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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
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