1. Towards Semantic Trajectory Outlier Detection Artur Ribeiro de Aquino 1 Luis Otavio Alvares 1 Chiara Renso 2 Vania Bogorny 1 1 1 Dep. de Matemática e Estatística – Universidade Federal de Santa Catarina (UFSC) 2 KDD Lab – Pisa, Italy

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

3. 3 Introduction and Motivation

4. 4

5. Many trajectory patterns Chasing [Siqueira, 2011] Frequent movements [Giannotti, 2007], [Trasarti 2011]; Meeting, Leadership, Convergence, Recurrence, Flocks [Laube, 2005]; 5

6. Introduction and Motivation Some works focused on outliers Uncommon behavior Example [Lee, 2008] [Yuan, 2011] [Alvares, 2011] [Fontes, 2013] 6

7. Problem Existing works do not interpret the outliers Application examples Public safety Traffic engineering Slow traffic Alternative routes 7

8. Objective Extend the work of Fontes [Fontes, 2013] Outlier interpretation Semantic classification Stop Outliers Event Avoiding Outliers Traffic Avoiding Outliers 8

9. 9 Proposal

10. Fontes [Fontes, 2013] 10

11. 11 Definition: Stop Outlier

12. Definition – Outlier Segment 12

13. Definition – Stop Outlier 13

14. 14 Definitions: Event Avoiding Outlier

15. Definition – Standard Segment 15

16. Definition - Event Avoiding Outlier 16

17. 17 Definitions: Traffic Avoiding Outlier

18. Definition – Synchronized Standard Segment 18

19. Definition – Traffic Avoiding Outlier 19

20. 20 Algorithm

21. Proposal - Algorithm Main 21

22. Proposal - Algorithm findEventAvoidingOutlier 22

23. Proposal - Algorithm findTrafficAvoidingOutlier 23

24. 24 Experimental Results

25. 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% 25

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

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

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 28

29. Related Works Lee, 2008 Yuan, 2011 Chen, 2011 Alvares, 2011 Fontes, 2013 Proposed Timexx Eventx Subtrajectoryxxxxxx Standardxxx Outlierxxxxx Standard Pathx Outlier Segmentx Standard Segmentx Semanticsx 29

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 30

31. Towards Semantic Trajectory Outlier Detection Artur Ribeiro de Aquino 1 Luis Otavio Alvares 1 Chiara Renso 2 Vania Bogorny 1 31 1 Dep. de Matemática e Estatística – Universidade Federal de Santa Catarina (UFSC) 2 KDD Lab – Pisa, Italy