Mobile phone placement during lectures and dependency
on LINE and text messaging: Survey of students at a
women’s university in Japan
Keywords:
LINE messaging, text messaging, mobile phone dependency, classroom behavior, higher education

Yuuki KATO, Sagami Women’s University
Shogo KATO, Tokyo Woman’s Christian University

Abstract
This study measured the dependency on text messaging of students in lectures at a women’s
university in Japan, comparing students who placed their mobile phone on their desk duringlectures with those who did not do so. Dependency was measured by using a text-message
dependency scale. Dependency on LINE messaging was measured by the same scale with “text”
replaced by “LINE” in each question. The results of the questionnaire survey showed a significant
difference in “emotional reaction,” one of the three subscales of the scale used in the study,
between students who placed their mobile phone on their desk and those who did not: the LINE
messaging dependency score was higher among those who did. Also, in each subscale score andin the total of those scores, dependency on LINE messaging was significantly higher thandependency on text messaging for both students who placed their mobile phones on their desks
during lectures and those who did not do so.

28

Journal of Socio-Informatics Vol. 8 No. 1 Feb. 2016

top, but it can be said that, at least as a background

1. Introduction

to this study, it is not difficult to find students
Previous studies discussed the use of personal

using mobile phones on desks during lectures at

laptop computers during university lecture.

Japanese universities in the present day.

Although these studies pointed out the negative

Accordingly, we undertook an investigation ofeffect of student laptop use during lectures,the use of mobile phones during lectures. In afinding that it was a distraction from the lectures

preliminary investigation in which 21 Japanese(Fang 2009; Fried 2008; Hembrooke and Gay

university

2003),

different

studies

showedstudents

participated,

52.4％

of

increased

students responded that they “use mobile phones

learning effectiveness through the appropriate

during lectures,” and 42.9％ of students said thatuse of laptops during lectures (Barak et al. 2006;

they “don’t feel embarrassed about operating

Demb et al. 2004; Gay et al. 2001). In modern

mobile phones during lectures (Tachino et al.

university lecture halls, the use of not only laptop2012).” In a study by Tachino et al. (2013a), a

computers but also of mobile phones (including

questionnaire was distributed to 20 students who

smart phones) by students in lectures has also

placed their mobile phones on their desks during

been frequently observed (Amali et al. 2012;

an exercise class in a computer lab room. The

Campbell 2006; Hammer et al. 2010; McCoy 2013;

students were asked to answer two questions: a

Wei and Wang 2010; Wei et al. 2012).

free response regarding the “reason for placing

In the past, we saw almost no use of laptop

the mobile phone on the desk,” and a yes or nocomputers by students in lectures at Japanese

question as to the “sense of guilt about placing auniversities, as is seen in other countries, but themobile phone on the desk.” The results showedpersonal use of mobile phones by students duringthat the most common reason for placing a

lectures was a common sight (Tachino et al.

mobile phone on the desk, given by 7 students,2012). In the past, use of mobile phones by

was “for the purpose of receiving contact,” and 9

students during lectures was conducted “underof the students responded that they felt no sense

the desk,” that is, out of sight of the instructorof guilt about placing their phone on their desk

(Matsushita 2007). However, we observed that

(Tachino et al. 2013a). Also, in Tachino et al.

the personal use of mobile phones by students(2013b) and Tachino et al. (2014), a yes/noduring lectures has changed owing to thequestionnaire was given to 237 students, asking

widespread use of smart phones, which was

about their “experience of placing a mobile phone

sparked in Japan by the introduction of theon the desk during lectures.” Among respondents,

iPhone in 2008. In other words, the shift in the

67.9％ said that they placed their phone on theirpersonal use of mobile phones to easy “on thedesk during regular lectures, and 30.8％ said thatdesk” use was made possible by the fact that thethey did so even in lectures where mobile phonebacks of smart phones are flat and that theuse was prohibited (Tachino et al. 2013b; Tachinophones are usually operated by touchscreen. It

et al. 2014). The common finding of thesecannot be said that the shape and design of smart

preliminary investigations is that most university

phones is the sole cause for the change fromstudents who use mobile phones during lectures

hiding one’s phone under the desk to using it onplace their phones on their desks for the purpose
29

Mobile phone placement during lectures and dependency on LINE and text messaging: Survey of students at a women’s university in Japan

of using their phone as a communication tool.

KATO et al(Kato et al 2012, 2013; Kato et al. 2013). Typically,

Building on the preliminary investigations

a speedy response is desired in mobile text

mentioned above, our current investigative study

message communications. Students are aware(Kato and Kato 2016) surveyed university aboutthat this expectation should be modified during

the use of mobile phones in lecture. The

classes. However, when there is pressure to notquestionnaire began with a question regarding

wait, or to keep others from waiting, until class

whether the student had their mobile phone

has finished, and in situations where there areplaced on their desk “now” (from the time ofrules restricting the use of mobile phones, a

distribution of the questionnaire to about 30

sense of unease among students demonstrates aminutes after the beginning of the class). The

clear dependency on mobile text message

analysis

students

communication. Yoshida et al. (2005) writes,

according to the answer on that question and(translated from Japanese) : “In recent years, thecomparing the other responses between groups.

use of mobile phones without respect of time or

The results of this study are given below. It wasplace, and the prioritization of mobile text

seen that the ratio of students who placed theirmessaging over communication with others rightmobile phone on their desk was 64.5％ and thatin front of them, is becoming a significant

there was a higher probability among those who

dependency issue with mobile text messaging

did so to use their phones during lessons toamong young people.”

proceededbygrouping

check phone, text, and LINE message content

Long hours of use was named as the primary

and to respond (by replying, etc.) to thosecause of “Internet dependency” according to a 2013

messages; further, those who placed their phone

Ministry of Internal Affairs and Communications

on their desk felt more unease when rules

survey targeting participants between upper

restricting the use of mobile phones during

primary school age and age 25 (Ministry ofclasses were in place (Kato and Kato 2016). An

Internal Affairs and Communications, Japan

additional pattern was seen: in mobile phone2014). According to the survey, 68.8％ of highcommunication by students during classes, text-

school students and 65.6％ of university students

based communication was an easy choice. In

responded that they had taken away from time

particular, the LINE application, which is a

dedicated for other activities in order to make(primarily smart phone-based) communication

more time for Internet use, mainly from sleeping

tool for free chat and voice over IP telephone(48.1％ of high school students and 47.5％ ofcalls, was used more often than standard mobile

university students) and study (46.6％ of hightext messaging (Hereafter, “LINE messaging”

school students and 34.6％ of university students)

will refer to LINE’s chat function, called “Talk”(Ministry of Internal Affairs and Communications,in-app) (Kato and Kato 2016).

