Clustering Trajectories of Moving Objects in an Uncertain World

Pelekis, Nikos; Kopanakis, Ioannis; Kotsifakos, Evangelos E.; Frentzos, Elias; Theodoridis, Yannis

doi:10.1109/icdm.2009.57

Cited by 66 publications

(47 citation statements)

References 23 publications

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“…Geometric Distance [9,10,19,27,[30][31][32]34,44,45] Direction [8,[16][17][18]23,27,33,34,39] Turning Angle [19,23,28,31] Sinuosity [16,20,28,31] Physical Velocity [8,18,19,27,28,44] Acceleration [18][19][20] Stop Point [17,18,27,30,33,34,36,43] POI [9,10,[14][15][16][21][22][23][24]…”

Section: Parameters Related Workmentioning

confidence: 99%

A Geometric Framework for Detection of Critical Points in a Trajectory Using Convex Hulls

Milaghardan

Abbaspour

Claramunt

2018

IJGI

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Large volumes of trajectory-based data require development of appropriate data manipulation mechanisms that will offer efficient computational solutions. In particular, identification of meaningful geometric points of such trajectories is still an open research issue. Detection of these critical points implies to identify self-intersecting, turning and curvature points so that specific geometric characteristics that are worth identifying could be denoted. This research introduces an approach called Trajectory Critical Point detection using Convex Hull (TCP-CH) to identify a minimum number of critical points. The results can be applied to large trajectory data sets in order to reduce storage costs and complexity for further data mining and analysis. The main principles of the TCP-CH algorithm include computing: convex areas, convex hull curvatures, turning points, and intersecting points. The experimental validation applied to Geolife trajectory dataset reveals that the proposed framework can identify most of intersecting points in reasonable computing time. Finally, comparison of the proposed algorithm with other methods, such as turning function shows that our approach performs relatively well when considering the overall detection quality and computing time.

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Section: Parameters Related Workmentioning

confidence: 99%

A Geometric Framework for Detection of Critical Points in a Trajectory Using Convex Hulls

Milaghardan

Abbaspour

Claramunt

2018

IJGI

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“…Lee et al [8] proposed an approach that clusters line-segments rst; then, from those clusters sub-trajectories are generated. Pelekis et al [13] introduced a distance metric for trajectories using fuzzy sets to model uncertainty of trajectories and to compute representative, grid-based trajectories for clusters. Morris and Trivedi [9] evaluated the trajectory clustering performance of seven clustering algorithms with six di erent distance functions on six trajectory datasets.…”

Section: Related Workmentioning

confidence: 99%

Mining Spatial Trajectories Using Non-parametric Density Functions

Chen

Eick

Rizk

2011

Machine Learning and Data Mining in Pattern Recognition

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Abstract. Analyzing trajectories is important and has many applications, such as surveillance, analyzing tra c patterns and hurricane path prediction. In this paper, we propose a unique, non-parametric trajectory density estimation approach to obtain trajectory density functions that are used for two purposes. First, a density-based clustering algorithm DENTRAC that operates on such density functions is introduced.Second, unique post-analysis techniques that use the trajectory density function are proposed. Our method is capable of ranking trajectory clusters based on di erent characteristics of density clusters, and thus has the ability to summarize clusters from di erent perspectives, such as the compactness of member trajectories or the probability of their occurrence. We evaluate the proposed methods on synthetic tra c and real-world Atlantic hurricane datasets. The results show that our simple, yet e ective approach extracts valuable knowledge from trajectories that is di cult to obtain with other approaches.

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“…Since the rapidly increased satellites and GPS (Global Position System) technologies have developed, it is possible to collect a large amount of trajectory data of moving objects such as the vehicle position data, hurricane track data, and animal movement data [1,2,16,17]. The analysis over these trajectory data is becoming important for many applications, such as meteorological observation and forecast, animal habits observation, road traffic situation analysis, and navigation in transportations [3][4][5][6]8]. According to the recorded trajectory data and road networks, the moving pattern, traffic situation and road recommendation services can be supported [1,2,12,15,18].…”

Section: Introductionmentioning

confidence: 99%

“…And then, the routes are recommended according to the history trajectory data of the related moving objects. Some schemes generate the density routes of the road networks by analyzing the trajectory data of vehicles [7][8][9][10]. The density regions of the road networks are evaluated by considering both the location and time of the moving objects.…”

Section: Introductionmentioning

confidence: 99%

A Clustering Scheme for Discovering Congested Routes on Road Networks

He¹,

Bok²,

Lim³

et al. 2015

Journal of Electrical Engineering and Technology

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-On road networks, the clustering of moving objects is important for traffic monitoring and routes recommendation. The existing schemes find out density route by considering the number of vehicles in a road segment. Since they don't consider the features of each road segment such as width, length, and directions in a road network, the results are not correct in some real road networks. To overcome such problems, we propose a clustering method for congested routes discovering from the trajectories of moving objects on road networks. The proposed scheme can be divided into three steps. First, it divides each road network into segments with different width, length, and directions. Second, the congested road segments are detected through analyzing the trajectories of moving objects on the road network. The saturation degree of each road segment and the average moving speed of vehicles in a road segment are computed to detect the congested road segments. Finally, we compute the final congested routes by using a clustering scheme. The experimental results showed that the proposed scheme can efficiently discover the congested routes in different directions of the roads.

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Clustering Trajectories of Moving Objects in an Uncertain World

Abstract: Mining Trajectory Databases (TD)

Cited by 66 publications

References 23 publications

A Geometric Framework for Detection of Critical Points in a Trajectory Using Convex Hulls

A Geometric Framework for Detection of Critical Points in a Trajectory Using Convex Hulls

Mining Spatial Trajectories Using Non-parametric Density Functions

A Clustering Scheme for Discovering Congested Routes on Road Networks

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