How Long to Wait?: Predicting Bus Arrival Time with Mobile Phone based Participatory Sensing

1. How Long to Wait?: Predicting Bus Arrival Time with Mobile Phone based Participatory Sensing Pengfei Zhou, YuanqingZheng, Mo Li -twohsien 2012.9.3

2. Outline • Introduction • System design • Evaluation • Limitations • Conclusion

3. Introduction • Why travelers do not like to travel by bus? • Excessively long waiting time • Existing methods to predict arrival time • Timetable ( operating hours, time intervals, etc.) • Special location tracking devices on buses Who will pay for this? $$$$$$$$$$$$

4. Objective • Crowd-participated approach • Sharing users • Querying users • Backend server • Energy friendly • Microphone, accelerometer Mobile Phone

5. Main idea • Map the bus routes to a space featured by sequences of nearby cellular towers

6. Challenges • Bus Detection • Bus Classification • Information Assembling

7. System Design

8. Pre-processing Celltower Data Top-3 strongest cell towers 300 meters apart

9. Example

10. Bus Detection • Audio detection : short beep audio response Peak at 1 kHz and 3kHz

11. Bus Detection • Sliding window, size: 32 samples • Empirical threshold: three standard deviation

12. Bus Detection • Accelerometer detection • Bus v.s. Rapid train

13. Bus Detection • Threshold • Small: trains will be misdetected as buses • Big: miss detection of actual buses

14. Bus Classification • Cell tower sequence matching • Smith-Waterman algorithm • If ui= Cw∈ Sj , uiand Sjare matching with each other, and mismatching otherwise

15. Bus Classification • w: rank of signal strenthpenalty cost for mismatches : -0.5

16. Overlapped route • Survey 50 bus route Range of cell tower: 300-900 meters threshold of celltower sequence length : 7

17. Cell tower Sequence Concatenation

18. Arrival Time Prediction

19. Evaluation

20. Experimental Methodology • Mobile phones • Samsung Galaxy S2 i9100 • HTC Desire • Experiment environment • 4 campus shuttle bus routes • 2 SBS transit bus route 179 and 241

21. Bus Detection Performance

22. Bus vs. MRT Train False detection: Driving along straight routes late during night time

23. Bus Classification Performance

24. Arrival Time Prediction

25. Arrival Time Prediction

26. System Overhead • Battery lifetime

27. Limitation and On-going Work • Alternative reference points • Number of passengers • First few bus stops • Overlapped routes

28. Conclusion • Present a crowd-participated bus arrival time prediction system using commodity mobile phones. • Evaluate the system through a prototype system deployed on the Android platform with two types of mobile phones.