Discovering similar multidimensional trajectories

Vlachos, Michail; Kollios, George; Gunopulos, Dimitrios

doi:10.1109/icde.2002.994784

Cited by 1,004 publications

(804 citation statements)

References 29 publications

Supporting

Mentioning

796

Contrasting

Unclassified

Order By: Relevance

“…Spatial transformations can also realign trajectories for better comparison. The Longest Common Substring (LCSS) approach does not consider the entire trajectory but instead finds similarities between substrings [25]. Edit Distance on Real Sequence (EDR), which measures the number of operations (insert, delete or replace) required to transform one trajectory to another, extends this by assigning penalties to the gaps between two matched sub-trajectories [26].…”

Section: Trajectory Similaritymentioning

confidence: 99%

Interpreting Pedestrian Behaviour by Visualising and Clustering Movement Data

McArdle

Demšar

Spek

et al. 2013

Web and Wireless Geographical Information Systems

View full text Add to dashboard Cite

Abstract. Recent technological advances have increased the quantity of movement data being recorded. While valuable knowledge can be gained by analysing such data, its sheer volume creates challenges. Geovisual analytics, which helps the human cognition process by using tools to reason about data, offers powerful techniques to resolve these challenges. This paper introduces such a geovisual analytics environment for exploring movement trajectories, which provides visualisation interfaces, based on the classic space-time cube. Additionally, a new approach, using the mathematical description of motion within a space-time cube, is used to determine the similarity of trajectories and forms the basis for clustering them. These techniques were used to analyse pedestrian movement. The results reveal interesting and useful spatiotemporal patterns and clusters of pedestrians exhibiting similar behaviour.

show abstract

Section: Trajectory Similaritymentioning

confidence: 99%

Interpreting Pedestrian Behaviour by Visualising and Clustering Movement Data

McArdle

Demšar

Spek

et al. 2013

Web and Wireless Geographical Information Systems

View full text Add to dashboard Cite

show abstract

“…Ref. [12] formalized a LCSS (Least Common Subsequence) distance, which assists the application of traditional clustering algorithms (e.g., partitioning, hierarchical, etc.) on object trajectories.…”

Section: Clustering Spatio-temporal Datamentioning

confidence: 99%

On Discovering Moving Clusters in Spatio-temporal Data

Kalnis

Mamoulis

Bakiras

2005

Advances in Spatial and Temporal Databases

339

237

View full text Add to dashboard Cite

Abstract.A moving cluster is defined by a set of objects that move close to each other for a long time interval. Real-life examples are a group of migrating animals, a convoy of cars moving in a city, etc. We study the discovery of moving clusters in a database of object trajectories. The difference of this problem compared to clustering trajectories and mining movement patterns is that the identity of a moving cluster remains unchanged while its location and content may change over time. For example, while a group of animals are migrating, some animals may leave the group or new animals may enter it. We provide a formal definition for moving clusters and describe three algorithms for their automatic discovery: (i) a straight-forward method based on the definition, (ii) a more efficient method which avoids redundant checks and (iii) an approximate algorithm which trades accuracy for speed by borrowing ideas from the MPEG-2 video encoding. The experimental results demonstrate the efficiency of our techniques and their applicability to large spatio-temporal datasets.

show abstract

“…Other proposals compute the longest common subsequence of two series [19], and the least common sub-sequence of two series, and take these measures as the distance between trajectories [1].…”

Section: Related Workmentioning

confidence: 99%

Analyzing Trajectories Using Uncertainty and Background Information

Kuijpers

Moelans

Othman

et al. 2009

Advances in Spatial and Temporal Databases

View full text Add to dashboard Cite

Abstract. A key issue in clustering data, regardless the algorithm used, is the definition of a distance function. In the case of trajectory data, different distance functions have been proposed, with different degrees of complexity. All these measures assume that trajectories are error-free, which is essentially not true. Uncertainty is present in trajectory data, which is usually obtained through a series of GPS of GSM observations. Trajectories are then reconstructed, typically using linear interpolation. A first source of error in trajectory are the GPS observations themselves, since many reported points lie outside the road network. Thus, the users position must be matched to a map, leading to the problem of map matching. A well-known model to deal with uncertainty in a trajectory sample, uses the notion of space-time prisms (also called beads), to estimate the positions where the object could have been, given a maximum speed. Thus, we can replace a (reconstructed) trajectory by a necklace (intuitively, a a chain of prisms), connecting consecutive trajectory sample points. When it comes to clustering, the notion of uncertainty requires appropriate distance functions. The main contribution of this paper is the definition of a distance function that accounts for uncertainty, together with the proof that this function is also a metric, and therefore it can be used in clustering. We also present an algorithm that computes the distance between the chains of prisms corresponding to two trajectory samples. Finally, we discuss some preliminary results, obtained clustering a set of trajectories of cars in the center of the city of Milan, using the distance function introduced in this paper.

show abstract

Discovering similar multidimensional trajectories

Abstract: We investigate techniques for analysis and retrieval of object trajectories in a two or three dimensional space.

Cited by 1,004 publications

References 29 publications

Interpreting Pedestrian Behaviour by Visualising and Clustering Movement Data

Interpreting Pedestrian Behaviour by Visualising and Clustering Movement Data

On Discovering Moving Clusters in Spatio-temporal Data

Analyzing Trajectories Using Uncertainty and Background Information

Contact Info

Product

Resources

About