Cooperative Transit Tracking using Smart-phones

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Cooperative Transit Tracking using Smart-phones

  1. 1. COOPERATIVE TRANSIT TRACKING USING SMART- PHONES ESOE R98525087 李孟翰 ESOE R99525045 郭羿呈 Arvind Thiagarajan MIT CSAIL James Biagioni Tomas Gerlich Jakob Eriksson University of Illinois at Chicago SenSys’10, November 3–5, 2010, Zurich, Switzerland. Copyright 2010 ACM 978-1-4503-0344-6/10/11 ...$10.00
  2. 2. Outline  Introduction  Motivation  Proposed Method  System Overview  Activity Classification by Accelerometer  Spatio-temporal Trajectory Matching  Tracking Underground Transit  Performance Evaluation  Conclusion 2
  3. 3. Outline  Introduction  Motivation  Proposed Method  System Overview  Activity Classification by Accelerometer  Spatio-temporal Trajectory Matching  Tracking Underground Transit  Performance Evaluation  Conclusion 3
  4. 4. 車快到 了~^^ &$*@ #$*%) Polly’s story 月底省 點錢 4
  5. 5. Why ? 發送訊息 等待公車 更新資料 5
  6. 6. Why ? Figure 2. GPS trace and an actual trajectory of a bus ride downtown Chicago 6
  7. 7. Why ? Figure 3. CDF of GPS localization errors for downtown and suburban environments 7
  8. 8. Why ? Figure 1. Measured difference between scheduled and actual arrival times of buses in Chicago 8
  9. 9. Outline  Introduction  Motivation  Proposed Method  System Overview  Activity Classification by Accelerometer  Spatio-temporal Trajectory Matching  Tracking Underground Transit  Performance Evaluation  Conclusion 9
  10. 10. Motivation  Provide more precise way for tracking services.  Other issues need to be considered. energy efficiency Activity Classification Tracking Underground Arvind Thiagarajan MIT CSAIL James Biagioni Tomas Gerlich Jakob Eriksson University of Illinois at Chicago Cooperative Transit Tracking using Smart-phones 10
  11. 11. Outline  Introduction  Motivation  Proposed Method  System Overview  Activity Classification by Accelerometer  Spatio-temporal Trajectory Matching  Tracking Underground Transit  Performance Evaluation  Conclusion 11
  12. 12. System Overview Figure 4. Cooperative transit tracking system 12
  13. 13. Outline  Introduction  Motivation  Proposed Method  System Overview  Activity Classification by Accelerometer  Spatio-temporal Trajectory Matching  Tracking Underground Transit  Performance Evaluation  Conclusion 13
  14. 14. Activity Classification by Accelerometer14
  15. 15. Outline  Introduction  Motivation  Proposed Method  System Overview  Activity Classification by Accelerometer  Spatio-temporal Trajectory Matching  Tracking Underground Transit  Filtering out spurious stops  Performance Evaluation  Conclusion 15
  16. 16. Spatio-temporal Trajectory Matching Sequential GPS Location Check slide windows and return only one bus number Car Bus Unknown INPUT OUTPU T Outlier Removal Least Squares Minimization Post Processing Schedule Deviation Overlapping Routes YE S YE S YE S NO NO NO 16
  17. 17. Spatio-temporal Trajectory Matching  Other Exception Check  Stopping Check  Need more than 3 stops, and each stop remains exceeds 15 sec.  Inter-Stop Distance CDF  Overlapping Routes Check  RMES > τ and Slide Windows more than 1 possible buses  confidence cutoff(CC)  Low: quick real-time tracking  High: more precise route map Figure 8. Inter-Stop Distance CDF for buses and cars. 17
  18. 18. Outline  Introduction  Motivation  Proposed Method  System Overview  Activity Classification by Accelerometer  Spatio-temporal Trajectory Matching  Tracking Underground Transit  Performance Evaluation  Conclusion 18
  19. 19. Tracking Underground Transit  Schedule-based hidden Markov mode(HMM) Set of emission score (ES) INPUT Check States Check the condition (con) of “stopped in tunnel” State is moving ? Is con satisfied? Moving in tunnel Stopped in tunnel Stopped at station OUTPU T States Transitions Emission detector YE S NO YE S NO 19
  20. 20. Tracking Underground Transit  States Transitions  Emission detector Figure 10. HMM for accelerometer and schedule-based subway tracking 20
  21. 21. Tracking Underground Transit  Other issues need to be considered : Filtering out spurious stops in tunnel. Figure 9. Detecting bus mobility by 21
  22. 22. Outline  Introduction  Motivation  Proposed Method  System Overview  Activity Classification by Accelerometer  Spatio-temporal Trajectory Matching  Tracking Underground Transit  Performance Evaluation  Conclusion 22
  23. 23. Activity Classifier Accuracy Table 3. Walking detection accuracy on a variety of labeled test traces. 23
  24. 24. Trajectory Matching Accuracy Figure 12. Prec./recall vs Confidence Cutoff, CTA Data. Figure 13. CDF of Decision Time, CTA Da 24
  25. 25. Table 4. Transit matching on car traces along or near known bus routes. Trajectory Matching Accuracy 25
  26. 26. Tracking Accuracy in Subway Figure 15. Comparison of estimated, scheduled and actual arrival time at each station. 26
  27. 27. Utility of Cooperative Transit Tracking Figure 17. Wait time vs. Penetration level using Cooperative Transit Tracking Only. Figure 16. Wait time vs. Penetration level using Cooperative Transit Tracking with fallback on schedule. 27
  28. 28. Utility of Cooperative Transit Tracking Figure 18. Requests served vs. Penetration level. 28
  29. 29. Outline  Introduction  Motivation  Proposed Method  System Overview  Activity Classification by Accelerometer  Spatio-temporal Trajectory Matching  Tracking Underground Transit  Performance Evaluation  Conclusion 29
  30. 30. Conclusion  Cooperative transit tracking that combines  power-efficient activity detection using accelerometer data  memory-efficient spatio-temporal bus trajectory matching using least squares minimization  accelerometer in conjunction with a Hidden Markov model to track underground trains when other localization schemes do not work. 30
  31. 31. Q&A 31

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