Japan 2014). Many related studies point out thatusing

the existence of adverse effects on normal life

mobile phones during classes differs from

Althoughpersonal

communication

activities is one possible indicator of dependency

standard class behavior (Ling 2004), one reason(Okada 2014). It has been noted that women are

this behavior occurs is because students desire

far more likely to use their mobile phones more

the rapid exchange of mobile text messages

than a PC, and that more women use mobile text30

Journal of Socio-Informatics Vol. 8 No. 1 Feb. 2016

messaging and LINE (Ministry of Internal Affairs

setting, students will be influenced by theseand Communications, Japan 2014). LINE, invarious factors, intra-class comparison of students

particular, has been reported to be the most-usedshould allow for discernment of individual

mobile communication tool (LINE Corporationdifferences.

2013), particularly its chat, which is faster andIn certain types of classes, such as sports

easier to use for long periods of time than mobile

classes, multiple seminars, or classes where thetext messaging (Kato 2015). Also, because LINE

lecturer strictly prohibits or punishes cell phone

displays read receipts (a function that lets theuse in-class, external influences are too strong tosender know when his/her message has been

confirm student factor. A survey by Terao and Itoread by the recipient), many young people(2014) reported that 40％ to 60％ of students

continually check their phone to see if theirused their mobile phones in lectures for personalmessages have been read by the other party, inreasons when no particular rules about mobile

addition to normal use to send and receive

phone use in class were made. Because this ismessages. A previous study (Kato and Kato 2016)

about

showed that LINE messaging is used more thanpreliminary research, even though the classroom

text messaging in class, and as a result it isconditions may be different, the observation thatthought that dependency on LINE messaging isabout 50％ of students are using their mobile

greater among students than dependency onphones during lectures (especially those in which

other forms of text messaging. Because of theno rules regarding phone use have been set)

above, it is necessary to examine the degree ofsuggests that the relationships between the in-student Internet dependency as a factor inclass factors and the students’ behavior can beconsidering the personal use of mobile phones

elucidated. Against this background, the present

during lectures. Furthermore, consideration

study was done in a class at a women’s university

should be given to the main communication tools

confirmed to meet the above preliminary study

usedcommunication

requirements (Kato and Kato 2016). An all-female(mobile text messaging and LINE) within theclass was chosen to exclude the effects of gender.

inmobiletext-based

overall scope of Internet dependency.

the

same

as

our

abovementioned

Given the above, the present study examined

This study takes the student factor (dependency)

the following two hypotheses.

into consideration in the personal use of mobile

Hypothesis 1: Due to the high probability ofphones in lectures, but there are a variety ofuse of mobile phones during class by students

influences on student behavior. Many factors

who place their phone on their desk as comparedother than the student factor can be considered,with those who do not do so, there is a highsuch as lecture content, form of the lecture, thedegree of dependency on mobile text message

lecturer, and the classroom. However, moving

communication among students who place theirstudents to an experimental setting wherein all ofmobile phone on their desk during classes.

these factors are controlled for could cause

Hypothesis 2: Due to the trend for LINE text

students to alter their normal behavior. Therefore,

messaging to be used during classes, the degree

the present study was conducted during a lectureof dependency on LINE text messaging among

at an actual university. Although in a real-life

present-day female university students is greater
31

Mobile phone placement during lectures and dependency on LINE and text messaging: Survey of students at a women’s university in Japan

KATO et althan that on mobile phone text messaging,

SD 0.63 years) were the enrolled students inanother text communication tool.

attendance on the day of the questionnaire.
The survey was performed in December 2013

2. Purpose

during the eleventh class meeting, approximately
30 minutes after the class began. Students

This study examined the relationship between

required approximately 15 minutes to complete

the behavior of placing mobile phones on desks

the questionnaire. At the time of the questionnaire,during university lectures and the dependency ofno class rules were in place regarding the use ofuniversity students on mobile text messaging

mobile phones.

and LINE. The dependency of students who(2) Questionnaire

placed their mobile phones on their desks duringlectures was compared to students who did not.

The questionnaire distributed to student

The aforementioned Hypothesis 1 and Hypothesis

survey participants first required a “Yes or No”

2 were also examined.

response to the question, “Is your mobile phoneon your desk at the present moment?”

3. Method

For the rest of the questionnaire, questions
were used to measure dependency on mobile(1) Participants and Class Surveyed

text messaging and LINE messaging. The text-

This study was performed at a women’s

message dependency scale of Yoshida et al.

university in a liberal arts class on understanding(2005) and Igarashi et al. (2008) was used tomedia. This is an elective course, taken by first-

measure dependency on mobile phone text

to fourth-year students from various schools

messaging. This scale incorporated an element ofwithin the university. As a result, although small

dependency on communication, taken fromgroups of three to five friends were seen, there

previous studies on Internet, computer, andwas little overall social cohesion as is frequently

mobile phone addiction (e.g., Armstrong et al.

seen in courses intended for specific majors.

2000; Block 2008; Caplan 2005; Griffiths 2000;

Enrolled students perform investigations on self-

Kandell 1998; Morahan-Martin and Schumacher

selected topics related to media and compile their

2000; Park 2005; Young 1998), and created from afindings into a PowerPoint presentation. In

comprehensive viewpoint of dependency on

addition to general lectures, in each classcommunication media (Igarashi et al. 2008;

approximately 10 of these presentations are

Yoshida et al. 2005). The full scale contains 56

randomly selected for the respective students toitems, but there is also a 15-item short-version

give 5-min presentations, which are then critiquedscale (Igarashi et al. 2008, p.2318), and it was thisby the instructor and the rest of the class. This

short-version scale that was used in this study.

class was the same class surveyed in Kato andThis scale is composed of 3 subscales. There

Kato (2016).

are 5 questions in the “emotional reaction”

There were 101 students enrolled in thesubscale, such as “I feel disappointed if I don’t

course, and the 80 participants (all Japanese

receive any text messages,” and “I often check

women 18-21 years old, mean age 18.98 years ±my mailbox to see if I have a new text message.”32

Journal of Socio-Informatics Vol. 8 No. 1 Feb. 2016

There are 5 questions in the “perception ofexcessive use” subscale, such as “I sometimes
send text messages while engaging in aconversation with another person,” and “I
sometimes spend many hours on text messages.”

Figure 1 N umber of students who placed

There are also 5 questions in the “relationshiptheir mobile phones on the desk

maintenance” subscale, such as “I cannot

during classmaintain new friendships without text messages,”and “I can’t form any new relationships without

the two groups, an analysis of variance wasusing text messages.” The questions require

performed with 2 factors, membership in the on-

responses on a 5-point scale: not at all applicable,

desk group (an intersubject or “group” factor)

not very applicable, can’t say either way,

and dependency on mobile text messaging /somewhat applicable, and very applicable.

