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Today’s industry is flooded with tracking data originating from vessels across the globe that transmit their position at frequent intervals. These voluminous and high-speed streams of data has led researchers to develop novel ways to compress them in order to speed-up processing without losing valuable information. To this end, several algorithms h...
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... the Hausdorff distance is directed which means that h(A, B) = h(B, A). For instance, in Figure 7 we can observe that h(A, B) = d(a 5 , b 1 ) where b 1 is the nearest point of B to A and d(a 5 , b 1 ) is the maximum distance. Similarly, h(B, A) = d(b 5 , a 1 ) where a 1 is the nearest point of A to B and d(b 5 , a 1 ) is the maximum distance. ...
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... Therefore, in recent years, this method has been more commonly applied in studies to improve the efficiency and accuracy of trajectory compression [7][8][9], especially for a large number of trajectory data [10,11], or for online trajectory compression [10,[12][13][14] and some specific scenarios' trajectory data compression (e.g., indoor trajectory data compression [15,16] and ship trajectory compression [17,18]). These studies have improved the accuracy and efficiency of trajectory data compression in various respects. ...
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.
... Some studies considered the road network [20][21][22][23][24], where the GPS trajectory is located, converted the original GPS trajectory into segment sequences, and then compressed the segment sequences. In addition to the compression of vehicle GPS trajectories, some studies explored the compression problem [25][26][27][28][29] for ship GPS trajectories collected by AIS. Beyond GPS trajectory compression in offline scenarios, some studies have also delved into real-time GPS trajectory compression algorithms. ...
Every day, hundreds of thousands of vehicles, including buses, taxis, and ride-hailing cars, continuously generate GPS positioning records. Simultaneously, the traffic big data platform of urban transportation systems has already collected a large amount of GPS trajectory datasets. These incremental and historical GPS datasets require more and more storage space, placing unprecedented cost pressure on the big data platform. Therefore, it is imperative to efficiently compress these large-scale GPS trajectory datasets, saving storage cost and subsequent computing cost. However, a set of classical trajectory compression algorithms can only be executed in a single-threaded manner and are limited to running in a single-node environment. Therefore, these trajectory compression algorithms are insufficient to compress this incremental data, which often amounts to hundreds of gigabytes, within an acceptable time frame. This paper utilizes Spark, a popular big data processing engine, to parallelize a set of classical trajectory compression algorithms. These algorithms consist of the DP (Douglas–Peucker), the TD-TR (Top-Down Time-Ratio), the SW (Sliding Window), SQUISH (Spatial Quality Simplification Heuristic), and the V-DP (Velocity-Aware Douglas–Peucker). We systematically evaluate these parallelized algorithms on a very large GPS trajectory dataset, which contains 117.5 GB of data produced by 20,000 taxis. The experimental results show that: (1) It takes only 438 s to compress this dataset in a Spark cluster with 14 nodes; (2) These parallelized algorithms can save an average of 26% on storage cost, and up to 40%. In addition, we design and implement a pipeline-based solution that automatically performs preprocessing and compression for continuous GPS trajectories on the Spark platform.
... Therefore, storing, retrieving, managing, processing, and querying these trajectories is computationally expensive and requires large storage spaces (Nasiri, Azimi, and Abbaspour, 2018;Zhao and Shi, 2019). A critical step in trajectory representation and preprocessing is to reduce incoming data volumes while preserving the main properties and semantics associated with these trajectories and resulting movement patterns (Makris, Kontopoulos, Alimisis, and Tserpes, 2021). Over the past few years, several compression methods have been proposed to reduce the volume of trajectory data. ...
Continuous progress in navigation, sensor-based, and GPS technologies have made smart devices essential to our daily lives and many location-based applications. However, the trajectory datasets generated by these applications require the management of large data volumes while preserving their main properties and semantics. One of the most popular methods for compressing trajectory data offline is the Douglas–Peucker (DP) algorithm, but its principles should be applied to a diverse range of contexts when considering real-time trajectory data. This paper introduces a Flexible Douglas-Peucker algorithm (FDP) that takes into account the data’s diversity, underlying properties, and semantics. The proposed framework is applied to the Geolife benchmark dataset with a series of different thresholds that reflects different contexts and constraints when performing a trajectory compression process. The results show that the proposed algorithm achieves a significant compression rate while preserving trajectory data points that have a semantic role concerning different modes of transportation.
