatic Identification System (AIS) play an important role in maritime traffic management, but a drawback is the huge amount of memory occupation which thus results in a low speed of data acquisition in maritime applications due to a large number of scattered data. This paper proposes a novel online vessel trajectory compression method based on the Improved Open Window (IOPW) algorithm. The proposed method compresses vessel trajectory instantly according to vessel coordinates along with a timestamp driven by the AIS data. In particular, we adopt the weighted Euclidean distance (WED), fusing the perpendicular Euclidean distance (PED) and synchronous Euclidean distance (SED) in IOPW to improve the robustness. The realistic AIS-based vessel trajectories are used to illustrate the proposed model by comparing it with five traditional trajectory compression methods. The experimental results reveal that the proposed method could effectively maintain the important trajectory features and significantly reduce the rate of distance loss during the online compression of vessel trajectories.

An Improved BLG Tree for Trajectory Compression with Constraints of Road Networks

With the rising popularity of portable mobile positioning equipment, the volume of mobile trajectory data is increasing. Therefore, trajectory data compression has become an important basis for trajectory data processing, analysis, and mining. According to the literature, it is difficult with trajectory compression methods to balance compression accuracy and efficiency. Among these methods, the one based on spatiotemporal characteristics has low compression accuracy due to its failure to consider the relationship with the road network, while the one based on map matching has low compression efficiency because of the low efficiency of the original method. Therefore, this paper proposes a trajectory segmentation and ranking compression (TSRC) method based on the road network to improve trajectory compression precision and efficiency. The TSRC method first extracts feature points of a trajectory based on road network structural characteristics, splits the trajectory at the feature points, ranks the trajectory points of segmented sub-trajectories based on a binary line generalization (BLG) tree, and finally merges queuing feature points and sub-trajectory points and compresses trajectories. The TSRC method is verified on two taxi trajectory datasets with different levels of sampling frequency. Compared with the classic spatiotemporal compression method, the TSRC method has higher accuracy under different compression degrees and higher overall efficiency. Moreover, when the two methods are combined with the map-matching method, the TSRC method not only has higher accuracy but also can improve the efficiency of map matching.

A Direction-Preserved Vessel Trajectory Compression Algorithm Based on Open Window

Ship trajectory data can be used in most marine-related research, and most ship trajectory data come from AIS. The large number of ships and the short reporting period of AIS have resulted in a huge amount of ship trajectory data, which has caused a certain amount of pressure on the relevant research. This paper proposes a direction-preserved vessel trajectory compression method based on Open Window, which can effectively retain the direction change feature points while ensuring the position error. In addition, the method can work in both offline and online modes. Also, the velocity threshold and low-speed redundancy points problems are considered. In order to verify the effect of the proposed method and the adaptability of the method to different feature data, a parallel experiment was performed on port water and coastal water datasets. The results show that our method can compress vessel trajectories while retaining the feature points. Meanwhile, by comparing it with the compression method that is based on distance thresholds, the vessel trajectory compress time is drastically reduced by up to 87.3% in the port water data. The research in this paper provides a new method through which to compress vessel trajectories for research in marine-related fields on vessel trajectory.

VOLTCom: A Novel Online Trajectory Compression Method Based on Vector Processing

VOLTCom: A Novel Online Trajectory Compression Method Based on Vector Processing

A method for detecting abnormal behavior of ships based on multi-dimensional density distance and an abnormal isolation mechanism.

Abnormal ship behavior detection is essential for maritime navigation safety. Most existing abnormal ship behavior detection methods only build A ship trajectory position outlier detection model; however, the construction of a ship speed outlier detection model is also significant for maritime navigation safety. In addition, in most existing methods for detecting a ship's abnormal behavior based on abnormal thresholds, one unsuitable threshold leads to the risk of the ship not being minimized as much as possible. In this paper, we proposed an abnormal ship behavior detection method based on distance measurement and an isolation mechanism. First, to address the problem of traditional trajectory compression methods and density clustering methods only using ship position information, the minimum description length principle based on acceleration (AMDL) algorithm and Multi-Dimensional Density Clustering (MDDBSCAN) algorithm is used in this study. These algorithms not only considered the position information of the ship, but also the speed information. Second, regarding the issue of the difficulty in determining the anomaly threshold, one method for determining the anomaly threshold based on the relationship between the velocity weights and noise points of the MDDBSCAN algorithm has been introduced. Finally, due to the randomness issue of the selected segmentation value in iForest, a strategy of selectively constructing isolated trees was proposed, thus further improving the efficiency of abnormal ship behavior detection. The experimental results on the historical automatic identification system data set of Xiamen port prove the practicality and effectiveness of our proposed method. Our experiment results show that the proposed method achieves an improvement of about 10% over the trajectory outlier detection based on the local outlier fraction method, about 14% over the isolation-based online anomalous trajectory method in terms of the accuracy of ship position information anomaly detection, and about 3% over the feature fusion method in terms of the accuracy of ship speed anomaly detection. This method improves algorithm efficiency by about 5% compared to the traditional isolation forest anomaly detection algorithm.

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.