LINE messaging (an intrasubject or “tool” factor).

To measure dependency on LINE text

Responses to the two dependency scale

messaging, the phrase “text messages” wasquestions were on a 5-point scale and were

replaced by the phrase “LINE messages” in eachanalyzed as Likert items, with “Not at allof the 15 questions in the text-message

applicable” given a value of 1 and “Very applicable”

dependency scale. For example, the phrase “Ia value of 5. Additionally, there was 1 student infeel disappointed if I don’t receive any text

each group who reported that they do not use

messages” for text-message dependency wasLINE messaging, and data for these 2 studentschanged to “I feel disappointed if I don’t receive

were omitted from the analysis.

any LINE messages” for LINE text messaging.

In the results of the analysis of variance using

Permission was obtained from the creators of

the total score of all 15 questions, the tool factor

the text-message dependency scale for the

had a significant main effect (F (1, 76) = 45.50, p

replacement of “text messages” with “LINE< .001), the group factor did not (F (1, 76) = 2.05),

messages” in the text of each question.

and the interaction was not significant (F (1, 76)= 0.95). Figure 2 shows that in both groups the

4. Results

dependency on LINE messaging was greaterthan the dependency on mobile text messaging.

Responses to the question, “Is your mobile

An analysis of variance was also performed forphone on your desk at the present moment?”the 3 subscale scores. Additionally, Cronbach’s

showed that 67.5％ (54 students) had theiralpha was calculated for each subscale with themobile phone placed on their desk (Figure 1).

following results for text messaging and LINE

This group of 54 students is referred to as themessaging, respectively: 0.84 and 0.90 for“on-desk group” hereinafter, and the group of 26

emotional reaction, 0.73 and 0.83 for excessive

students who answered negatively is called theuse,“not-on-desk group.”

maintenance.

and

0.91

and

0.85

for

relationship

Next, to compare the score for dependency on

In the results for the emotional reaction

text messaging and LINE messaging between

subscale, there were marked differences between
33

Mobile phone placement during lectures and dependency on LINE and text messaging: Survey of students at a women’s university in Japan

KATO et almessage dependency.
For the relationship maintenance subscale, thetool factor main effect was significant (F (1, 76) =13. 30, p < .001), but the group factor main effectwas not (F (1, 76) = 0.63) and there was no
significant interaction (F (1, 76) = 0.75). Figure 5
shows that in both groups LINE-messaging
dependency

Figure 2washigherthanmobile-text

message dependency.

Comparison of average dependency

Finally, an analysis of variance was performed

scores between on-desk and not-using the scores of each of the 5 questions foron-desk groups

emotional reaction, where a difference in thesignificances of the tool factor main effect (F (1,group factor was observed. Significance was76) = 30.58, p < .001), the group factor main effectobserved for each of the following 3 items. For “I(F (1, 76) = 3.30, p < .10), and the interaction
effect (F (1, 76) = 3.03, p<.10). Figure 3 showsthat in both groups, LINE-messaging dependency
was higher than mobile text-message dependency,
but that LINE-messaging dependency was evenhigher in the on-desk group.
For the perception of excessive use subscale,the tool factor main effect was significant (F (1,76) = 58.63, p < .001), but the group factor main
effect was not (F (1, 76) = 0.49) and there was no

Figure 4 C omparison of average excessive

significant interaction (F (1, 76) = 0.00). Figure 4

use subscale scores between on-shows that in both groups LINE-messaging

desk and not-on-desk groups

dependency was higher than mobile text-

Figure 5 C omparison of average relationship
Figure 3

Comparison of average emotional

maintenancereaction subscale scores between

between on-desk and not-on-desk

on-desk and not-on-desk groups

groups34

subscalescoresJournal of Socio-Informatics Vol. 8 No. 1 Feb. 2016feel disappointed if I don’t receive any text
messages / LINE messages”, the interaction was
significant (F (1, 76) = 4.90, p < .05) as were thetool factor main effect (F (1, 76) = 24.57, p < .001)
and the group factor main effect (F (1, 76) = 3.48,
p < .10). Figure 6 shows that for both groups
LINE-messaging dependency was higher thanmobile text-message dependency, but that LINEmessaging dependency was even higher in the

Figure 7 C omparison of average scoreson-desk group. For “I feel disappointed if I don’t

between on-desk and not-on-desk

get a reply to my text message / LINE message

groups for the statement “I feel

immediately,” the tool factor main effect wasdisappointed if I don’t get a reply tosignificant (F (1, 76) = 17.96, p < .001) as was the

my text message / LINE message

group factor main effect (F (1, 76) = 5.63, p < .05),

immediately.”

but there was no significant interaction (F (1, 76)= 1.69). The item “I feel anxious when people
don’t immediately reply to my text message /LINE message” had the same pattern: asignificant tool factor main effect (F (1, 76) =14.22, p < .001) and group factor main effect (F (1,76) = 5.38, p < .05), but an insignificant interaction(F (1, 76) = 0.85). The LINE-messaging dependency
score was higher than the mobile text messaging
dependency score, and both scores for the on-

Figure 8 C
 omparison of average scores

desk group were higher than those for the not-between on-desk and not-on-desk
groups for the statement “I feelanxiouswhen

immediately

people

don’t

to

text

reply

my

message / LINE message.”on-desk group (Figures 7 and 8).

5. Discussion
Figure 6

Comparison of average scores
between on-desk and not-on-deskUsing a questionnaire delivered to students ofgroups for the statement “I feela women’s university in Japan, dependency on

disappointed if I don’t receive any

LINE and text messaging was measured fortext messages / LINE messages.”

students who placed their mobile phones on their
35

Mobile phone placement during lectures and dependency on LINE and text messaging: Survey of students at a women’s university in Japan

KATO et al

desk during class and those who did not do so,

communication is a form of chatting. That is,and two hypotheses were examined.

because LINE messages are regarded as more

The first hypothesis, that there is a high degreeofdependency

onmobiletext

casual,daily

conversation(withmessagesmessageexchanged simultaneously and continuously),communication among students who place their

the perception of excessive use score was higher

mobile phone on their desk during classes, isfor LINE messaging than for mobile text

partly supported. A difference in score wascommunication. As regular conversation createsobserved between the on-desk and not-on-deskand maintain personal relationships, it wasgroups in only the emotional reaction subscale ofexpected that the relationship maintenance score

dependency on LINE messaging, with the on-

would be slightly higher for LINE messaging,desk group having a higher score. Among the 3

which enables conversation-style exchanges

subscales, the score for perception of excessive

more readily than mobile text messaging does.

use was highest in both groups for both mobile

Additionally, LINE messaging notifies the senderphone text messaging and LINE messaging.