... To increase the effectiveness of emission inventory investigations, large amounts of data must be compressed before analysis. Compressed data can free up storage space and make it easier to store and transmit trajectory information [5]. More importantly, the ship's trajectory data may be thoroughly analyzed with the help of simplification, allowing it to can retain pertinent information and eliminate superfluous material. ...
An active area of study under the dual carbon target, which is based on automatic identification systems (AIS), is the emission inventory of pollutants from ships. Data compression is required because there is currently so much data that it has become difficult to transmit, process, and store it. A trajectory simplification method considering the ship sailing state and acceleration rate of change is developed in this paper to assure the validity of the compressed data used in the emission inventory analysis. By carefully examining the integral relationship between acceleration and pollution emissions, the algorithm constructs an acceleration rate of change function for data compression and categorizes AIS data by ship navigation status. By dynamically altering the amount of acceleration change, the developed function can stabilize the pollutant emission calculation error and adaptively calculate the threshold value. The experimental results show that the emission calculation error of the proposed algorithm is only 0.185% when the compression rate is 90.28%.
... In previous studies, knowledge was extracted from AIS data, and researchers commonly emphasized the importance of data preprocessing. There are challenges in extracting knowledge from AIS data because it has limitations such as sensor error, volume of data, incompleteness, and noise [2,[20][21][22][23]. Among these preprocessing techniques, reducing the number of AIS-data points to reduce storage and computation costs is significant. ...
With the development of maritime technology and equipment, most ships are equipped with an automatic identification system (AIS) to store navigation information. Over time, the size of the data increases, rendering its storage and processing difficult. Hence, it is necessary to transform the AIS data into trajectories, and then simplify the AIS trajectories to remove unnecessary information that is not related to route shape. Moreover, topographic information must be considered because otherwise, the simplified trajectory can intersect obstacles. In this study, we propose an AIS trajectory simplification algorithm considering topographic information. The proposed algorithm simplifies the trajectories without the intersection of the trajectory and obstacle using the improved Douglas–Peucker algorithm. Polygon map random (PMR) quadtree was used to consider topographic information on the coast, and the intersection between topographic information and simplified trajectories was efficiently computed using the PMR quadtree. To verify the effectiveness of the proposed algorithm, experiments were conducted on real-world trajectories in the Korean sea. The proposed algorithm yielded simplified trajectories with no intersections of the trajectory and obstacle. In addition, the computational efficiency of the proposed algorithm with the PMR quadtree was superior to that without the PMR quadtree.
... The key idea of RRE is to group trajectories of similar shapes into the same cluster, from which a reference route can be constructed. Since the original trajectories extracted from AIS data may contain large numbers of points, we apply the Douglas-Peucker (DP) algorithm (Makris et al., 2021) to reduce the number of points in each trajectory to reduce the computational complexity. The DP algorithm is applied to each of the trajectories from the training dataset. ...
Accurate vessel Time of Arrival (ToA) estimation is important for port operation and resource management. In this paper, we propose a data-driven approach for estimating the ToA of ships based on Automatic Identification System (AIS) data. The proposed approach exploits a novel trajectory clustering approach to extract representative routes, from which training trajectories are grouped into clusters and ToA estimation models based on Support Vector Regressor (SVR) are trained for each cluster. The proposed ToA estimation approach adopts a short period of the latest AIS data as input and performs SVR model selection and ToA estimation in real-time. A historical AIS dataset provided by the Danish Maritime Authority is used to evaluate the proposed approach. Numerical results for tanker ships travelling to the port of Skagen demonstrate that the proposed approach can achieve ToA estimations that are 40% to 70% more accurate than state-of-the-art ToA estimation methods.
... A survey and empirical study of various trajectory simplification techniques is available in [44]. In another recent survey [25], several state-of-the-art simplification algorithms are empirically compared specifically against AIS data. Although such generic online or offline methods can provide reliable summaries over vessel trajectories, they entirely lack support for mobility-annotated features in the retained sampled points. ...
