towards semantic trajectory outlier detection artur ribeiro de aquino 1 luis otavio alvares 1 chiara...

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Towards Semantic Trajectory Outlier

DetectionArtur Ribeiro de Aquino1

Luis Otavio Alvares1

Chiara Renso2

Vania Bogorny1

1Dep. de Matemática e Estatística – Universidade Federal de Santa Catarina (UFSC)

2KDD Lab – Pisa, Italy

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Summary

Introduction and Motivation

Problem

Objective

Proposal Definition Algorithm

Experimental Results

Related Works

Conclusion and Future Works

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Introduction and Motivation

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Introduction and Motivation

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Introduction and Motivation

Many trajectory patternsChasing [Siqueira, 2011]Frequent movements [Giannotti, 2007], [Trasarti

2011];Meeting, Leadership, Convergence, Recurrence,

Flocks [Laube, 2005];

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Introduction and Motivation

Some works focused on outliersUncommon behavior

Example [Lee, 2008] [Yuan, 2011] [Alvares, 2011] [Fontes, 2013]

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Problem

Existing works do not interpret the outliers

Application examplesPublic safetyTraffic engineering

Slow traffic Alternative routes

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Objective

Extend the work of Fontes [Fontes, 2013]

Outlier interpretation

Semantic classificationStop OutliersEvent Avoiding OutliersTraffic Avoiding Outliers

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Proposal

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Proposal

Fontes [Fontes, 2013]

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Definition:Stop Outlier

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Definition – Outlier Segment

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Definition – Stop Outlier

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Definitions:Event Avoiding Outlier

15Definition – Standard Segment

16Definition - Event Avoiding Outlier

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Definitions:Traffic Avoiding Outlier

18Definition – Synchronized Standard Segment

19Definition – Traffic Avoiding Outlier

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Algorithm

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Proposal - Algorithm

Main

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Proposal - Algorithm

findEventAvoidingOutlier

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Proposal - Algorithm

findTrafficAvoidingOutlier

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Experimental Results

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Experimental Results

Taxi trajectories in San Francisco

Split trajectories (occupation, weekdays)

537.098 trajectories with 6.314.120 points in total

maxDist = 100m

minSup = 5%

minLength = 10%

26Experimental Results – Stop Outlier

minTime = 15 min

73 stop outliers

44:13 min of duration

27Experimental Results – Event Avoiding Outlier

Event at Bayshore Freeway (US101)

From 17:30 to 21:30

28Experimental Results – Traffic Avoiding Outlier

timeTol = 15 min

6 traffic avoiding outliers

Synchronized standard segments (avg): 7:05 min

Fastest standard segments (avg): 3:30 min

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Related Works

Lee, 2008

Yuan, 2011

Chen,

2011

Alvares,

2011

Fontes,

2013

Proposed

Time x x

Event x

Subtrajectory x x x x x x

Standard x x x

Outlier x x x x x

Standard Path x

Outlier Segment x

Standard Segment x

Semantics x

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Conclusion and Future Works

Lack of interpretation on previous approaches

New concepts were provided aiming the semantics

Cases found were correctly interpreted

Future…Weight to each outlier segmentOutlier classification based on their outlier segments

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Towards Semantic Trajectory Outlier

DetectionArtur Ribeiro de Aquino1

Luis Otavio Alvares1

Chiara Renso2

Vania Bogorny1

1Dep. de Matemática e Estatística – Universidade Federal de Santa Catarina (UFSC)

2KDD Lab – Pisa, Italy