when a message has been read. This means that,

Excessive use indicates extended periods ofin addition to waiting for a reply that is notcontinuous mobile text messaging and LINE

arriving quickly, which occurs with mobile text

messaging, and the use of mobile text messaging

messaging, LINE messaging also causes users toand LINE messaging in communicating back andwait for a reply that is not arriving quickly even

forth with people as well. In contrast, thethough the sender that knows the recipient has

relationship maintenance subscale, which asked

read the message. Thus the emotional reactions

about the necessity of mobile text messages andof loneliness and unease arise more easily with

LINE messages to create and maintaining

LINE messaging.

personal relationships, was lowest among theAccording to LINE Corporation (2013), mostsubscales for both groups. Taken together, theseLINE users are in their teens and twenties, with

imply that university students use mobile text

the amount dropping for those 30 and older.

messaging frequently but do not think that

While most university students use LINE topersonal relationships are impossible to create

communicate

and maintain without mobile text and LINE

generation, most members of the older generation

messaging. It was also observed that students

use standard mobile text messaging, mainly

who place their phone on their desk during classbecause they are not as accustomed to the newer

have a marked tendency to feel lonely or uneasycommunication tool as younger students (Katowhen they do not receive a quick reply from the2015).

other party during their communication, but thiscommunicating by LINE are different from thoseemotional reaction was more apparent for LINEin mobile text messaging. Because for mostmessaging than in mobile text messaging.

university students, familiar people such as

Thus,

with

the

students

partiesoftheinvolved

same

when

The second hypothesis, that the degree offriends, significant others, and so on are generally

dependency on LINE text messaging amongin the same generation, it is assumed that they

university students is greater than that on mobile

use LINE to communicate with these parties andphone text messaging, is supported. LINE text

mobile text messaging to communicate with out36

Journal of Socio-Informatics Vol. 8 No. 1 Feb. 2016of-generation individuals such as superiors at

and dependency scale score, although it goeswork or parents. If parties are different, thewithout saying that future studies should

message content will also differ: in LINE, theinvestigate the various external and internal

content is mainly conversation that includes ainfluences on phone behavior.

variety of emotional expressions, while in mobile

6. Conclusions

text messaging the content is more likely to bemainly business-like exchange. Compared withmobile text messaging, the use ratio of LINE isThis study examined the relation between

therefore increasing, with LINE taking on an

dependency on mobile text messaging and LINE

important role in the deepening of relationships

messaging and the act of placing a mobile phonesuch as friendships. Because of this, it is thoughton the desk during classes, focusing on students

that there are many more messages received in

at a Japanese women’s university.

LINE and that the attention paid to replies and

1) Students who place their mobile phone onwhether messages have been read or replied totheir desk have a particularly high dependency

has contributed to increased dependency onscore for LINE messaging for emotionalLINE.

reactions, including feelings of uncertainty andloneliness in waiting for a slow reply.

This study was conducted in a classroom
where no rules had been set regarding mobile2) There is a higher dependency score forphones. However, this didn’t completely remove

LINE messaging than for mobile text messaging.

differences in influence between students. For

This study focused on the act of university

example, we assume that differences in student’s

students placing their phone on their desk during

seating were influential. Generally speaking, ifclasses. Our previous study showed that the act

the seat in front of you is occupied, it’s easier toof placing a mobile phone on one’s desk is a sign

take out your mobile phone without being seenthat shows the potential for mobile phone useby the lecturer. This can be described as anduring classes (Kato and Kato 2015). According

external influence on the placing of a phone on

to this study, this action also suggests a highthe desk. However, several student factors other

degree of dependency on mobile phone use bythan dependency can also be considered, such as

the student.

difference in student age. First-year students

7. Limitations and future research

may still experience residual influences frommobile phone rules in high school, which are
generally very strict. Also, individual differences

The number of samples in this study was 78, so

between students may also become factors, suchconsideration remained focused on classification

as student relationships and events taking placeof whether students did or did not place theirthat day (such as an emergency call, or having

mobile phones on their desk. In the future, it will

just received a romantic confession). This study,

be necessary to conduct this same survey under

however, demonstrated and clarified that there

the same conditions, but with an increase sample

are a number of correlations between thesize, to improve the quantity of data. By doing so,

behavior of setting phones on desks in lectures

it will become possible to classify phone use
37

Mobile phone placement during lectures and dependency on LINE and text messaging: Survey of students at a women’s university in Japan

KATO et alaccording to common student attributes (e.g.

and mobile world. Oxford, NY: Oxford University

academic year, major, seat that day, frequency ofPress.

mobile text messaging / LINE use, purpose of

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306-307.

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Modern university students are often called

Caplan, S. E. (2005). A social skill account of problematic“digital natives,” indicating a generation that has

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they are surrounded by computers and mobile

Demb, A., Erickson, D., & Hawkins-Wilding, S.

phones, and it is said that they are skilled at(2004). The laptop alternative: Student’s reactions

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during classroom lectures should be conducted

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Acknowledgements

Fried, C. B. (2008). In-class laptop use and its effects

This work was supported by JSPS KAKENHI

on student learning. Computers & Education,

Grant Numbers 24501220, 24700913, 15K01095,

50(3), 906-914.

15K01089.

Gay, G., Stefanone, M., Grace-Martin, M., &Hembrooke, H. (2001). The effects of wireless

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40

An Evaluation of Measures against the Urban Heat Islandfrom the Viewpoint of Artificial Exhaust Heat of Road Traffic*– An Evaluation Using GIS in the Tokyo 23-Ward Area –Keywords:
Urban Heat Island, Artificial Exhaust Heat, Road Traffic, Geographic Information Systems (GIS), Tokyo
23-Ward Area

Kohei IMAI, Fuji Xerox Co., Ltd

Kayoko YAMAMOTO, University of Electro-Communications

Abstract
This study aims to evaluate measures against the Urban Heat Island (UHI) from the viewpointof artificial exhaust heat of road traffic using Geographic Information Systems (GIS) in the Tokyo
23-Ward Area where the degree of UHI is particularly strong. We developed a GIS database thatreflects road traffic conditions and calculated the volume of artificial exhaust heat of road traffic
to evaluate measures against the UHI.
The findings of this study can be summarized in the following three points.
(1) Artificial exhaust heat volume for moving targets was remarkably higher than that of stationary
targets and, in particular, artificial exhaust heat volume was high on roads with remarkable
numbers of vehicles and running speeds such as expressways and ring roads. Artificial
exhaust heat volume was particularly high for cars and regular trucks by model, and forweekdays and holidays, and daytime and night-time, artificial exhaust heat volume was mainly
high during the day on weekdays.
(2) In UHI-related policy for moving targets, the suppression of waste heat through choice of fuelburned, improvement of traffic flow by securing space for cyclists and pedestrians,development of bypasses, and upgrading signal control managed to reduce artificial exhaust
heat volume even though there were differences in degree of reduction. However, roads withhigh artificial exhaust heat volume reduction ratio differed with each UHI-related policy.
(3) In UHI-related policy for stationary targets, special road surfaces and the increase in efficiencyof energy consumption equipment achieved a reduction ratio of around 30％ in artificial
exhaust heat volume, more than moving targets as well as making it possible to expect a
reduction in artificial exhaust heat volume on a wide scale.
* This paper is a translation of the refereed paper which was firstly published in Journal of Social Informatics, Vol.
21, No. 2, pp.47-58, 2010.

