Trajectory Pattern Mining

1. Trajectory Pattern Mining Fosca Giannotti, Mirco Nanni, Dino Pedreschi, Fabio Pinelli KDD Lab (ISTI-CNR & Univ. Pisa) Presented by: Qiming Zou

2. Overview • Motivations • Trajectory • T-Pattern • Regions of Interest • Future Work • Q&A

3. Motivations • Large number of mobile devices, mobile services available

4. Motivations • It is possible to collect position traces from such devices • We can extract information and patterns from these data to describe mobility behaviors • Use this information for fields such as urban planning

5. Trajectory • Trajectories are sequences that contain the spatial and temporal information about movements

6. Trajectory • Trajectories are usually given as spatiotemporal (ST) sequences: <(x0, y0, t0), ..., (xn, yn, tn)> • xi, yi is the position coordinate relative to the origin • ti is the time stamp for the position information

7. Trajectory • 2D and 3D representation of a trajectory:

8. T-Pattern • A Trajectory Pattern (T-Pattern) is a couple (s, α), where: • s = <(x0, y0),..., (xn, yn)> is a sequence of n+1 locations • α= <α1,..., αn> are the transition times such that αi = Δti = ti– ti-1

9. T-Pattern • A T-Pattern Tp occursin a trajectory if it contains a subsequence S such that: • each (xi, yi) in Tp matches a point (xi’, yi’) in S • the transition times in Tp are similar to those in S

10. T-Pattern • The same exact spatial location (x, y) usually never occurs • Yet, close locations often represent the same place • The same exact transition times usually do not occur often • However, close times often indicate similar behavior

11. T-Pattern • To solve the problem, we introduce the notions of: • Spatial neighborhood: Two points match if one falls within a spatial neighborhood N() of the other • Temporal tolerance: Two transition times match if their temporal difference is ≤ τ

12. T-Pattern • Example:

13. Regions of Interest • It is too computational intensive and yield little practical use to generate all T-Patterns • Solution: Use a Regions of Interest approach, only use these regions as nodes of the T-Patterns

14. Regions of Interest • Given a set of Regions of Interest R, define the neighborhood of (x, y) as: • Neighbors = belong to the same region • Points in no region have no neighbors

15. Regions of Interest S=<(x0, y1, t1), ..., (x4, y4, t4)> => <(R4, t0), (R3, t2), (R3, t3), (R1, t4)>

16. Regions of Interest • What if the Regions of Interests are not known before hand? • Define heuristics for automatic Regions of Interest extraction from data: • Geography-based (crossroads) • Usage-based (popular places) • Mixed (popular squares)

17. Future Work • Application-oriented tests on large, real datasets • Study relations with • Geographic background knowledge • Privacy issues • Reasoning on trajectories and patterns

18. Trajectory Pattern Mining Questions?