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Learning Transportation Mode from Raw GPS Data for Geographic Applications on the Web

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. Outline. Introduc tion F ramework Methodology E xperiment Conclusion & future work. Outline. Introduc tion F ramework

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Learning Transportation Mode from Raw GPS Data for Geographic Applications on the Web

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

  13. Framework • CRF-Based Inference

  14. Outline • Introduction • Framework • Methodology • Experiment • Conclusion & future work

  15. Methodology Transition matrix of transportation modes • 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

  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 • Inference performance Inferring accuracy of transportation mode over change point-based segmentation method

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

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

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

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

  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

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