1. Learning Transportation Mode from Raw GPS Data for Geographic Applications on the Web Yu Zheng, Like Liu, Xing Xie yuzheng@microsoft.com Microsoft Research Asia

2. Outline Introduction Framework Methodology Experiment Conclusion & future work

3. Outline Introduction Framework Methodology Experiment Conclusion & future work

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

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

6. 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

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

8. 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

9. Outline Introduction Framework Methodology Experiment Conclusion & future work

10. Framework Preliminary

11. Framework Inference strategy

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

13. Framework CRF-Based Inference

14. Outline Introduction Framework Methodology Experiment Conclusion & future work

15. 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 Transportation modes WalkCarBusBike Walk/53.4%32.8%13.8% Car95.4%/2.8%1.8% Bus95.2%3.2%/1.6% Bike98.3%1.7%0%/ Transition matrix of transportation modes

16. Methodology Change point-based Segmentation Algorithm – Step 1: distinguish all possible Walk Points, non-Walk Points. – Step 2: merge short segment composed by consecutive Walk Points or non-Walk points – Step 3: merge consecutive Uncertain Segment to non-Walk Segment. – Step 4: end point of each Walk Segment are potential change points

17. Outline Introduction Framework Methodology Experiment Conclusion & future work

18. Experiments Framework of experiment Feature Extraction – length – mean velocity – expectation of velocity – variance of velocity – top three velocities – top three accelerations

19. Experiment Devices Data

20. Experiment Evaluation method – Precision of inference a segment Accuracy by Length Accuracy by Duration – Change Point Precision of change point Recall of change point

21. Experiment: Result Inferring accuracy of transportation mode over change point-based segmentation method Inference performance

22. Experiment Recall of change point using change point based segmentation method Precision of change point using change point based segmentation method Inference performance of change point

23. Experiment: Result change point uniform duration (120 s) uniform length (100 m) Accuracy by Length0.685 0.647 0.399 Accuracy by Duration0.753 0.701 0.674 Recall/change point0.887 0.867 Precision/change point0.406 0.197 0.148 Comparison of different segmentation methods using Decision Tree

24. Experiment: Result Comparison of inference results of CRF over different segmentation methods change point uniform duration (90 s) uniform length (150 m) Accuracy by Length0.528 0.5240.617 Accuracy by Duration0.358 0.4130.525 Recall/ change point0.281 0.1210.656 Precision /change point0.286 0.0700.159

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

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

27. Thanks! Q&A Yu Zheng @ Microsoft