A Trajectory Compression Method Based on Fréchet Distance

To better maintain the geometric similarity between the compressed trajectory and the original trajectory, this paper proposes a trajectory compression method based on Fréchet distance. This method selects the characteristic points of the trajectory based on the Fréchet distance, and keeps the similarity of the geometric shape characteristics of the compressed trajectory and the original trajectory. Compared with the traditional DP (Douglas-Peucker) algorithm and the TD-TR (Top-Down Time-Ratio) algorithm, the method in this paper can obtain better geometric similarity between the compressed trajectory and the original trajectory. At the same time, better visualization of trajectory data is obtained.

Research on the Method and Application of MapReduce in Mobile Track Big Data Mining

Background: Mass movement trajectory data with real scenarios has been evolved with big data mining to solve the data redundancy problem. Methods: This paper proposes a parallel path based on the Map Reduce compression method, using two kinds of piecewise point mutual crisscross, the classified method of trajectory, and then segment trajectory distribution to multiple nodes to parallelize the compression. Results: Finally, the results based on both compression methods have been simulated for the different real-time data by merging both techniques. Conclusion: The performance test results show that the parallel trajectory compression method proposed in this paper can greatly improve the compression efficiency and completely eliminate the error caused by the failure of the correlation between the segments.

Vessel AIS Trajectory Online Compression Based on Scan-Pick-Move Algorithm Added Sliding Window

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.

Self-Adaptive Online Trajectory Sampling (SAOTS) Using Spectral Domain Properties

Abstract We study the vehicle trajectory compression problem in this paper to reduce data size and transmission cost, while ensuring that the reduced data can satisfy the needs of transportation applications. We first propose an online segmentation method and then explore the spectral domain properties of vehicle trajectory segments by using the speed profile as the input signal. Based on the analysis, we introduce a spectral domain property called critical frequency that reflects the nature of the speed variation experienced by an individual vehicle trajectory. We show that, this property can be utilized to determine the proper sampling strategy of trajectory segments. We then design SAOTS, a self-adaptive online trajectory sampling method, for vehicle trajectory segmentation and compression. SAOTS utilizes vehicle flow state to segment a vehicle trajectory, and the critical frequency to sample the data records in the segment. Results show that resampling the trajectory segments based on the critical frequencies can vastly reduce the size of the GPS data while conserving most of the useful information. Tested using a small field dataset and a large, open dataset and compared with existing trajectory compression methods, SAOTS can help achieve the best balance in terms of data reduction, latency, accuracy, and application performances.

A parallel online trajectory compression approach for supporting big data workflow

Nowadays with booming of sensor technology, location big data exhibit as high complexity, massive volume, real-time and stream-based characteristic. The current workflow systems are facing the challenge hardly to efficiently process the real-time location big data like trajectory stream. Online compression method is an available solution to preprocess these trajectory data in order to speed up the processing of big data workflow. However, the current online compression methods are in a serial execution that are hard to fast compress massive real-time original trajectory data. Aiming at this problem, we employ the multi-core and many-core approaches to accelerate a representative online trajectory compression method SQUISH-E. First a parallel version of SQUISH-E is proposed. PSQUISH-E used a data parallel scheme based on overlap technique and OpenMP to achieve the implementation over multiple-core CPUs. For further reducing compression time, we combine iteration method and GPU Hyper-Q feature to develop GPU-aided PSQUISH-E algorithm called as G-PSQUISH-E. The experimental results showed that (1) the data parallel scheme based on overlap can reach a similar SED error as the SQUISH-E (2) the proposed PSQUISH-E running on multi-core CPU achieved 3.8 times acceleration effect, and (3) G-PSQUISH-E further accelerated the effect of about 3 times compared with PSQUISH-E.

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.

Lossy compression for PDE-constrained optimization: adaptive error control

For the solution of optimal control problems governed by nonlinear parabolic PDEs, methods working on the reduced objective functional are often employed to avoid solving large systems in the dimension of the full spatio-temporal discretization. The evaluation of the reduced gradient requires one solve of the state equation forward in time, and one backward solve of the adjoint equation. The state enters into the adjoint equation, requiring the storage of a full 4D data set. If Newton-CG methods are used, two additional trajectories have to be stored. To get numerical results which are accurate enough, in many cases very fine discretizations in time and space are necessary, which leads to a significant amount of data to be stored and transmitted to mass storage. Lossy compression methods were developed to overcome the storage problem by reducing the accuracy of the stored trajectories. The inexact data induces errors in the reduced gradient and reduced Hessian. In this paper, we analyze the influence of such a lossy trajectory compression method on Newton-CG methods for optimal control of parabolic PDEs and design an adaptive strategy for choosing appropriate quantization tolerances.

Encoding network-constrained travel trajectories using routing algorithms

This study proposes a generic encoder for network-constrained travel trajectories, and it implements two encoders by combining the proposed generic encoder with two routing algorithms, which reduce the size of a travel trajectory's path along a road network without modifying it. Although most previous trajectory compression methods introduce an error in the spatial component of trajectories to achieve compression, we argue that the path of a travel trajectory, i.e. the sequence of roads travelled, is crucial information for many applications. Experimental results obtained using a large-data set of real travel trajectories show that the two proposed encoders achieve high compression, outperforming a general purpose data compression method.