1. 1 Learning Transportation Mode from Raw GPS Data for Geographic Applications on the Web Yu Zheng, Like Liu, Xing Xie, WWW’08 Microsoft Research Asia Advisor: Chia-Hui Chang Presenter: Teng-Kai Fan Date: 2010-03-19

2. 2 Outline Introduction Framework Methodology Experiment Conclusion & future work

3. 3 Background Percentage of GPS-enabled handset among mobile phone (Gartner Dataqueste: Forecast: GPS-enabled device 2004-2011)

4. 4 Introduction What we do: Infer transportation modes from users’ GPS logs GPS log Infer model

5. 5 Introduction Motivation Differentiate GPS trajectory of different transportation modes Learning knowledge from raw GPS data enable people to absorb more knowledge from others’ life experience Trigger people’s memory about their past Understand people’s life pattern Understanding user behavior Context-aware computing Modeling traffic condition Discover social pattern … Difficulty A trajectory may contain more than two kinds of transportation modes Pure velocity-based method may suffer from congestion

6. 6 Introduction Distribution of mean velocity (m/s) of different transportation modes Distribution of maximum velocity (m/s) of different transportation modes

7. 7 Introduction Contributions We propose A change point-based segmentation method An inference model based on supervised learning A post-processing algorithm based on conditional probability Significance A step toward mining knowledge from raw GPS data for geographic applications on the Web A step toward understanding user behavior based on GPS data Evaluation results Large-scale data collected by 45 people over a period of 6 months Almost 70 percent accuracy

8. 8 Framework Preliminary a place where people change their transportation modes

10. 10 Framework Post-Processing Segment[i].P(Bike) = Segment[i].P(Bike) * P(Bike|Car) Segment[i].P(Walk) = Segment[i].P(Walk) * P(Walk|Car)

11. 11 Framework CRF-Based Inference transportations mode feature from segment

12. 12 Methodology Commonsense knowledge from real world Typically, people need to walk before transferring transportation modes Typically, people need to stop and then go when transferring modes Walk should be a transition between different transportation modes Transition matrix of transportation modes

13. 13 Methodology Change point-based Segmentation Algorithm Step 1: using a loose upper bound of velocity (Vt) and acceleration (at) to distinguish all possible Walk Points, non-Walk Points. Step 2: merge short segment (the length less than a thredshold) composed by consecutive Walk Points or non-Walk points Step 3: merge consecutive Uncertain Segment (less than 50 meters) to non-Walk Segment. Step 4: end point of each Walk Segment are potential change points

15. Feature Extraction

16. length

17. mean velocity

18. expectation of velocity

19. variance of velocity

20. top three velocities

22. 16 Experiment Evaluation method Precision of inference a segment Accuracy by Length Accuracy by Duration Change Point Precision of change point Recall of change point N: the total number of the segments after being partitioned by a segmentation method. m: # of segments our approach correctly predicted

25. 19 Experiment: Result Comparison of different segmentation methods using Decision Tree

26. 20 Experiment: Result Comparison of inference results of CRF over different segmentation methods

27. 21 Conclusion Segmentation method Inference method SVM Change Point based Bayesian Net Uniform Duration based Decision Tree Uniform Length based CRF

28. 22 Future work Identify more valuable features Location-constraint conditional probability Improving prediction performance of CRF-based approach