Predestination: Inferring Destinations from Partial Trajectories

1. Predestination: Inferring Destinations from Partial Trajectories John Krumm and Eric Horvitz Presented by Amy Palmer

2. Predestination • Aim • Predicting a driver’s destination as their trip progresses • Previous work • Predicting routes based on GPS information • New ideas • Predicting destination, not route, using a fully tiled map rather than a few discrete locations

3. Experiment • Create a 40x40 grid on a map • Each cell is 1 km x 1 km • N = 1600; i = 1,2,3,….,N • Probabilistic Model • P(D = i | X = x) • D: destination • X: observed features

4. Experiment • Simple Close-World Model • Pclosed(D = i) • Based on where the driver has been • Open-World Model • Popen(D=i) = (1-α-β)Pclosed(D=i)+αW(D=i)+βPG(D=i) • Allows exploration of new places • Only uses info from before the test day • Complete data Model • Uses info from all days

5. Experiment • Simple Close-World Model • Pclosed(D = i) • Based on where the driver has been • Open-World Model • Popen(D=i) = (1-α-β)Pclosed(D=i)+αW(D=i)+βPG(D=i) • Allows exploration of new places • Only uses info from before the test day • Complete data Model • Uses info from all days

6. Close-World Model • Pclosed(D=i); i = 1,2,3,…,N • Plot histogram of all N • Normalize

7. Experiment • Simple Close-World Model • Pclosed(D = i) • Based on where the driver has been • Open-World Model • Popen(D=i) = (1-α-β)Pclosed(D=i)+αW(D=i)+βPG(D=i) • Allows exploration of new places • Only uses info from before the test day • Complete data Model • Uses info from all days

8. Open World Model Popen(D=i) = (1-α-β)Pclosed(D=i) + αW(D=i) + βPG(D=i)

9. Open World Model Popen(D=i) = (1-α-β)Pclosed(D=i) + αW(D=i) + βPG(D=i)

10. Open World Model Popen(D=i) = (1-α-β)Pclosed(D=i) + αW(D=i) + βPG(D=i) Closed world prior Discrete Probability Distribution Ground Cover Probability Weighting Factors

11. Open World Model Popen(D=i) = (1-α-β)Pclosed(D=i) + αW(D=i) + βPG(D=i) Closed world prior Discrete Probability Distribution Ground Cover Probability Weighting Factors

12. Open World Model Popen(D=i) = (1-α-β)Pclosed(D=i) + αW(D=i) + βPG(D=i) Closed world prior Discrete Probability Distribution Ground Cover Probability Weighting Factors

13. Open World Model Popen(D=i) = (1-α-β)Pclosed(D=i) + αW(D=i) + βPG(D=i) Closed world prior Discrete Probability Distribution Ground Cover Probability Weighting Factors

14. Open World Model Popen(D=i) = (1-α-β)Pclosed(D=i) + αW(D=i) + βPG(D=i) Closed world prior Discrete Probability Distribution Ground Cover Probability Weighting Factors

15. Open World Model Popen(D=i) = (1-α-β)Pclosed(D=i) + αW(D=i) + βPG(D=i) Closed world prior Discrete Probability Distribution Ground Cover Probability Weighting Factors

16. Open World Model Popen(D=i) = (1-α-β)Pclosed(D=i) + αW(D=i) + βPG(D=i) Closed world prior Discrete Probability Distribution Ground Cover Probability Weighting Factors • United States Geological Survey (USGS) • PG(D=i) = ΣP(D = i | G = j) Pi(G = j)

17. Open World Model Popen(D=i) = (1-α-β)Pclosed(D=i) + αW(D=i) + βPG(D=i) Closed world prior Discrete Probability Distribution Ground Cover Probability Weighting Factors • United States Geological Survey (USGS) • PG(D=i) = ΣP(D = i | G = j) Pi(G = j)

18. Open World Model Popen(D=i) = (1-α-β)Pclosed(D=i) + αW(D=i) + βPG(D=i) Closed world prior Discrete Probability Distribution Ground Cover Probability Weighting Factors

19. Open World Model Popen(D=i) = (1-α-β)Pclosed(D=i) + αW(D=i) + βPG(D=i) Closed world prior Discrete Probability Distribution Ground Cover Probability Weighting Factors

20. Open World Model Popen(D=i) = (1-α-β)Pclosed(D=i) + αW(D=i) + βPG(D=i) Closed world prior Discrete Probability Distribution Ground Cover Probability Weighting Factors

21. Open World Model Popen(D=i) = (1-α-β)Pclosed(D=i) + αW(D=i) + βPG(D=i) Closed world prior Discrete Probability Distribution Ground Cover Probability Weighting Factors

22. Open World Model Popen(D=i) = (1-α-β)Pclosed(D=i) + αW(D=i) + βPG(D=i) • Closed world prior • Discrete Probability Distribution • Ground Cover • Probability Weighting Factors

23. Open World Model Popen(D=i) = (1-α-β)Pclosed(D=i) + αW(D=i) + βPG(D=i) • Closed world prior • Discrete Probability Distribution • Ground Cover • Probability Weighting Factors

24. Open World Model Popen(D=i) = (1-α-β)Pclosed(D=i) + αW(D=i) + βPG(D=i) • Closed world prior • Discrete Probability Distribution • Ground Cover • Probability Weighting Factors

25. Open World Model Popen(D=i) = (1-α-β)Pclosed(D=i) + αW(D=i) + βPG(D=i) • Closed world prior • Discrete Probability Distribution • Ground Cover • Probability Weighting Factors

26. Experiment • Simple Close-World Model • Pclosed(D = i) • Based on where the driver has been • Open-World Model • Popen(D=i) = (1-α-β)Pclosed(D=i)+αW(D=i)+βPG(D=i) • Allows exploration of new places • Only uses info from before the test day • Complete data Model • Uses info from all days

27. Driving Efficiency Microsoft MapPoint N(N-1) ordered pairs Effect of trip time Combine with Bayes’ Rule

28. Driving Efficiency Microsoft MapPoint N(N-1) ordered pairs Effect of trip time Combine with Bayes’ Rule

29. Driving Efficiency • Microsoft MapPoint • N(N-1) ordered pairs • Effect of trip time • Combine with Bayes’ Rule

30. Driving Efficiency • Microsoft MapPoint • N(N-1) ordered pairs • Effect of trip time • Combine with Bayes’ Rule

31. Driving Efficiency • Microsoft MapPoint • N(N-1) ordered pairs • Effect of trip time • Combine with BayesRule

32. Results Complete Open-world Closed-world

33. Future Directions • Increasing the grid resolution • Learning specific places for individuals • Incorporating temporal data

34. Future Directions • Increasing the grid resolution • Learning specific places for individuals • Incorporating temporal data

35. Future Directions • Increasing the grid resolution • Learning specific places for individuals • Incorporating temporal data