We present an open-source system that can optimize compressed trajectory representations for large fleets of vessels. We take into account the type of each vessel in order to choose a suitable configuration that can yield improved trajectory synopses, both in terms of approximation error and compression ratio. We employ a genetic algorithm that converges to a fine-tuned configuration per vessel type without any hyper-parameter tuning. These configurations can provide synopses that retain less than 10% of the original points with less than 20m approximation error in a real world dataset; in another dataset with 90% less samples than the previous one, the synopses retain 20% of the points and achieve less than 80m error. Additionally the level of compression can be chosen by the user, by setting the desired approximation error. Our system also supports incremental optimization by training in data batches, and therefore continuously improves performance. Furthermore, we employ a composite event recognition engine to efficiently detect complex maritime activities, such as ship-to-ship transfer and loitering; thanks to the synopses generated by the genetic algorithm instead of the raw trajectories, we make the recognition process faster while also maintaining the same level of recognition accuracy. Our extensive empirical study demonstrates the effectiveness of our system over large, real-world datasets.
... Massive volumes of spatio-temporal data are constantly being generated by the vast amount of available devices, services and systems. Geospatial services such as GPS systems, Google Maps and RFID sensors are producing terabytes of spatial data every day and in combination with the growing popularity of location-based services and map-based applications, there is an increasing demand in the spatial support of databases systems [9]- [12]. There are challenges in managing and querying the massive scale of spatial data such as the high computation complexity of spatial queries and the efficient handling the big data nature of them. ...
The sudden rise of GPS-enabled mobile devices has given birth to research related to the analysis and visualization of big mobility data that are stored in large spatio-temporal databases. Therefore, this research is focused on evaluating and comparing widely-used database systems that are employed in the analysis of spatio-temporal data. Specifically, three database systems are evaluated and compared with each other, namely PostGIS, MobilityDB, and MongoDB, in their ability to perform range, temporal aggregate, distance and nearest-neighbor queries. To this end, a subset of the BerlinMOD benchmark queries is employed for evaluation purposes over vessel tracking data. The experimental results presented in this paper are only preliminary in an attempt to drive future research in the field of industrial use case surveillance.
... Besides, it is also used by researchers for traffic anomaly detection, route estimation, collision prediction, and path planning (Tu et al., 2017;Murray and Perera, 2020;Kontopoulos et al., 2020). As AIS data is featured with big volume, incompleteness, noise, and dark vessels, data preprocessing is the prerequisite of AIS application (Tu et al., 2017;Kontopoulos et al., 2018;Makris et al., 2021b). One critical step of such prerequisite is data denoising and rectification, including detecting missing and noise data fields and adopting proper methods to rectify them. ...
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.
... The existing methods of trajectory data compression can be divided into three categories [8][9][10][11]. The first is a trajectory data compression based on line segment simplification [12][13][14][15][16][17]; the trajectory in free space is segmented linearly by constraints, and only two end points of each approximate segment are stored to achieve compression. The second is a trajectory data compression based on road network structure [18][19][20][21][22]; the trajectory points are mapped onto the road network, and the original trajectory is represented by a grid structure to reduce the amount of data. ...
... After that, the fixed number of segments will start from 1 and increase from small to large to search for the corresponding optimal segmentation solution (line 2-10). By calculating and evaluating the compression ratio and segmentation accuracy, a solution meeting the satisfaction requirements will be found (line [5][6][7][8]. Finally, each trajectory in the original data set is optimized by the above method (line 1-11), the compressed trajectory data set can be generated (line 12), and the compression ratio and segmentation accuracy are given (line 13). ...
... In the experiment of real trajectory data set, the number of initialization particles N is 15 and the maximum number of iterations I N is 5. The DP algorithm needs to adju the distance threshold to obtain the optimal segmentation, and the threshold sampli point in the experiment is [8,6,4,3, 2,1.5,1, 0.7, 0.5, 0.3] DP d . The segmentation algorith in TRACLUS will be affected by trajectory scaling, and different scaling ratios will aff the segmentation accuracy. ...
Linear approximate segmentation and data compression of moving target spatio-temporal trajectory can reduce data storage pressure and improve the efficiency of target motion pattern mining. High quality segmentation and compression need to accurately select and store as few points as possible that can reflect the characteristics of the original trajectory, while the existing methods still have room for improvement in segmentation accuracy, reduction of compression rate and simplification of algorithm parameter setting. A trajectory segmentation and compression algorithm based on particle swarm optimization is proposed. First, the trajectory segmentation problem is transformed into a global intelligent optimization problem of segmented feature points, which makes the selection of segmented points more accurate; then, a particle update strategy combining neighborhood adjustment and random jump is established to improve the efficiency of segmentation and compression. Through experiments on a real data set and a maneuvering target simulation trajectory set, the results show that compared with the existing typical methods, this method has advantages in segmentation accuracy and compression rate.
