Exploring Spatial-Temporal Trajectory Model for Location Prediction

1. TMSG- Paper Reading Exploring Spatial-Temporal Trajectory Model for Location Prediction 2011.11.23

2. Agenda • Authors & Publication • Paper Presentation • My Comments

3. Authors & Publication • Wen-ChihPeng (彭文志) • http://people.cs.nctu.edu.tw/~wcpeng/ • Advanced Database System Lab • http://db.csie.nctu.edu.tw/ • Best Student Paper Award • IEEE MDM2011 • http://mdmconferences.org/mdm2011/

4. Paper Outline • Introduction • Related works • Framework • Model • Prediction • Experiments • Conclusion

5. Introduction • Location prediction problem • Given an object’s recent movements and a future time, the location of this object at the future time is estimated

6. ? Motivation 11:30 T1勝出!!

7. Related works • Next movement • Markov chain • Motion functions • Granularity problem • Density-based • Grid-based • Pattern recognition • Trajectory mining

8. The framework of location prediction using STT model • Frequent region discovery • Sufficient number of data points • Trajectory transformation • Region-based moving sequence • STT model construction • Probabilistic suffix tree • Transition probability • Appearing probability PST

9. The framework of location prediction using STT model (contd.)

10. Spatial-temporal trajectory model construction • Frequent region discovery and trajectory transformation • Def. 1: Frequent Region • Def. 2: Region-based Moving Sequence • Spatial-temporal trajectory model construction • Predictive table: spatial and temporal correlation between the region and next movement • Transition time interval: ik+1 = (mean, sd) • MinSup: minimal support segment count in a region • Object moving time: Gaussian distribution

11. Frequent region discovery • Eps: the neighborhood number of a given radius • MinTs: minimum number of points

12. Trajectory transformation MinSup = 6 !!

13. Spatial-temporal trajectory model construction

14. STT model

15. Location prediction using STT model • Prediction concept • To find the best next movement literally until the query time is reached • Kernel methods • Movement similarity • Moving potential • Location prediction

16. Movement similarity • To search a best similar node between query sequence and STT node • Measuring the similarity of a labeled sequence of a tree node nk of STT and the moving sequence sq • i is the longest common suffix of nkand sq • The more recent movements have greater effect on future movements • Sq=abc ; Patterns: a(0.07), b(0.27), c(0.64), bc(0.91), ab(0.34)

17. Moving potential • To calculate the next movement candidates of the best similar node located • Measuring the spatial and temporal relationship simultaneously • Prospatial : Conditional probability • Protemporal: Chebyshev’s inequality

18. Moving potential (contd.) • Arrival time te = current time tc + average transition interval mean • Temporal error: Minimum difference of te and the representative time tk+1 of next movement candidates • Example: • Next movement of nk: ik+1=(5,2) • tk+1={12:00, 15:00, 17:00} • If the current time is 11:52 • ================================ • Arrival time = 11:52 + 5 = 11:57 • Minimum temporal error = |11:57-12:00|=3 • Protemporal = (2^2) / (3^2) = 0.44

19. Location prediction

20. Location prediction (contd.) 1 (1x1)

21. Experiments • Experimental setting • Prediction accuracy comparison • Storage requirements comparison • Sensitivity analysis of parameters

22. Experimental setting • CarWeb • http://carweb.cs.nctu.edu.tw/carweb/ • Authors’ work published in 2008 • A real car trajectory dataset • Hsinchu city, Taiwan • RunSaturday • http://www.runsaturday.com • Collect training paths of sports hobbyists • Walk, run, bike

23. Prediction accuracy comparison • E1: To verify the prediction accuracy of STT can be improved by using grid-based clustering approach • STT-Grid vs. STT-DBSCAN • Test 150 queries • Prediction error

24. Prediction accuracy comparison (contd.) • E2: Prediction performance comparison • STT vs. HPM (Hybrid Prediction Model) • An association rule-based pattern prediction approach • Under the various MinTs • Prediction error

25. Storage requirements comparison • HPM dramatically grows with the MinTs • STT using data structure of suffix tree can compress the number of sequential patterns

26. Sensitivity analysis of parameters

27. Sensitivity analysis of parameters (contd.)

28. Sensitivity analysis of parameters (contd.)

29. Sensitivity analysis of parameters (contd.)

30. Conclusion • To discover frequent movement patterns • To answer predictive queries • To reduce the pattern storage size • A spatial-temporal trajectory model • Capture an object’s moving behavior • Forecast its future locations

31. My Comments • Strengths~ • Well paper structure • Well representative illustrations • Abundant experiments • Accuracy + storage + sensitivity • Transition probability + Appearing probability • Be a more sophisticated trajectory formation

32. My comments (contd.) • Weaknesses~ • Too many repeated sentences • No future work suggestions • The definition / interval of the RECENT movement is vague • The sentence (assumption) needs to be verified (by experiments) • “The more recent movements have greater effect on future movements”

33. My comments (contd.) • Doubt~ • Frequent region detection:: Order issue vs. MinSup?

34. My comments (contd.) • Insight~ • Different mobility modes reflect different movement patterns number • Arbitrary vs. Limited • Different prediction design • Reduce patterns number • Promote prediction accuracy

35. Thanks for your listening………..