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

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

3 Introduction and Motivation

4

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

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

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

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

9 Proposal

Fontes [Fontes, 2013] 10

11 Definition: Stop Outlier

Definition – Outlier Segment 12

Definition – Stop Outlier 13

14 Definitions: Event Avoiding Outlier

Definition – Standard Segment 15

Definition - Event Avoiding Outlier 16

17 Definitions: Traffic Avoiding Outlier

Definition – Synchronized Standard Segment 18

Definition – Traffic Avoiding Outlier 19

20 Algorithm

Proposal - Algorithm Main 21

Proposal - Algorithm findEventAvoidingOutlier 22

Proposal - Algorithm findTrafficAvoidingOutlier 23

24 Experimental Results

Taxi trajectories in San Francisco Split trajectories (occupation, weekdays) trajectories with points in total maxDist = 100m minSup = 5% minLength = 10% 25

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

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

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

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

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

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