Relative Efficiency of Taxi Services by Data Envelopment Analysis among Cities and Counties in Gyeonggi Province

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*** Ğʑ}ၽᩑǍᬱ ᩑǍ᭥ᬱ ([email protected])

Received March 26 2012, Revised October 5 2012, Accepted April 30 2013

Copyright ⵑ 2013 by the Korean Society of Civil Engineers

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0)

ǣǣȀȀǤǤȀͳͲǤͳʹ͸ͷʹȀǤʹͲͳ͵Ǥ͵͵ǤͶǤͳͷ͹ͳ

ǤǤǤ

GHDἺ#ⴲⱧ㬚#ቻᏮᦂᙲ#⢚㵥ጮ⊂#㚛⢚⛚␂⡢#㱦⳦⛯#⍂⛛

׌۩ผȵୋ೾઴ȵ৉୍߾

Kim, Dae Hoon, Jang, Tae Youn, Song, Je Ryong

Relative Efficiency of Taxi Services by Data Envelopment Analysis among Cities and Counties in Gyeonggi Province

ABSTRACT

This study analyzes the relative efficiences through satisfaction on taxi services of thirty-one cities and counties in Gyeonggi Province.

The DEA (Data Envelopment Analysis) is used to measure efficiencies on the privately owned taxies and the corporate taxies respectively. Efficiency in the DEA is measured relative to the highest observed performance rather than against some average.

Pyeongtaek, Pocheon, Osan, Yangpyeong, and Gapyeong have the highest efficiency in the private owned taxies. And Pyeongtaek, Pocheon, Osan, and Gapyeong have the highest one in the corporate taxies. These cities and counties have the ability to use less resources for equal efficiency relative to others. Rank-sum test proves that there is no statistical difference in efficiency between the privately owned taxies and the corporation owned taxies.

Key words : Data Envelopment Analysis (DEA), Taxi Services, Relative Efficiency, Gyeonggi-Do

Ⅹಾ

ᅙᩑǍ۵Ğʑࠥԕ31}᜽Ǒᨱᕽᬕ⧪ࡹŁᯩ۵┾᜽᮹ᕽእᜅᨱݡ⦽อ᳒ࠥෝʑⅩಽḡᩎᄥ┾᜽᮹⬉ᮉᖒᮥ⠪a⦹ᩡ݅. ⬉ᮉᖒ⠪a۵

}ᯙ┾᜽᪡ჶᯙ┾᜽ಽǍᇥ⦹ᩍᇥᕾ⦹ᩡ݅. ᜽읆Ǒᄥ⬉ᮉᖒ⠪a۵DEA(Data Envelopment Analysis) ༉⩶ᮥᯕᬊ⦹ᩡ݅. ᅙᩑǍᨱᕽᔍ ᬊ⦽ᯱഭ۵Ğʑࠥ31}᜽읆Ǒ᮹}ᯙၰჶᯙ┾᜽ෝݡᔢᮝಽ᳑ᔍ⦽ᯱഭᯕ݅. 31}ḡᯱℕ᮹⬉ᮉᖒ⠪ađŝෝᔕ⠕ᅕ໕, }ᯙ┾᜽᮹Ğ ᬑ⬉ᮉᱢᯙḡᯱℕ۵⠪┾᜽, ⡍⃽᜽, ᪅ᔑ᜽, ᧲⠪Ǒ, a⠪Ǒ॒5}ḡᯱℕಽӹ┡ԍ݅. ჶᯙ┾᜽۵⠪┾᜽, ⡍⃽᜽, ᪅ᔑ᜽, a⠪Ǒ॒4}ḡ

ᯱℕᯕ݅. ᯕॅḡᯱℕॅᮡᯝᱶ⦽ᙹᵡ᮹อ᳒ࠥෝᔾᔑ⧉ᨱᯩᨕᕽ݅ෙḡᯱℕᅕ݅ᱢᮡ᧲᮹ᕽእᜅෝᔍᬊ⦹۵܆ಆᮥ⪶ᅕ⦹Łᯩᮭᮥ᮹

ၙ⦽݅. }ᯙ┾᜽᪡ჶᯙ┾᜽eDMUॅ᮹☖ĥᱢ₉ᯕaᯩ۵ḡෝá᷾⦹ʑ᭥⦹ᩍᙽ᭥⧊á᷾(Rank- sum Test)ᮥ᜽⧪⦽đŝࢱḲ݉e ᨱᕽእᜅᱽŖᨱ঑ෙอ᳒ࠥ₉ᯕ۵ᨧ۵äᮝಽᇥᕾࡹᨩ݅.

áᔪᨕ ᯱഭ⡍௞ᇥᕾ(DEA), ┾᜽ᕽእᜅ, ᔢݡᱢ⬉ᮉᖒ, Ğʑࠥ

1. ᕽು

1.1 ઴֜ଭࢼլࢫࡧୡ

ྙᱥᬕ⧪ ᕽእᜅ(door to door)ෝ ᱽŖ⦹۵ Łɪᕽእᜅ᮹ ʑ܆ᮥ aḥ ┾᜽a ᯕᬊᯱॅ᮹ ݅᧲⦽ ᫶Ǎෝ ∊᳒᜽┅ḡ ༜⦽ ₥ ᵡ

ݡᵲƱ☖ᙹ݉ᮝಽ ᱥ௞ࡹŁᯩ݅. 2011֥ʑᵡᮝಽ Ğʑࠥ۵ 35,778ݡa ᬕ⧪ࡹŁᯩ۵ ၹ໕, ᕽᬙ᜽۵2001֥ᇡ░ ┾᜽ᱢᱶȽ༉

İࣀėॡ

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Table 1. Facts of Taxis(2011)

Total No. of Operating Taxis

No. of Privately Owned Taxis

No. of Corporation Owned Taxis

No. of Corporation Companies

Gyeonggido 35,778 25,304 10,474 193

Seoul 72,355 49,504 22,851 255

Table 2. Previous Researches

Field Research Contents

Taxi Policy

1. Taxi Problems and Solution (Kang, Sang Uook, 2009)

2. Study on Actual Taxi Total Quantity of each Region (Kang, Sang Uook, 2009) 3. Optimal Taxi Quantity (Kim, Kyung Hwan et al., 1998)

4. Characteristics on Corporation Owned Taxis (Kim, Rak Gi et al., 2007) Revenue 1. Simplifing Taxi Fare System (Song, Je Ryong et al., 2005)

2. Study on Taxi Revenue in Seoul (Lee, Seung Jae et al., 2001)

Taxi Management

1. