1. Modeling of Human Movement Behavioral Knowledge from GPS Traces for Categorizing Mobile Users
Sourse: Www 2017
Advisor: Jia-Ling Koh
Speaker: Hsiu-Yi,Chu
Date: 2017/11/7

2. Outline
Introduction
Method
Experiment
Conclusion

3. Introduction Question
Can we map the knowledge of one known region to another unknown(target) region and use this knowledge to categorize the users in the target region?

4. Introduction Goal Labeled GPS log Unlabeled GPS log
Knowledge
User category label

5. Outline
Introduction
Method
Experiment
Conclusion

6. Method

7. Method User Trajectory Segment <S[], W[], Traj_Win[]>
S[]: list of stay points, s = <lat, lon, Geotagg>
W[]: list of waiting points, w = <lat,lon>
Traj_Win[]: {S1, (x1,x2), (x2,y2), … ,S2}

8. Method
User Trajectory Segment

9. Method Semantic Stay Point Taxonomy(SSPTaxonomy)
SSPTaxonomy:<N, Nc, W>
N: place type of the Taxonomy
Nc: associated code of the node place
W: aggregated footprints of user

10. Method
Semantic Stay Point Taxonomy(SSPTaxonomy)

11. Method User-Trace Summary(UTS) NB = <G,Θ>,G=<V,E>
v1i: (Nc,ti)
Nc: associated code of the node place
ti: temporal value of the node
ei: dependences between the vertices

12. Method
User-Trace Summary(UTS)
Bayesian Network

13. Method
Θx5|Pax5 = 0.46

14. Method
Θx4|Pax4*Θx5|Pax5*Θx2|Pax2 =
0.6*0.46*0.98=

15. Method Temporal Common Sub-sequence, (TempCS)clustering algorithm
Similarity measure(Bhattacharyya distance):
DB(X4, X5)=-ln{[X4(0)X5(0)]1/2+[X4(1)X5(1)]1/2}=
-ln{[0.4*(0.4* *0.54)]1/2+[0.6*(0.4* *0.46)]1/2}

16. Method Temporal Common Sub-sequence, (TempCS)clustering algorithm
NB1:X4X3X5X1X2
NB2:X4X1X6X5X2
Common stay points(Lc):X4X5X2X1
Common Sub-sequence(Ls):X4X5X2

17. Method Similarity between NB1and NB2: SimSequence(NB1,NB2)=
3/4[DB(X4, X5)+ DB(X5, X2)]

18. Method User Categorization Classification task: PVu = {p1,p2,…,pi}
i: user-category
pi: probability of the user u in category i

19. Method User Categorization Feature f1: visit in types of places
f2: Speed of movement or transportation mode
f3: User Movement

20. Method User Categorization Bayesian network When independent
Weighting each of feature

21. Method
Transfer Learning

22. Method Transfer Learning Extract the parent’s code cp of a node c.
Node c has n sibling, append n+1 along with the parent’s code cpn+1.
Check whether the same place-type in and assign the same code if present.
Generate
Get the common taxonomy

23. Outline
Introduction
Method
Experiment
Conclusion

24. Experiment
Dataset

25. Experiment
Accuracy of User-Classification

26. Experiment

27. Outline
Introduction
Method
Experiment
Conclusion

28. Conclusion
Address the user categorization problem from the GPS traces of the users.
Propose a framework to model individual’s movement patterns.
Transfer knowledge base from one city domain to another unknown city.