1. A Study of Comfort Measuring System Using Taxi Trajectories Li-Ping Tung 1, Tsung-Hsun Chien 2,3, Ting-An Wang 3, Cheng-Yu Lin 3, Shyh-Kang Jeng 2, and Ling-Jyh Chen 3 1 National Chiao Tung University, Taiwan 2 National Taiwan University, Taiwan 3 Academia Sinica, Taiwan 1

2. Introduction The comfort or rides has been identified as one of the top criteria that affects passengers’ satisfactory with public transportation system. 2 Comfort does matter!!

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

4. Internet of Things The idea of IoT is to interconnect state-of-the-art digital products in physical world to provide more powerful applications.  intelligent transportation systems  remote healthcare systems  smart grid systems 4

5. Vehicles and the Trajectory Data Vehicles are view as parts of Internet of Things  GPS devices allow recording the movement track of moving vehicles.  The collected trajectory data could be real-time transmitted to the data server via wireless technologies, such as WiMAX and 4G LTE. Applications of trajectory data  provide passengers with the expected trip time and fare of a given itinerary  predict driving directions  supervise urban traffic or serve location-based services 5

6. Comfort Measuring System Exploit the GPS data Calculate the comfort index by following ISO 2631 Comfort Score: 20 x (6 – CI) 6 Acceleration Level uncomfortablecomfortable

7. Taxi Trajectory Dataset One of the Taipei service providers Duration: 2010/11/8~2010/11/28 Objects: 200,000 trajectories among about 700 taxis 7 fielddata typedescription idintsequence number micmacchartaxi number longitudedoublelongitude of trajectory latitudedoublelatitude of trajectory speeddoubledriving speed datatime driving time clientontaxiboolload/uoload

8. Statistical Results of Dataset (1) 8 Among 24 Hours Among a WeekAmong 24 Hours

9. Statistical Results of Dataset (2) 9 85% is under 30 minutes  for passengers saving time low fare  for drivers risk of no load in the returning trip 9 Trip Time Driving Time

10. Comfort Scores in CDF Distribution 10 comfortable

11. Comfort Scores Analysis - Day and Night 11 without passengerswith passengers Comfort Scores: 1. day > night 2. w/o passengers > w/ passengers

12. Comfort Scores Analysis – Trip Time 12

13. Comfort Scores Analysis – Trip Distance 13

14. Ranking of Load among a Day Ranking lists according to some criteria  number of loads  comfort score 14

15. Ranking of Comfort Score 15 The 10 BEST The 10 WORST

16. Implication from Ranking Lists Track back to the trajectories to understand what happened  drivers’ driving behaviors  road conditions  traffic conditions 16

17. Conclusions We present a Comfort Measuring System for vehicles equipped with GPS devices.  It shows that comfort level varies with trip time/distance w/ and w/o passengers Ranking lists according to comfort score and number of loads Work on spatial-temporal analysis is ongoing (e.g., road conditions, drivers’ behavior, and traffic congestion). 17