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Towards Semantic Trajectory Outlier Detection

Towards Semantic Trajectory Outlier Detection. Artur Ribeiro de Aquino 1 Luis Otavio Alvares 1 Chiara Renso 2 Vania Bogorny 1. 1 Dep. de Matem ática e Estatística – U niversidade Federal de Santa Catarina (UFSC) 2 KDD Lab – Pisa, Italy. Summary. Introduction and Motivation

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Towards Semantic Trajectory Outlier Detection

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  1. Towards Semantic Trajectory Outlier Detection Artur Ribeiro de Aquino1 Luis OtavioAlvares1 Chiara Renso2 Vania Bogorny1 1Dep. de Matemática e Estatística – UniversidadeFederal de Santa Catarina (UFSC) 2KDD Lab – Pisa, Italy

  2. Summary • Introduction and Motivation • Problem • Objective • Proposal • Definition • Algorithm • Experimental Results • Related Works • Conclusion and Future Works

  3. Introduction and Motivation

  4. Introduction and Motivation

  5. Introduction and Motivation • Manytrajectorypatterns • Chasing [Siqueira, 2011] • Frequentmovements[Giannotti, 2007], [Trasarti 2011]; • Meeting, Leadership, Convergence, Recurrence, Flocks [Laube, 2005];

  6. Introduction and Motivation • Some worksfocusedonoutliers • Uncommonbehavior • Example • [Lee, 2008] • [Yuan, 2011] • [Alvares, 2011] • [Fontes, 2013]

  7. Problem • Existing works do not interpret the outliers • Application examples • Publicsafety • Trafficengineering • Slowtraffic • Alternativeroutes

  8. Objective • Extendtheworkof Fontes [Fontes, 2013] • Outlierinterpretation • Semanticclassification • Stop Outliers • EventAvoidingOutliers • TrafficAvoidingOutliers

  9. Proposal

  10. Proposal • Fontes [Fontes, 2013]

  11. Definition:Stop Outlier

  12. Definition – Outlier Segment

  13. Definition – Stop Outlier

  14. Definitions:Event Avoiding Outlier

  15. Definition – Standard Segment

  16. Definition - Event Avoiding Outlier

  17. Definitions:Traffic Avoiding Outlier

  18. Definition – Synchronized Standard Segment

  19. Definition – Traffic Avoiding Outlier

  20. Algorithm

  21. Proposal - Algorithm • Main

  22. Proposal - Algorithm • findEventAvoidingOutlier

  23. Proposal - Algorithm • findTrafficAvoidingOutlier

  24. Experimental Results

  25. 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%

  26. Experimental Results – Stop Outlier • minTime = 15 min • 73 stop outliers • 44:13 min of duration

  27. Experimental Results – Event Avoiding Outlier • Event at Bayshore Freeway (US101) • From 17:30 to 21:30

  28. Experimental 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

  29. Related Works

  30. 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 segment • Outlier classification based on their outlier segments

  31. Towards Semantic Trajectory Outlier Detection Artur Ribeiro de Aquino1 Luis OtavioAlvares1 Chiara Renso2 Vania Bogorny1 1Dep. de Matemática e Estatística – UniversidadeFederal de Santa Catarina (UFSC) 2KDD Lab – Pisa, Italy

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