Similar Sub-trajectory Retrieval for Moving Objects in Spatio-temporal Databases

Shim, Choon-Bo; Chang, Jae-Woo

doi:10.1007/978-3-540-39403-7_24

Cited by 9 publications

(6 citation statements)

References 4 publications

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“…This shows that if the spatiotemporal variable is considered, then T R C would have the most similar trajectory to T R A . This outcome differs from the similarity measures proposed by [2] and [11], which consider only the similarity between projected trajectories.…”

Section: Similarity Of Moving Object Trajectory On Road Networkmentioning

confidence: 62%

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Spatio-temporal Similarity Analysis Between Trajectories on Road Networks

Hwang

Kang

2005

Lecture Notes in Computer Science

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In order to analyze the behavior of moving objects, a measure for determining the similarity of trajectories needs to be defined. Although research has been conducted that retrieved similar trajectories of moving objects in Euclidean space, very little research has been conducted on moving objects in the space defined by road networks. In terms of real applications, most moving objects are located in road network space rather than in Euclidean space. In this paper, we investigate the properties of similar trajectories in road network space. And we propose a method to retrieve similar trajectories based on this observation and similarity measure between trajectories on road network space. Experimental results show that this method provides not only a practical method for searching for similar trajectories but also a clustering method for trajectories.

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Section: Similarity Of Moving Object Trajectory On Road Networkmentioning

confidence: 62%

“…However, since this method assumes Euclidean space, it is difficult to apply it to road network space. A similar method was proposed in [11] but has the same problem of Euclidean distance as [2].…”

Section: Related Workmentioning

confidence: 99%

Spatio-temporal Similarity Analysis Between Trajectories on Road Networks

Hwang

Kang

2005

Lecture Notes in Computer Science

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“…Their approach uses the shape similarity between lines to retrieve required objects. Shim and Chang [7] considered the similarity of sub-trajectories and proposed a distance 'KWarping' algorithm. We also find similar approaches in Valachos et al [8]), Sakurai et al [10], and Chen et al [11].…”

Section: Related Workmentioning

confidence: 99%

Trajectory Similarity of Network Constrained Moving Objects and Applications to Traffic Security

Abraham

Lal²

2010

Intelligence and Security Informatics

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Abstract. Spatio-Temporal data analysis plays a central role in many securityrelated applications including those relevant to transportation infrastructure, border and inland security. In several applications, data objects move on predefined spatial networks such as road segments, railways, and invisible air routes, which provides the possibility of representing the data in reduced dimension. This dimensionality reduction gives additional advantages in spatiotemporal data management like indexing, query processing, similarity and clustering of trajectory data etc. There are many proposals concerning trajectory similarity problem which includes Euclidian, network, time based measures and concepts known as Position of Interest(POI), Time of Interest(TOI) etc. This paper demonstrates how these POI and TOI methods could be advantages in security informatics domain suitable to work with road network constrained moving object data, stored using a binary encoding scheme proposed in a previous PAISI paper.

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“…They define a data model of trajectories as directed lines in a space, and the similarity between trajectories as the Euclidean distance between directed discrete lines and extraction of the individual moving patterns from each object from the trajectories considering both time and location. Reference [19] considered the similarity of sub-trajectories and proposed a 'K-warping' distance algorithm. Reference [14] focused on the spatial shapes and compared spatial shapes of moving object trajectories by developing algorithms for evaluating OWD (One Way Distance) in both continuous and discrete cases of the trajectories for similarity search.…”

Section: B Trajectory Similaritymentioning

confidence: 99%

Characterizing Traffic Density and Its Evolution through Moving Object Trajectories

Kharrat

Zeitouni

Sandu-Popa

et al. 2009

2009 Fifth International Conference on Signal Image Technology and Internet Based Systems

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Managing and mining data derived from moving objects have become an important issue in recent years. In this paper, we are interested in mining trajectories of moving objects, such as vehicles in the road network. We propose a method for discovering dense routes by clustering similar road sections according to both traffic and location in each time period. The traffic estimation is based on the collected spatiotemporal trajectories. We also propose a characterization approach of the temporal evolution of dense routes by a graph connecting dense routes over consecutive time periods. This graph is labeled by a degree of evolution. We have implemented and tested the proposed algorithms, which have shown their effectiveness and efficiency.

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Similar Sub-trajectory Retrieval for Moving Objects in Spatio-temporal Databases

Cited by 9 publications

References 4 publications

Spatio-temporal Similarity Analysis Between Trajectories on Road Networks

Spatio-temporal Similarity Analysis Between Trajectories on Road Networks

Trajectory Similarity of Network Constrained Moving Objects and Applications to Traffic Security

Characterizing Traffic Density and Its Evolution through Moving Object Trajectories

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