VTrack : Accurate, Energy-Aware Road Traffic Delay Estimation Using Mobile Phones

1. Sensys2009 VTrack: Accurate, Energy-Aware Road Traffic Delay Estimation Using Mobile Phones Speaker:Lawrence

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

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

4. Introdution • 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. Introdution • 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. Introdution • Contribution • Quantitative evaluation of the end to end quality of time estimates from noisy and sparsely sampled locations.

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

8. System Overview • Key Application • Detecting and visualizing hotspots • Real time route planning iPhone web page

9. Requirements • 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. Challenges • Map matching with outages and errors. • Time estimation - even with accurate trajectories is difficult • Localization accuracy is at odd with energy consumption

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

12. Algorithm • HMM • AMarkov 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 transitionprobability.

13. HMM • Hidden state: road segments • Observables: position samples • Transition probability: from one road to next • Emission probability: conditional probability of <segment, position>

14. Match mapping process 1 2 3 4

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

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

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

18. Data collection • Raw data • 800 hours • 25 cars

19. Evaluation of Route Planning WiFi good enough

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

21. Evaluation of Energy Accuracy • 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)

22. Impact of Noise

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

24. Conclusion • 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.