1<br />Learning Transportation Mode from Raw GPS Data for Geographic Applications on the Web<br />Yu Zheng, Like Liu, Xing...
2<br />Outline<br />Introduction<br />Framework<br />Methodology<br />Experiment<br />Conclusion & future work<br />
3<br />Background<br />Percentage of GPS-enabled handset among mobile phone<br />(Gartner Dataqueste: Forecast: GPS-enable...
4<br />Introduction<br />What we do: Infer transportation modes from users’ GPS logs<br />GPS log<br />Infer model<br />
5<br />Introduction<br />Motivation<br />Differentiate GPS trajectory of different transportation modes<br />Learning know...
6<br />Introduction<br />Distribution of mean velocity (m/s) of different transportation modes<br />Distribution of maximu...
7<br />Introduction<br />Contributions<br />We propose<br />A change point-based segmentation method<br />An inference mod...
8<br />Framework<br />Preliminary<br />a place where people change<br />their transportation modes<br />
9<br />Framework<br /><ul><li>Inference strategy</li></ul>divide the GPS track into trips and then partition each trip int...
10<br />Framework<br />Post-Processing<br />Segment[i].P(Bike) = Segment[i].P(Bike) * P(Bike|Car)<br />Segment[i].P(Walk) ...
11<br />Framework<br />CRF-Based Inference<br />transportations<br />mode<br />feature from segment<br />
12<br />Methodology<br />Commonsense knowledge from real world<br />Typically, people need to walk before transferring tra...
13<br />Methodology<br />Change point-based Segmentation Algorithm<br />Step 1: using a loose upper bound of velocity (Vt)...
14<br />Experiments<br /><ul><li>Framework of experiment
Feature Extraction
length
mean velocity
expectation of velocity
variance of velocity
top three velocities
top three accelerations</li></li></ul><li>15<br />Experiment<br />Devices<br />Data<br />
16<br />Experiment<br />Evaluation method<br />Precision of inference a segment <br />Accuracy by Length <br />Accuracy by...
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Learning Transportation Mode From Raw Gps Data For Geographic Applications On The Web

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Learning Transportation Mode From Raw Gps Data For Geographic Applications On The Web

  1. 1. 1<br />Learning Transportation Mode from Raw GPS Data for Geographic Applications on the Web<br />Yu Zheng, Like Liu, Xing Xie, WWW’08<br />Microsoft Research Asia<br />Advisor: Chia-Hui Chang<br />Presenter: Teng-Kai Fan<br />Date: 2010-03-19<br />
  2. 2. 2<br />Outline<br />Introduction<br />Framework<br />Methodology<br />Experiment<br />Conclusion & future work<br />
  3. 3. 3<br />Background<br />Percentage of GPS-enabled handset among mobile phone<br />(Gartner Dataqueste: Forecast: GPS-enabled device 2004-2011)<br />
  4. 4. 4<br />Introduction<br />What we do: Infer transportation modes from users’ GPS logs<br />GPS log<br />Infer model<br />
  5. 5. 5<br />Introduction<br />Motivation<br />Differentiate GPS trajectory of different transportation modes<br />Learning knowledge from raw GPS data<br />enable people to absorb more knowledge from others’ life experience<br />Trigger people’s memory about their past<br />Understand people’s life pattern<br />Understanding user behavior<br />Context-aware computing<br />Modeling traffic condition<br />Discover social pattern<br />…<br />Difficulty<br />A trajectory may contain more than two kinds of transportation modes<br />Pure velocity-based method may suffer from congestion<br />
  6. 6. 6<br />Introduction<br />Distribution of mean velocity (m/s) of different transportation modes<br />Distribution of maximum velocity (m/s) of different transportation modes<br />
  7. 7. 7<br />Introduction<br />Contributions<br />We propose<br />A change point-based segmentation method<br />An inference model based on supervised learning<br />A post-processing algorithm based on conditional probability<br />Significance<br />A step toward mining knowledge from raw GPS data for geographic applications on the Web<br />A step toward understanding user behavior based on GPS data<br />Evaluation results<br />Large-scale data collected by 45 people over a period of 6 months<br />Almost 70 percent accuracy<br />
  8. 8. 8<br />Framework<br />Preliminary<br />a place where people change<br />their transportation modes<br />
  9. 9. 9<br />Framework<br /><ul><li>Inference strategy</li></ul>divide the GPS track into trips and then partition each trip into segments by change points<br />
  10. 10. 10<br />Framework<br />Post-Processing<br />Segment[i].P(Bike) = Segment[i].P(Bike) * P(Bike|Car)<br />Segment[i].P(Walk) = Segment[i].P(Walk) * P(Walk|Car)<br />
  11. 11. 11<br />Framework<br />CRF-Based Inference<br />transportations<br />mode<br />feature from segment<br />
  12. 12. 12<br />Methodology<br />Commonsense knowledge from real world<br />Typically, people need to walk before transferring transportation modes<br />Typically, people need to stop and then go when transferring modes<br />Walk should be a transition between different transportation modes<br />Transition matrix of transportation modes<br />
  13. 13. 13<br />Methodology<br />Change point-based Segmentation Algorithm<br />Step 1: using a loose upper bound of velocity (Vt) and acceleration (at) to distinguish all possible Walk Points, non-Walk Points. <br />Step 2: merge short segment (the length less than a thredshold) composed by consecutive Walk Points or non-Walk points<br />Step 3: merge consecutive Uncertain Segment (less than 50 meters) to non-Walk Segment.<br />Step 4: end point of each Walk Segment are potential change points<br />
  14. 14. 14<br />Experiments<br /><ul><li>Framework of experiment
  15. 15. Feature Extraction
  16. 16. length
  17. 17. mean velocity
  18. 18. expectation of velocity
  19. 19. variance of velocity
  20. 20. top three velocities
  21. 21. top three accelerations</li></li></ul><li>15<br />Experiment<br />Devices<br />Data<br />
  22. 22. 16<br />Experiment<br />Evaluation method<br />Precision of inference a segment <br />Accuracy by Length <br />Accuracy by Duration<br />Change Point<br />Precision of change point<br />Recall of change point<br />N: the total number of the segments after being<br />partitioned by a segmentation method.<br />m: # of segments our approach correctly predicted<br />
  23. 23. 17<br />Experiment: Result<br /><ul><li>Inference performance </li></ul>Inferring accuracy of transportation mode over change point-based segmentation method<br />
  24. 24. 18<br />Experiment<br /><ul><li>Inference performance of change point</li></ul>Recall of change point using change point based segmentation method<br />Precision of change point using change point based segmentation method<br />
  25. 25. 19<br />Experiment: Result<br />Comparison of different segmentation methods using Decision Tree<br />
  26. 26. 20<br />Experiment: Result<br />Comparison of inference results of CRF over different segmentation methods<br />
  27. 27. 21<br />Conclusion<br />Segmentation method<br />Inference method<br />SVM<br />Change Point based<br />Bayesian Net<br />Uniform Duration based<br />Decision Tree<br />Uniform Length based<br />CRF<br />
  28. 28. 22<br />Future work<br />Identify more valuable features<br />Location-constraint conditional probability<br />Improving prediction performance of CRF-based approach<br />

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