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

2. Outline
Introduction
System design
Evaluation
Limitations
Conclusion

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

4. Objective Crowd-participated approach Energy friendly 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 , ui and Sj are matching with each other, and mismatching otherwise

15. Bus Classification
𝑓 𝑠 𝑤 = 0.5 𝑤− w: rank of signal strenth penalty cost for mismatches : -0.5

16. Overlapped route Survey 50 bus route Range of cell tower:
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.