A Comfort Measuring System for Public Transportation Systems Using Participatory Phone Sensing Cheng-Yu Lin 1, Ling-Jyh Chen 1, Ying-Yu Chen 1, and Wang-Chien Lee 2 1 Academia Sinica, Taiwan 2 The 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 lets combine the participatory phone sensing and top-down data collection model?

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

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.

Exploit the GPS and G-sensor (3-axis accelerometer) of modern smart phones Calculate comfort index by following ISO 2631 Weighted AverageAcceleration Level uncomfortablecomfortable Phone Sensing

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 Authorized Data

Data Mashup User Trajectory Bus Trajectory D i = average (, ) kk We suppose the user is on the b-th bus, s.t. b = arg Min D i

Implementation 4,028 buses, 287 routes, 15 agencies, and 1 sample per minute VProbe

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.

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

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).

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