Similarity-Based Compression of GPS Trajectory Data

Birnbaum, Jeremy; Meng, Hsiang-Cheng; Hwang, Jeong-Hyon; Lawson, Catherine T.

doi:10.1109/comgeo.2013.15

Cited by 15 publications

(17 citation statements)

References 9 publications

(17 reference statements)

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“…It is well known that trajectory data is essentially different from traditional point data. The commonly used point clustering algorithms cannot be directly adopted to handle trajectory clustering [ 53 , 54 , 55 ]. There are many basic questions that need to be considered for trajectory clustering.…”

Section: Literature Review Of Clustering Methodsmentioning

confidence: 99%

A Dimensionality Reduction-Based Multi-Step Clustering Method for Robust Vessel Trajectory Analysis

Liu

et al. 2017

Sensors

155

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The Shipboard Automatic Identification System (AIS) is crucial for navigation safety and maritime surveillance, data mining and pattern analysis of AIS information have attracted considerable attention in terms of both basic research and practical applications. Clustering of spatio-temporal AIS trajectories can be used to identify abnormal patterns and mine customary route data for transportation safety. Thus, the capacities of navigation safety and maritime traffic monitoring could be enhanced correspondingly. However, trajectory clustering is often sensitive to undesirable outliers and is essentially more complex compared with traditional point clustering. To overcome this limitation, a multi-step trajectory clustering method is proposed in this paper for robust AIS trajectory clustering. In particular, the Dynamic Time Warping (DTW), a similarity measurement method, is introduced in the first step to measure the distances between different trajectories. The calculated distances, inversely proportional to the similarities, constitute a distance matrix in the second step. Furthermore, as a widely-used dimensional reduction method, Principal Component Analysis (PCA) is exploited to decompose the obtained distance matrix. In particular, the top k principal components with above 95% accumulative contribution rate are extracted by PCA, and the number of the centers k is chosen. The k centers are found by the improved center automatically selection algorithm. In the last step, the improved center clustering algorithm with k clusters is implemented on the distance matrix to achieve the final AIS trajectory clustering results. In order to improve the accuracy of the proposed multi-step clustering algorithm, an automatic algorithm for choosing the k clusters is developed according to the similarity distance. Numerous experiments on realistic AIS trajectory datasets in the bridge area waterway and Mississippi River have been implemented to compare our proposed method with traditional spectral clustering and fast affinity propagation clustering. Experimental results have illustrated its superior performance in terms of quantitative and qualitative evaluations.

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Section: Literature Review Of Clustering Methodsmentioning

confidence: 99%

A Dimensionality Reduction-Based Multi-Step Clustering Method for Robust Vessel Trajectory Analysis

Liu

et al. 2017

Sensors

155

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“…When multiple trajectories are highly similar, trajectory compression can benefit from the commonalities among these trajectories. There are two main methods for compressing multiple trajectories based on their similarity: One is the TrajStore method proposed by Cudré-Mauroux, Wu, and Madden (2010); the other is the time mapping-based compression method proposed by Birnbaum, Meng, Hwang, and Lawson (2013). The TrajStore method selects one central trajectory as the reference trajectory R from a group of similar trajectories, and then any other trajectory T in this group can be redescribed using R. The time mapping-based compression method is a further deepening of the TrajStore method, which extracts the mapping between the time values of T and those of R. These mappings can be highly compressed due to a strong correlation between the time values of trajectories.…”

Section: Similarity-based Trajectory Compression Methodsmentioning

confidence: 99%

Integrated compression of vehicle spatio‐temporal trajectories under the road stroke network constraint

Zhao

Stefanakis²

2018

Transactions in GIS

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With fast growth of all kinds of trajectory datasets, how to effectively manage the trajectory data of moving objects has received a lot of attention. This study proposes a spatio‐temporal data integrated compression method of vehicle trajectories based on stroke paths coding compression under the road stroke network constraint. The road stroke network is first constructed according to the principle of continuous coherence in Gestalt psychology, and then two types of Huffman tree—a road strokes Huffman tree and a stroke paths Huffman tree—are built, based respectively on the importance function of road strokes and vehicle visiting frequency of stroke paths. After the vehicle trajectories are map matched to the spatial paths in the road network, the Huffman codes of the road strokes and stroke paths are used to compress the trajectory spatial paths. An opening window algorithm is used to simplify the trajectory temporal data depicted on a time–distance polyline by setting the maximum allowable speed difference as the threshold. Through analysis of the relative spatio‐temporal relationship between the preceding and latter feature tracking points, the spatio‐temporal data of the feature tracking points are all converted to binary codes together, accordingly achieving integrated compression of trajectory spatio‐temporal data. A series of comparative experiments between the proposed method and representative state‐of‐the‐art methods are carried out on a real massive taxi trajectory dataset from five aspects, and the experimental results indicate that our method has the highest compression ratio. Meanwhile, this method also has favorable performance in other aspects: compression and decompression time overhead, storage space overhead, and historical dataset training time overhead.

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“…For the evaluation of the compression, the maximum SED is employed. Birnbaum et al [21] present a method which exploits the similarities between sub-trajectories and creates a time mapping by finding for each time value of a trajectory the corresponding time value of a similar trajectory, using linear interpolation. A compression algorithm is applied on the time mapping, which removes the points but keeps the compression error under a user-defined threshold.…”

Section: Related Workmentioning

confidence: 99%

A Comparison of Trajectory Compression Algorithms Over AIS Data

et al. 2021

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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 have been developed that try to compress streams of vessel tracking data without compromising their spatio-temporal and kinematic features. In this paper, we present a wide range of several well-known trajectory compression algorithms and evaluate their performance on data originating from vessel trajectories. Trajectory compression algorithms included in this research are suitable for either historical data (offline compression) or real-time data streams (online compression). The performance evaluation is three-fold and each algorithm is evaluated in terms of compression ratio, execution speed and information loss. Experiments demonstrated that each algorithm has its own benefits and limitations and that the choice of a suitable compression algorithm is application-dependent. Finally, considering all assessed aspects, the Dead-Reckoning algorithm not only presented the best performance, but it also works over streaming data, which constitutes an important criterion in maritime surveillance.

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Similarity-Based Compression of GPS Trajectory Data

Cited by 15 publications

References 9 publications

A Dimensionality Reduction-Based Multi-Step Clustering Method for Robust Vessel Trajectory Analysis

A Dimensionality Reduction-Based Multi-Step Clustering Method for Robust Vessel Trajectory Analysis

Integrated compression of vehicle spatio‐temporal trajectories under the road stroke network constraint

A Comparison of Trajectory Compression Algorithms Over AIS Data

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