Towards a Semantic Trajectory Similarity Measuring

Arboleda, Francisco Javier Moreno; Fernandez, Santiago Roman; Borgony, Vania

doi:10.17485/ijst/2017/v10i18/103400

Cited by 7 publications

(5 citation statements)

References 21 publications

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“…Several studies have been conducted to determine the similarity between trajectories based on criteria such as shape, speed, direction, visited sites, and categories of visited sites (e.g., educational or entertaining), among others. A few works [3,5] have also considered the activities carried out on the visited sites (e.g., studying, working, and participating in sports); however, they have not considered aspects related to the timing of the activities, such as duration, order, and frequency on a typical day.…”

Section: Motivationmentioning

confidence: 99%

“… Activities. The activities carried out by moving objects in the visited sites are a little-explored aspect for determining the similarity between trajectories [5]. A method that considers both the visited sites and the activities carried out by the users within them would offer more elements to the analysts to determine the similarity between users.…”

Section: Conclusion and Possible Lines Of Researchmentioning

confidence: 99%

“…Trajcevski et al [11] -Tiakas et al [14] Shape -Liu and Schneider [7] -Lee et al [4] -Yanagisawa et al [12] Direction -Liu and Schneider [7] Xiao et al [13] -Tiakas et al [14] Order -Xiao et al [13] -Zhao et al [8] -Ying et al [9] Moment -Chang et al [11] Xiao et al [13] -Lee and Chung [2] -Ying et al [9] -Moreno et al [5] Sequence of stops -Liu and Schneider [7]Activities-Kondaveeti[3] -Moreno et al[5] …”

mentioning

confidence: 99%

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An Approach to the Similarity Between Trajectories

Arboleda¹,

Luna²,

Carvajal³

2022

TEM Journal

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The trajectory of an object that moves in a space during a time interval describes its movement. Trajectories can be grouped by similarity according to different criteria, e.g., their shape, speed, distance travelled, duration, and visited sites, among others. These criteria have typically been approached separately, with few works considering the combination of criteria to measure similarity between trajectories. In this paper, we analyse several methods to calculate the similarity between trajectories. The most representative proposals are analysed in detail to identify lines of research. The analysis focuses especially on the proposals that consider semantic aspects for determining similarity, e.g., the categories of visited sites, and the combination of criteria. By means of two motivating examples, we show that the similarity between trajectories: i) is not a trivial notion and ii) can be useful in fields such as marketing, tourism, traffic, and animal monitoring, among others.

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Section: Motivationmentioning

confidence: 99%

Section: Conclusion and Possible Lines Of Researchmentioning

confidence: 99%

mentioning

confidence: 99%

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An Approach to the Similarity Between Trajectories

Arboleda¹,

Luna²,

Carvajal³

2022

TEM Journal

View full text Add to dashboard Cite

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“…There are few works [4], [5] consider the trajectory distance from the spatial aspect, the temporal aspect and the textual aspect all at once. The similarity measurement proposed by [5] is a linear combination of the Euclidean distance, the time interval intersection, and the number of full matching pair.…”

Section: Spatial-temporal-textual Similarity Measurementsmentioning

confidence: 99%

“…The similarity measurement proposed by [5] is a linear combination of the Euclidean distance, the time interval intersection, and the number of full matching pair. Reference [4] improves [5] by defining a new textual similarity by considering the hierarchy of the label semantics. A category tree is defined for the text classification, and different weights are assigned to the nodes for establishing the importance.…”

Section: Spatial-temporal-textual Similarity Measurementsmentioning

confidence: 99%

Approximate Similarity Measurements on Multi-Attributes Trajectories Data

et al. 2019

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With the development of global positioning technology, sensor networks, and smart mobile terminal, a large number of trajectory data are accumulated. Trajectory data contains a wealth of information, including spatiality, time series, and other external descriptive attributes (i.e., features, travelling mode, and so on). Trajectory analysis and mining show the great value. The research of trajectory similarity measurement is the basis of trajectory data management and mining, which plays an important role in trajectory computing. Most trajectory similarity work only focuses on the spatial-temporal features. The addition of multi-attributes to the trajectories changes the trajectory similarity. However, there are few researches focusing on multi-attributes trajectory similarity. In this paper, we propose two novel trajectory similarity measurements, i.e. maximum-minimum trajectory distance and sum of minimum trajectory distance and analyze the correlation among the spatial-temporal similarity and textual similarity. Finally, the measurement validity is verified and visualized through clustering, by both a simulation dataset and a real dataset. INDEX TERMS Trajectory similarity measurement, clustering, big trajectory data, trajectory computing.

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