1. Trajectories Simplification Method for Location-Based Social Networking Services Presenter: Yu Zheng on behalf of Yukun Cheng, Kai Jiang, Xing Xie Microsoft Research Asia March 16, 2009

2. Background 2 GPS devices enable us to record our location history with GPS trajectories Location-based social networking services using web maps Bikely, GeoLife, Sharing life experiences with GPS trajectories Many GPS trajectories are too dense to be displayed Cost lots of hardware resource. Make an application very slow IE browser could crash It is not necessary to show so many details to users

3. Motivation We need to simplify a trajectory from n point to m point (m<<n) Traditional line simplification methods only maintain the skeleton (geospatial shape) information of a trajectory while missing the semantic meanings 3 n points m points

4. 4 A travel route consists of 10,000+ points contains one driving segment and one walking segment

5. 5 A result returned by the DP algorithm A result returned by our method

6. Our Goal TS algorithm: Simplify a trajectory with N points to a m-point one While maintain not only the skeleton information But also the semantic meanings for trajectory sharing 6

7. Framework of TS Partition a trajectory into some segments Assign the headcount of points to each segment Rank the points in a segment according to some factors Retrieve the high-rank points from each segment and formulate a simplified a trajectory 7

8. Preliminaries Distance between a point and its nearest neighbors Heading direction of a point Heading change of a point 8

9. 1. Trajectory Segmentation See our WWW2008 publication for details 9

10. 2. Assign Points to a Segment The weight of a segment depends on The length of a segment The average heading change 10,

11. 3. Ranking Points in a Segment Factors 1: Distance between a point and its nearest neighbors 11 Example 1 Example 2

12. 3. Ranking Points in a Segment Factors 2: Heading change of a point 12 Example 1 Example 2

13. 3. Ranking Points in a Segment Factors 3: Accumulated Heading Change 13

14. Factors Determine the Weigth of a Point Distance between a point and its nearest neighbors Heading change of a point Accumulated Heading Change 14

15. An example of our method 15

16. Experiments GPS trajectory data 335 travel routes generated by 65 users The distance of each trajectory is over 5km On average each trajectory has 2,100+ points, a distance of 63 km and a 4-hour time span 16

17. Evaluation on Effectiveness Average normalized perpendicular: Calculate the perpendicular distance of each point to the simplified results Compute the root mean square value of these perpendicular distance 17

18. Evaluation on Effectiveness 18

19. Evaluation on Effectiveness 19

20. Efficiency The average computing complexity DP: TS : 20

21. Conclusion & Future work Conclusion TS care not only the skeleton information but also the semantic meanings of travel (user behaviors) TS is more effective than DP in simplifying travel routes TS is also more efficient than DP Future work Dynamic simplification: display the top k points in present view Cost lots of online computation 21

22. Thanks! yuzheng@microsoft.com 22