1. Sensys 2009 Speaker:Lawrence

2.  Introduction  Overview & Challenges  Algorithm  Travel Time Estimation  Evaluation  Conclusion

3.  Introduction  Overview & Challenges  Algorithm  Travel Time Estimation  Evaluation  Conclusion

4.  Motivation  Traffic delays and congestions  Real time traffic information  Challenges  Energy consumption  Inaccurate position samples  VTrack  Vehicles as probes  A real time traffic monitoring system  Motivating Problem  How the quality of VTrack’s travel time estimates on the sensor being sampled and the sampling frequency.

5.  Key finding  HHM-based map matching is robust to noise  Travel times estimated from WiFi localization alone are accurate enough for route planning  Travel times estimated from WiFi localization alone cannot detect hotspots accurately  Sampling GPS periodically to save power

6.  Contribution  Quantitative evaluation of the end to end quality of time estimates from noisy and sparsely sampled locations.

7.  Introduction  Overview & Challenges  Algorithm  Travel Time Estimation  Evaluation  Conclusion

8.  Key Application  Detecting and visualizing hotspots  Real time route planning iPhone web page

9.  Accuracy  For route planning, errors in the 10%~15% range.  Efficient enough to run in real time  Some existing map-matching algorithm run A* style shortest path algorithm  Energy efficient  GPS excessively drains the battery

10.  Map matching with outages and errors.  Time estimation - even with accurate trajectories is difficult  Localization accuracy is at odd with energy consumption

11.  Introduction  Overview & Challenges  Algorithm  Travel Time Estimation  Evaluation  Conclusion

12.  HMM  A Markov process with a set of hidden states and observables.  Viterbi Decoding  Dynamic programming tech  Find the maximum likelihood sequence of hidden states given a set of observables and emission probability and transition probability.

13.  Hidden state: road segments  Observables: position samples  Transition probability: from one road to next  Emission probability: conditional probability of

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15.  Introduction  Overview & Challenges  Algorithm  Travel Time Estimation  Evaluation  Conclusion

16.  The traversal time T(s) for any segment S:  Estimation Errors  Outages during transition times. ▪ Intersection delay  Noisy position samples ▪ Noisy sensor

17.  Introduction  Overview & Challenges  Algorithm  Travel Time Estimation  Evaluation  Conclusion

18.  Raw data  800 hours  25 cars

19. WiFi good enough

20. Detect 80%~90% of hotspots. Not too aggressive.

21.  Estimating WiFi Cost  The cost per sample of GPS is 24.9X the cost per sample of WiFi.  8% of total power consumption  Offline Energy Optimization (Assuming the WiFi cost is 1 unit)

23.  Introduction  Overview & Challenges  Algorithm  Travel Time Estimation  Evaluation  Conclusion

24.  Using mobile phones to accuracy estimate travel times using inaccurate samples.  Address key challenge  1. reducing energy consumption  2. accurate travel time from inaccurate rate positions  VTrack uses an HMM-based map matching scheme.  Successfully identify highly delayed segments and accuracy route planning with noisy.