41

An Evaluation of Measures against the Urban Heat Island from the Viewpoint of Artificial Exhaust Heat of Road Traffic – An Evaluation Using GIS in the Tokyo 23-Ward Area –

1. Introduction

IMAI et al

Based on the above, this study targets theTokyo 23-Ward Area where UHI intensity is

1.1 Point of view and purpose of research

particularly high, using a Geographic Information

Due to global warming in recent years, Urban

System (GIS) (1) with the aim of evaluating UHI-Heat Islands (UHI) have become a serious

related policies focusing on artificial exhaustproblem for major urban areas in every country

heat from road traffic. According to the Tokyoof the world. Japan’s Inter-Ministry Coordination

Metropolitan Heat Island Mitigation Committee

Committee to Mitigate Urban Heat Island(2003), recorded temperatures in the Tokyo 23-

developed the Outline of the Policy Framework

Ward Area have risen by approximately 3℃ in

to Reduce Urban Heat Island Effects in 2004. The

the past 100 years, and due to the fact that this

Ministry of Economy, Trade and Industry (2007)

rise is high in comparison with other large cities

reported that Japan’s energy consumption in thein Japan and other countries, it is understood thatprivate consumer, transportation and industrial

UHI intensity as urban warming is remarkably

sectors increased by approximately 2.6, 2.1 andhigh in addition to the effects of global warming.

1.0 times respectively from 1973 to 2005. The

1.2 Related Work

Ministry of the Environment (2003a) also
reported an increasing trend in artificial exhaust

The first study on UHI to be published in Japan

heat due to energy consumption in the Tokyo 23-

was Tyson et al. (1973), and the number ofWard Area, and suggested that the proportions ofstudies began to increase dramatically from

artificial exhaust heat were approximately 50％

around the year 2000. Based on the results of abuildings, 40％ automobiles and 10％ factories.

review of prior research targeting the relationshipMikami (2005) suggests changes in urban

between UHI and artificial exhaust heat in the

structure and artificial exhaust heat as causes forsame way as this study, the following 16 major

UHI, while the Japanese Meteorological Agency

studies were divided into 4 groups depending on(2007) suggests changes in land use, the effects

their research targets.

of buildings and the effects of artificial exhaust

Phenomenon / characteristics (3 studies):(1) heat. Though the highest proportion of artificial

Mikami (2002), Ashie et al. (2004), Ishimaru

exhaust heat is caused by buildings, measures to(2004)
Causes / mechanism (1 study): Mikami(2) increase natural ground cover and greenery have(2005)

already been implemented, and there are many
examples of UHI research related to city green(3) Effects / issues (7 studies): Mochida et al.

zones and increasing greenery for buildings and(2001), Yoshida et al. (2002a, 2002b),

their grounds represented by Hirano et al. (2004)

Kamishige (2004), Sugahara (2005), Tamuraand Hoyano (2005). However, the proportion of

et al. (2006b), Narumi (2006)(4) Measures / effects (5 studies): Ota (2001),

artificial exhaust heat caused by road traffic in
the

field

of

transportation,

inTamura et al. (2003, 2005a, 2005b, 2006a)

particular

automobiles, is high and despite this being one of

As shown by the above prior research inthe major causes of UHI, there has been little

related fields, though (3) Effects / issues havefocus on this issue.

been most frequently studied, there have been42

Journal of Socio-Informatics Vol. 8 No. 1 Feb. 2016few studies on the relationship between UHI andresearch information is updated using future

the artificial exhaust heat from road traffic. Only

technology developments and advances inAshie et al. (2004) discussed the differences

research, it will be possible to update and provide

between sensible heat and latent heat, andeven more accurate information. In addition,

calculated artificial exhaust heat output for roadthough this study targeted the Tokyo 23-Wardtraffic in 500m grid units in the Tokyo 23-Ward

Area, if it were possible to obtain the same kind of

Area using GIS to obtain an understanding of thedata and information, it would be possible tooutput characteristics of artificial exhaust heat.

conduct evaluation by applying this evaluation

Though Ashie et al. (2004) conducted analysis inmethod to other urban areas. Due to this, we can

500m grid units, it did not specifically investigate

expect the evaluation method proposed in thisto propose UHI mitigation, or a detailed

study to be temporally and spatially general-

understanding

purpose and duplicable.

ofartificial

heat

volumeconsidering road traffic status.

Next, this study set initial functions prior to the

Accordingly, this study refers the results of theimplementation of each UHI-related policy andabove-mentioned prior research as a foundation,effect functions for post-policy implementation,anddatabasesuggested respective methods of calculating

development and information analysis tool toartificial exhaust heat volume before and after

develop a digital map database reflecting thepolicy implementation using GIS, and used thesestatus of road traffic. Furthermore, using this,to calculate artificial exhaust heat volume.

specific evaluation of UHI-related policy is

Furthermore, by comparing artificial exhaust

conducted after calculating artificial exhaust heat

heat volume before and after the implementation

volume from road traffic for each road unit usingof each UHI-related policy, evaluation was

GIS.

conducted by investigating the effects ofhighlyeffective

GIS

as

a

implementation of each policy. Though it should

2. Evaluation method

be noted that Ashie et al. (2004) conductedanalysis in 500m grid units as mentioned above,

2.1 Evaluation framework and method

this study used a digital base map that expresses

This study began with a review of priorroads in line format as detailed in the section

research to gather UHI-related policy withbelow to add diverse data, and develop anreference to artificial exhaust heat from road

integrated database to analyze and evaluate. By

traffic in the Tokyo 23-Ward Area, and organizeddoing this, it was possible to calculate artificial

this information focusing on the characteristics

exhaust heat volume by road unit and evaluateof each policy target. Effect functions were set in

UHI-related policy, taking road traffic statusorder to analyze the effects of UHI-related policy

characteristics into account.

after implementation, taking road traffic status

2.2 
GIS database development / usage

into account, and road traffic status data andprocessfunction coefficients used government data,

Figure 1 shows the development / usage

experiment results and estimated values.

process for the GIS database in this study. First of

Using the above method, if all data and43

An Evaluation of Measures against the Urban Heat Island from the Viewpoint of Artificial Exhaust Heat of Road Traffic – An Evaluation Using GIS in the Tokyo 23-Ward Area –Figure 1

IMAI et al

GIS Database Development / Usage Process

3. 
Organization of UHI-related policy andall, road data was developed in digital map format

setting of functions

from 42,461 lines with numerical map 2500
(National Geospatial Framework) . Next, this(2)

3.1 The relationships between the organization

data was added to / integrated with data relatingof UHI-related policy and functions

to the road attributes and road traffic status onand numerical map 2500 to

All policies listed by the Ministry of thedevelop a database of road traffic status in theEnvironment (2003b), the Tokyo Metropolitan

Tokyo 23-Ward Area.

