Predicting fromGPS and Accelerometer DataWhen and Where Tourists Have Viewed Exhibitions

ENTER 2014 Research Track Slide Number 1

Predicting fromGPS and Accelerometer Data

When and Where Tourists Have Viewed Exhibitions

aJunya KawaseaYohei KuratabNaoto Yabe

aDepartment of Tourism ScienceGraduate School of

Urban Environmental SciencesTokyo Metropolitan University, Tokyo,

Japan

bGraduate School of EducationJoetsu University of Education, Niigata,

Japan

http://www.ifitt.org/


They measure:• three-dimensional position• velocity

GPS devices


We can tell:

• Where the subjects have passed

• Where the subjects have been staying

Like this point.


Problem

However, are these techniques sufficient for tourist activity surveys?

An intelligent technique for inferring tourist activities from their GPS logs will be highly desirable.


Our Previous SurveyReported 　 at ENTER2012

(Kawase et al.2012)

We found – an significant relationship between tourists’

walking speed and their viewing behaviours.– Staying is not equivalent to viewing.


Outline of Our Previous Experiment

• Purpose : To investigate the relationship between people’s activity (viewing or not) and GPS data

• Subjects : 5 students (3 males & 2 females).• The subjects went around the zoo freely,

carrying a GPS logger• They were video-recorded


Result: Walking speedand Probability of Viewing


tt

tt v

P

PP 3237.06194.0

1loglogit

Logistic Regression Model

EstimateStandard

deviation

Chi-squared

p-valueExp

(estimate)

0.6194 0.0226 752.1810 0.0000

-0.3237 0.0105 948.5306 0.0000 0.7235

Probability of viewing at time t

Walking speedat time t


• Remaining problems– Only one parameter to presict tourists‘

behaviours– Limited subjects

• Additional experiment– More parameters– With larger number of subjects,

including families accompanying kindergartner


Outline of Additional Experiment

• Tama Zoological park in Tokyo• Subjects

– 5 student pairs in their 20s.– 9 families

parent(s) with kindergartners• They were video-recorded


Data Example

The subject was viewing an exhibition

The subject was not viewing an exhibition


Candidates for Parameters

• Visitors’ walking speed: vt

• Increase of speed from previous log: at

• Distance to the nearest exhibition: dt

• Decrease of distance from previous log:r-

t= -(dt-dt-1)/dt

• Decrease of distance to the next log:r+

t= -(dt+1-dt)/dt




Estimate Standarddeviation

Chi-squared p-value

Exp(estimat

e)β0 1.6713 0.0231 5243.7826 0.0000

β1 -0.6531 0.0099 4312.5704 0.0000 0.5204

β2 0.2217 0.0197 127.1331 0.0000 1.2481

β3 -0.0747 0.0015 2472.2617 0.0000 0.9280

β4 -0.0107 0.0039 7.4203 0.0064 0.9893

Increase of speed from previous log

at time t

Distance to the nearest exhibition

at time t

Decrease of distance to the next log at time t

ttttt rβdβaβvββp 43210 +++logit

Logistic Regression Model for 20s

The subject is viewing an exhibition more likely when he/she is located close to the exhibition, and walking

slowly or pausing.


Table 1. Number of activity logs of the students in their twenties

Logistic Regression Modelfor 20s

Predictionsuccess

rateViewingNot

Viewing

ActualViewing 18790 6438 74.48%

Not Viewing 6369 13670 68.22%

This model Model usingvt alone

NagelkerkeR2 0.3106 0.2063

Predictive value 71.71% 70.46%

NagelkerkeR2 and Predictive value

By using additional parameters,we successfully improved our modelfor estimating the visitors’ viewing

activities.


Logistic Regression Modelfor families



Estimate Standarddeviation

Chi-squared p-value

Exp(estimat

e)β0 0.5449 0.0211 669.750 0.0000

β1 -0.3137 0.0098 1014.3037 0.0000 0.7308

β2 0.0712 0.0200 12.7142 0.0004 1.0738

β3 -0.0902 0.0020 2060.2309 0.0000 0.0321

β4 0.0068 0.0068 0.0231 0.1564 1.0068

Increase of speed from previous log

at time t

Distance to the nearest exhibition

at time t

Decrease of distance to the previous log at time t

ttttt rβdβaβvββp 43210 +++logit


Table 2. Number of activity logs of the kindergartners

Logistic Regression Modelfor families

Predictionsuccess

rateViewingNot

Viewing

ActualViewing 4548 8064 36.06%

Not Viewing 3127 21337 87.22%

This model Model usingvt alone



NagelkerkeR2 and Predictive value


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probabilityof viewing

walking speed level (km/h)Kindergartners 20s

In Sum• For the students: Successful• For the family(kindergartner): Insufficient

The kindergartners showed interests in many things and switched their focus quickly from one things to another.

Adults also have possible activities other than viewing an exhibition

even in front of an animal exhibition


・ Accelerometer・ Gyrocompass

etc


Considering AdditionalHuman Behaviours’ Data

• Accelerometers

• The studies of human behaviours using accelerometers are already seen in many fields


Our Pilot Experiment

• We using: GPS and Accelerometer loggers


Our Pilot Experiment

• We calculatedthe average of absolute value of each output for every second, considering that these parameters indicate the intensity of the subject’s movement.



Estimate

Standard

deviation

Chi-squared p-value

Exp(estimat

e)

β0 4.0288 0.1806 497.783 0.0000

β1 -0.3413 0.0372 84.2470 0.0123 0.7109

β2 -0.0001 0.0000 6.2691 0.0231 0.9999

β3 -0.0002 0.0001 5.1617 0.0000 0.9998

β4 -0.0012 0.0001 270.7085 0.0000 0.9988

β5 -0.0006 0.0001 102.5665 0.0000 0.9994

ytxtytxttt aβaβvββp 543210 ++logit


Acceleration x-axisat time t

Angular velocityaround x-axis

at time t

Angular velocityaround y-axis

at time tAcceleration y-axis

at time t

The subject is viewing an exhibition more likely

when he/she is taking slower action.



This model Model of Section3



NagelrkerkeR2 and Predictive value

• the output of accelerometers seems highly useful for improving the reliability of the prediction model.


Conclusions• We revealed

– we can make more reliable model for youth by considering additional parameters available from GPS logs

– On the other hand, it is difficult to predict kindergarteners’ viewing state from the same parameters

• We demonstrated that the combined use of a GPS sensor and an accelerometer is promising

Technology

Predicting fromGPS and Accelerometer DataWhen and Where Tourists Have Viewed Exhibitions