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, Taiwan2National Taiwan University, Taiwan3Academia Sinica, Taiwan

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Introduction

The comfort or rides has been identified as one of the top criteria that affects passengers’ satisfactory with public transportation system.

2Comfort Comfort does matter!!does matter!!Comfort Comfort does matter!!does matter!!

How to Measuring it?

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Questionnaire/Interview

Professional Instruments

Problems: Cost, Timeliness, and ScalabilityProblems: Cost, Timeliness, and Scalability

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

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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

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Comfort Measuring System

Exploit the GPS data Calculate the comfort index by following ISO 2631

Comfort Score: 20 x (6 – CI)

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Acceleration Level

uncomfortablecomfortable

Taxi Trajectory Dataset

One of the Taipei service providers Duration: 2010/11/8~2010/11/28 Objects: 200,000 trajectories among about 700 taxis

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field data type description

id int sequence number

micmac char taxi number

longitude double longitude of trajectory

latitude double latitude of trajectory

speed double driving speed

datatime datatime driving time

clientontaxi bool load/uoload

Statistical Results of Dataset (1)

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Among 24 Hours Among a WeekAmong 24 Hours

Statistical Results of Dataset (2)

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85% is under 30 minutes for passengers

saving time low fare

for drivers risk of no load in the

returning trip

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Trip Time Driving Time

Comfort Scores in CDF Distribution

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comfortable

Comfort Scores Analysis - Day and Night

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without passengers with passengers

Comfort Scores:1. day > night 2. w/o passengers > w/ passengers

Comfort Scores Analysis – Trip Time

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Comfort Scores Analysis – Trip Distance

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Ranking of Load among a Day Ranking lists according to some criteria

number of loads comfort score

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Ranking of Comfort Score

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The 10 BEST The 10 WORST

Implication from Ranking Lists Track back to the trajectories to understand

what happened drivers’ driving behaviors road conditions traffic conditions

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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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