Heat Island Mitigation Committee (2003), androad traffic census(3)

Furthermore, from the next section onwards,

the Ministry of Land, Infrastructure, Transport

following the framework and method explained

and Tourism (2007) were researched, and thein the previous section, the volume of artificial

nine policies shown in Table 1 are the result of

exhaust heat before and after implementation of

extracting only UHI-related policies that were

each UHI-related policy was calculated using

implemented or under consideration for the

GIS, and this data was also integrated into themitigation of artificial exhaust heat from roadroad traffic status database. It should be noted

traffic in the Tokyo 23-Ward Area. Furthermore,that initial and effect variables for road traffic

the authors focused on the characteristics ofstatus data, effect variables for degree of

each policy target, and divided to organize thesevariability of traffic status and the artificial

into three groups: A. Transport framework / fuel,exhaust heat volume calculation formula included

B. Road / road surface construction, and C. Road

in initial and effect functions in Figure 1 areequipment. It should be noted that UHI-related

shown in section 3. Section 4 shows the concrete

policies No.3, 4, 5, 7 and 8 aimed to improve

artificial exhaust heat volume calculation methodrunning speed by alleviating and reducing traffic

using GIS in a flow chart and calculates artificial

congestion during the day.

exhaust heat volume.

Though the effects of implementation ofrelated policies are diverse, they do permit
eventual effects that inhibit artificial exhaust heat
(main effects). Accordingly, primary factors
directly before achieving main effects were
defined as supplementary effects, and the44

Journal of Socio-Informatics Vol. 8 No. 1 Feb. 2016

determinants of supplementary effects were

efficiency of equipment. Furthermore, thedetermined as effect variables. Effect functions

targets of supplementary effects were divided

were set and calculations were made for artificial

into two groups: moving and stationary depending

exhausttheon whether the target is traffic flow data (number

supplementary effects of each related policyof vehicles, running speed) or traffic facility datausing these effect variables. As shown in the(road length or width, number of traffic signals).

previous section, initial functions were also set

Using the above-mentioned method, it was

before the implementation of these policies inpossible to specify how effects are emphasizedorder to obtain an understanding of theby the supplementary effects of each related

implementation effects of each UHI-related

policy, and to organize data categories in roadpolicy.

traffic status data which is an effect variable andheatvolumeaffectedby

It was possible to divide supplementary effects

degree of variability of traffic status for each UHI-

into four categories: improvements in fuelrelated policy. As moving targets were mainly

consumption, improvements in running speed,

affected by sensible heat and stationary targets

reduction in heat storage volume, and increase in

by latent heat, it can be said that the categories inTable 1

UHI-Related Policy and Effect Variables

1) The ○ symbol in the table represents road traffic status data which is an effect target for supplementary
effects in each UHI-related policy, and each value for road traffic status data and degree of variability intraffic status (diffusion and progress rates) marked with ○ is used to calculate artificial exhaust heatvolume. The × symbol represents road traffic status data which is not an effect target for supplementary
effects in each UHI-related policy.
2) UHI-related policies No.1 and No.2 are both UHI policies related to automobiles. According to the
Ministry of the Environment (2003b), the Tokyo Metropolitan Heat Island Mitigation Committee (2003),
and the Ministry of Land, Infrastructure, Transport and Tourism (2007), hybrid vehicles with two sourcesof power in the engine and in the motor, and driven using a combination of their respective benefits aredefined as vehicles that can achieve both energy conservation and low emissions. Policy No.1 targets
only hybrid vehicles, whereas policy No.2 targets vehicles that run on gasoline, excluding hybrid
vehicles.
45

An Evaluation of Measures against the Urban Heat Island from the Viewpoint of Artificial Exhaust Heat of Road Traffic – An Evaluation Using GIS in the Tokyo 23-Ward Area –

road

surface

IMAI et al

this study considers the effects of both sensible

Environment (4),and latent heat.

coefficients in the results of Kanto Regional

waste

heat

Development Bureau, the Ministry of Land,

3.2 Setting initial functions

Infrastructure, Transport and Tourism (5) project,

Table 2 provides an overview of initial

and traffic signal waste heat coefficientsfunctions, while Table 3 provides an overview ofconverted to one day’s worth of heat volume

initial variables. A formula for calculating artificialfrom electricity consumption by light bulb andexhaust heat volume in initial functions such as

LED from the National Police Agency (6).

those in Table 2 was developed according to theTable 2 shows each data category for roadtwo categories mentioned above, moving andtraffic status data used as initial variables, andstationary targets. Using this formula, a basic

these are also used in the same way in effectstructure was formed to group effect functions

functions detailed in the next section. Road traffic

calculating artificial exhaust heat volume after

census values were used in each of the above-

the implementation of each UHI-related policy. It

mentioned categories, and values for numbers ofshould be noted that, as elements of effectcars, buses, and small and regular trucks werefunctions were designated as effect variables, ininput by model and running speed values forthe same way, elements of initial functions wereboth weekdays and holidays in FY2005 were

designated as initial variables. Initial variables

input. For the number of traffic signals, theare: road traffic census road traffic status data,

number of intersections with traffic signals waswaste heat coefficients by model and speed

calculated by dividing them into two types

converted

consumption

depending on the width of the road: main roads

coefficients by model and speed which are

and minor roads, and the number of traffic

provided by Ministry of Land, Infrastructure,signals was set at four as the average number

Transport and Tourism / Ministry of thefound at a regular cross-shaped intersection and

from

the

energyTable 2

Table 3

Initial Functions

Outline of Initial Variables

46

Journal of Socio-Informatics Vol. 8 No. 1 Feb. 2016

calculations were made.

traffic status data. These are the actual changing
values at the time of setting effect functions in

3.3 Setting effect functions

order to calculate artificial exhaust heat volume

Table 4 provides an overview of effectafter the implementation of each policy. If there

functions, while Table 5 provides an overview ofare changes to the expected implementation

effect variables. Effect functions such as those ineffects of UHI-related policies, it is possible toTable 4 were composed using effect variables

recalculate artificial exhaust heat volume by re-

and in effect variables, there were functions forsetting this degree of variability for traffic status.

