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A Study of Comfort Measuring System Using Taxi Trajectories

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. Introduction.

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A Study of Comfort Measuring System Using Taxi Trajectories

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  1. A Study of Comfort Measuring System Using Taxi Trajectories Li-Ping Tung1, Tsung-Hsun Chien2,3, Ting-An Wang3, Cheng-Yu Lin3, Shyh-Kang Jeng2, and Ling-Jyh Chen3 1National Chiao Tung University, Taiwan 2National Taiwan University, Taiwan 3Academia Sinica, Taiwan

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

  3. How to Measuring it? Professional Instruments Questionnaire/Interview Problems: Cost, Timeliness, and Scalability New Solution: The IoT concept

  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

  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 New Application: Comfort Measurement

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

  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

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

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

  10. Comfort Scores in CDF Distribution comfortable

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

  12. Comfort Scores Analysis – Trip Time

  13. Comfort Scores Analysis – Trip Distance

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

  15. Ranking of Comfort Score 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

  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).

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