A Comfort Measuring System for Public Transportation Systems Using Participatory Phone Sensing

1. A Comfort Measuring System for Public Transportation Systems Using Participatory Phone Sensing Cheng-Yu Lin1, Ling-Jyh Chen1, Ying-Yu Chen1, and Wang-Chien Lee2 1Academia Sinica, Taiwan 2The Pennsylvania State University at University Park, USA

2. What are people doing on the bus? Comfort does matter!!

3. How to measure it? Professional Instruments Questionnaire/Interview Problems: Cost, Timeliness, and Scalability

4. ParticipatoryPhoneSensing • A new sensing paradigm to exploit the sensing capabilities of modern smart phones to gather, analyze, and share local knowledge of our surroundings(e.g.,CenseMe, SoundSense, Nericell) • It does not rely on dedicated sensing infrastructures and the top-down model of data collection. • It is more penetrative, and encourages participation at personal, social, and urban levels. Question: how about let’s combine the participatory phone sensing and top-down data collection model?

5. ComfortMeasurementSystem • Goal: to evaluate the comfort level of public transportation systems Participants Public Transportation Systems Sensing data (e.g. locations, acceleration, and time) Authorized data (e.g. bus trajectories and vehicle properties) Data Mashup and Statistics Scoring and ranking results

6. Our Contributions • We propose the Comfort Measurement Systemthat exploits participatory phone sensing (bottom-up model) and the authorized data (top-down model). • We prototype a CMS, called TPE-CMS, to evaluate the public bus transportation service in Taipei City. • We conduct a 70-day experience to reveal the insights of the Taipei e-bus system.

7. Phone Sensing • Exploit the GPS and G-sensor (3-axis accelerometer) of modern smart phones • Calculate comfort index by following ISO 2631 Weighted Average Acceleration Level comfortable uncomfortable

8. Authorized Data • No need to reinvent the wheel! • We take advantage of existing real-time bus tracking systems, which are available in many major cities world-wide (e.g., Boston, Cambridge, Seattle, and Taipei). • It contains the bus trajectory, route number, operating agency, and the other useful data. • This may be the most challenge, because you have to talk to the authority 

9. Data Mashup Bus Trajectory 4 5 User Trajectory Di= average ( , ) k k 3 5 4 4 2 3 We suppose the user is on the b-th bus, s.t. b = arg Min Di 2 3 2 1 1 1

10. Implementation 4,028 buses, 287 routes, 15 agencies, and 1 sample per minute VProbe http://VProbe.org/TPE-CMS/

11. Experiments • Period: 2010/03/15 – 2010/07/22 • 15 volunteers • Collect trajectory and vibration traces of Taipei buses using Android phones • Keep a memo of the ground truth (i.e., the agency, route, and license number of their bus rides) • 425 trajectories collected, involving 12 agencies and 3 types buses

12. Results(1/3) - Trajectory Matching Results

13. Results(2/3) -The Statistics based on Buses Types • Light buses are uncomfortable. • No significant difference between the standard buses and the low-floor ones.

14. Results(3/3) - The Statistics based on Buses Agencies • The most comfortable and uncomfortable agencies are exactly the same as the ones reported in the survey made by Taipei Department of Transportation.

15. Conclusions • We present a Comfort Measuring System for public transportation systems, and prototype the system in Taipei city. • The CMS system can be deployed in any cities, as long as there are volunteering participants and there are authorized transportation data available. • Work on analyzing other factors that affect comfort levels is ongoing (e.g., road conditions, drivers’ behavior, and traffic congestion).

16. http://VProbe.org/ http://VProbe.org/TPE-CMS/ Thanks for Your Attention!