which degree of variability of traffic status was

Moreover, waste heat coefficients by model /added to road traffic status data as shown in the

speed in initial functions were re-set in moving

previous section as a correction factor after thetarget UHI-related policies No.1, 2, 3, 4, 5, 8 and 9

implementation of each UHI-related policy.

as waste heat coefficients by model / speed in

Moreover, there were also functions for which

effect functions. This is because the policy target

heat waste coefficient by model / speed, roadin UHI-related policies No.1 and 2 is related to A.

surface waste heat coefficient, and traffic signal

Transport framework / fuel, and there is potential

waste heat coefficient were each converted

for future change in goal values for waste heat

respectively.

volume, waste heat rate and rate of spread ofDegree of variability of traffic status effecttarget vehicles. This is because, in UHI-related

variables was set as a correction factor in order topolicies No.3, 4, 5, 8 and 9, corrected values forreflect the effects of the implementation of eachrunning speed in road traffic status data throughUHI-related policy in each data category of road

improvement of degree of variability in traffic

Table 4

Effect Functions

47

An Evaluation of Measures against the Urban Heat Island from the Viewpoint of Artificial Exhaust Heat of Road Traffic – An Evaluation Using GIS in the Tokyo 23-Ward Area –Table 5IMAI et al

Overview of Effect Variables

status at the time of composing effect functions

and traffic signal waste heat variables in the same

are inherent as heat waste variables by model

way in traffic signal waste heat coefficients, andand speed. Furthermore, road surface waste heateach artificial exhaust heat volume was re-coefficients in initial functions were reflected indefined. UHI-related policy No.3 was based on

the implementation effects of each policy as roadresearch results in the centre of Nagano City, andsurface heat waste variables in effect functions,as a result of reviewing research such as data48

Journal of Socio-Informatics Vol. 8 No. 1 Feb. 2016

fromgovernmental

documents,

4. Calculation of artificial exhaust heat volume

experimentusing GIS

results and estimated values relating to the effectfunctions of the policies mentioned in 2.1, at

4.1 Artificial exhaust heat calculation method

present, this study determined that its own

Figure 2 shows the artificial exhaust heat

research target areas were the most appropriate.

volume calculation method using GIS in flow
chart format. First of all, initial variables were set

Figure 2

Calculation Method for Artificial Exhaust Heat Volume using GIS
49

An Evaluation of Measures against the Urban Heat Island from the Viewpoint of Artificial Exhaust Heat of Road Traffic – An Evaluation Using GIS in the Tokyo 23-Ward Area –IMAI et al

as heat waste coefficients by model and speed forand holidays, and four types of vehicles inmoving targets, and as road surface heat waste

accordance with the categories in the road traffic

coefficients and traffic signal waste heatcensus. However, as moving target UHI-related

coefficients for stationary targets in initial

policies No.3, 4, 5, 8 and 9 aimed to improvefunctions. Artificial exhaust heat volume wasrunning speed by alleviating and reducing traffic

calculated before the implementation of UHI-

congestion during the day, artificial exhaust heat

related policies. Road traffic status data correctedvolume after the implementation of these policies

with degree of variability in traffic status, waste

was calculated in the daytime. In addition, as

heat variables by model and speed in moving

stationary target artificial exhaust heat volume istargets, and road surface waste heat variables

calculated with one day’s worth of waste heatand traffic signal waste heat variables involume, artificial exhaust heat volume after thestationary targets were set as effect variables in

implementation of stationary target UHI-related

effect functions and artificial exhaust heat volume

policies No. 6 and 7 was calculated by day.

was calculated after the implementation of UHI-

4.2 Results of calculations of artificial exhaust

related policies.

heat volume for moving targets

It should be noted that, as waste heatcoefficients by model and speed differ in the

Using the calculation method shown in thedaytime (9 am - 8 pm) and the night-time (9 pm - 8

previous section, artificial exhaust heat volumeam), artificial exhaust heat volume for moving

was calculated for both moving and stationary

targets was calculated for both daytime andtargets. Table 6 shows artificial exhaust heatnight-time in addition to calculations for weekdays

volume for moving targets which was remarkably

Table 6

Results of Calculation of Artificial Exhaust Heat Volume for Moving Targets by Model(%, top value: daytime, bottom value: night-time)50

Journal of Socio-Informatics Vol. 8 No. 1 Feb. 2016

Figure 3

Status of Artificial Exhaust Heat Volume in Daytime on Weekdays(10,000 kJ, total of all vehicles)

high in comparison to that of stationary targets.

for one day on all roads in the Tokyo 23-Ward

Figure 3 shows the weekday daytime status

Area. Total artificial exhaust heat volume forwhere artificial exhaust heat is at its highest. In

these stationary targets was not more thanaddition, as artificial exhaust heat volume wasaround 1.3％ of total artificial exhaust heathigh for all vehicles, regardless of the time, onvolume for both daytime and night-time forthe same roads with remarkable vehicles

moving targets on both weekdays and holidays.

numbers and running speed such as expressways

The greatest characteristic of vehicles by

and ring roads, Table 6 shows artificial exhaustmodel was that artificial exhaust heat volume forheat volume for all these roads. It should becars and regular trucks on all roads was high on

noted that artificial exhaust heat volume forboth weekdays and holidays, during the day andstationary targets was 1,336,070,000 kJ from roadat night. If these values are amalgamated, they

surface and 6,939,200,000 kJ from traffic lights

account for more than 80％ of artificial exhaust51

An Evaluation of Measures against the Urban Heat Island from the Viewpoint of Artificial Exhaust Heat of Road Traffic – An Evaluation Using GIS in the Tokyo 23-Ward Area –IMAI et alheat volume for each road. Artificial exhaust heatand related policies were evaluated. Table 7

volume was high on weekdays for all roads

shows the proportion of reduction of artificial

excluding those in central and south-westernexhaust heat volume after the implementation ofareas in the weekday / holiday category, and it

UHI-related policy for roads with high artificial

was high in the daytime for all roads in theexhaust heat volume in the same way as Table 6.

daytime / night-time category. Metropolitan

It was understood that, for the Tokyo 23-WardExpressway No.5 (Ikebukuro) in the northern

Area overall, the reduction ratio of No.2

area in particular had higher disparity between(suppression of waste heat through choice of fueldaytime and night-time on holidays than otherburned) was high at over 10％, and that it was

roads.

possible to reduce artificial exhaust heat volume,
albeit slightly, in No.3 (improvement of traffic

