Transcript Document 7326700

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
What are people doing on the bus?
Comfort does matter!!
How to measure it?
Questionnaire/Interview
Professional Instruments
Problems: Cost, Timeliness, and Scalability
Participatory Phone Sensing
• 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?
Comfort Measurement System
• 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
Our Contributions
• We propose the Comfort Measurement System that
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.
Phone Sensing
• Exploit the GPS and G-sensor (3-axis accelerometer) of
modern smart phones
• Calculate comfort index by following ISO 2631
Weighted Average
comfortable
Acceleration Level
uncomfortable
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 worldwide (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 
Data Mashup
Di = average (
k
k
,
4
)
5
3
5
4
3
2
2
1
1
1
Bus Trajectory
User Trajectory
4
2
3
We suppose the user is on the
b-th bus, s.t. b = arg Min Di
Implementation
4,028 buses, 287 routes,
15 agencies, and 1 sample
per minute
VProbe
http://VProbe.org/TPE-CMS/
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
Results(1/3) Trajectory Matching Results
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.
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.
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).
Thanks for
Your Attention!
http://VProbe.org/
http://VProbe.org/TPE-CMS/