5. Evaluation of UHI-related policy

flow

by

securing

space

for

cyclists

andpedestrians), No.4 (development of bypasses),

5.1 Evaluation of UHI-related policy for moving

and No.8 (upgrading signal control). In addition,

targets

in No.2, apart from Metropolitan Expressway

A comparison was made between artificial

No.5 (Ikebukuro), the reduction ratios on

exhaust heat volume after the implementation of

weekdays and holidays were of a similar level,

policies using effect functions in UHI-related

and it can be said that the effect of implementation

policies No.1, 2, 3, 4, 5, 8 and 9 for moving targets,

was higher during the day in particular. However,

and artificial exhaust heat volume before theafter the implementation of No.1 (spread of low-

implementation of policies using initial functions

emission vehicles), it was suggested that there is

Table 7

Reduction Ratio of Artificial Exhaust Heat Volume After the Implementation of UHI-Related
Policies (excluding No.7) (%, top value: daytime, bottom value: night-time)

Note: UHI-related policies No.3, 4, 5, 8 and 9 for moving targets had the aim of improving running speed
by alleviating and reducing daytime traffic congestion therefore only daytime artificial exhaust heat volume
after the implementation of these policies was calculated, and as values for night-time were not calculated,
the relevant cells for night-time reduction ratios are marked with a -.
52

Journal of Socio-Informatics Vol. 8 No. 1 Feb. 2016

Table 8 R eduction Ratio of Artificial Exhaust Heat Volume after the Implementation of UHI-Related Policy No.7 (%)

potential for an increase in artificial exhaust heat

reduction ratios were observed for each road as

volume mainly at night. As shown in the previous

shown in Tables 7 and 8, as discrepancies in roadsection, this is because waste heat coefficients bywidth in No.6 and number of traffic signals bymodel and speed differ between day and night,

unit length in No.7 were reflected, differencesand values are set to increase according tooccurred.

improvements in running speed.

By implementing these two related policies, it

Meanwhile, it was discovered that roads withwas discovered that it was possible to reducehigh reduction ratios for artificial exhaust heatartificial exhaust heat volume in the Tokyo 23-

volume differed for each UHI-related policy. For

Ward Area overall by 27.7％ and 30.9％

example, though the above-mentioned No.1,

respectively, and that it was possible to obtain theNo.5 (improvement of access roads to airports

same level of effects in all areas. Consequently, as

and ports), and No.9 (diffusion of ETC) did notshown in section 4.2, though artificial exhaust

manage to reduce artificial exhaust heat volume

heat volume for stationary targets was remarkably

much in the Tokyo 23-Ward Area overall, thelower than that of moving targets, it can be said

Metropolitan Expressway No.5 (Ikebukuro) has

that reduction ratio was higher than that ofa reduction ratio of more than 10％ during themoving targets in this related policy. As the two

day on holidays.

policies mentioned above are implemented in all
Tokyo 23-Ward Area, it is expected that artificial

5.2 
EvaluationofUHI-related

policyforexhaust heat volume will reduce over a wide area.

stationary targets

However, as policy No.7 targets roads installed

In the same way as in the previous section,with traffic signals, implementation effects were

evaluation of UHI-related policies No.6 and 7 for

not obtained on expressways or ring roads withstationary targets was conducted. Artificial

high artificial exhaust heat volume for moving

exhaust heat volume reduction ratio for No.6

targets as shown in the previous section. In(special road surfaces) is shown in Table 7 above,

addition, though this study does not target small-and the reduction ratio for roads with highscale roads that are not published in the roadartificial exhaust heat volume among thosetraffic census, as it is thought to be possible toinstalled with traffic signals for No.7 (increase in

expect implementation effects for these withefficiency of energy consumption equipment) is

policy No.7, it is necessary to develop a more

shown in Table 8. Though differences indetailed road traffic status database and conduct
53

An Evaluation of Measures against the Urban Heat Island from the Viewpoint of Artificial Exhaust Heat of Road Traffic – An Evaluation Using GIS in the Tokyo 23-Ward Area –

evaluation.

IMAI et al

special road surfaces, and the increase inefficiency of energy consumption equipment

6. Conclusion and Future Researchachieved a reduction ratio of around 30％ in
artificial exhaust heat volume, more than

By using GIS as a database development andmoving targets as well as making it possible

information analysis tool in this study, it was

to expect a reduction in artificial exhaust heatpossible to develop a digital map database thatvolume on a wide scale.

reflects road traffic status, and to calculate

The following five points require research inartificial exhaust heat volume for road traffic in

the future.

each road unit. Specific evaluation of UHI-related(1) By more effectively using the various

policies was then conducted, and artificial

functions of GIS and obtaining an accurate

exhaust heat volume was able to be explained

understanding of the spatial changes in roadand displayed in an easy-to-understand mannertraffic status, the calculation of even more

on a digital map with spatial distribution status.

rational values. In addition, the calculation of

The conclusions of this study can be

artificial exhaust heat volume in each roadsummarized by the following three points.

unit taking reciprocal effects due to spatial

Artificial exhaust heat volume for moving(1) 

positioning into account.

targets was remarkably higher than that of(2) Even more detailed evaluation of areas which

stationary targets, and, in particular, artificial

have particularly high UHI density in theexhaust heat volume was high on roads with

Tokyo 23-Ward Area.

remarkable numbers of vehicles and running

Development and evaluation of a detailed(3) 

speeds such as expressways and ring roads.

road traffic status database targeting small-

Artificial exhaust heat volume was particularly

scale roads.

high for cars and regular trucks by model and(4) Understanding of the implementation effects

for weekdays and holidays, and daytime andof each UHI-related policy and proposals fornight-time, artificial exhaust heat volume was

effective policy implementation plans.

mainly high during the day on weekdays.

(5) Application of the evaluation methods in this(2) In UHI-related policy for moving targets, thestudy to other urbans areas with high UHI

suppression of waste heat through choice ofdensity and evaluation.

fuel burned, improvement of traffic flow by
securing space for cyclists and pedestrians,

Notes

development of bypasses and upgrading(1) The GIS used in this study was ESRI’s Arc GIS

signal control managed to reduce artificial

Desktop Arc Info (Ver. 9.2).

exhaust heat volume, even though there wereGeographical Survey Institute (issued 2003 (6(2) differences in degree of reduction. However,issues)), numerical map 2500 (National Geospatial

roads with high artificial exhaust heat volume

Framework) Kanto - 3

reduction ratio differed with each UHI-related(3) Japan Society of Traffic Engineers (2007) 2005

policy.

Road Traffic Census National Road / Street(3) In UHI-related policy for stationary targets,

Traffic Status Survey

54

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Heat Emissions into the Metropolitan Urban

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pp.802-815.

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Mikami T. (2002) Solution Proposed by theindex.html, accessed December 19, 2007

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Produce Warming. Energy Review, Vol.22, No.10,(2004) The Challenge of alleviating the Heat

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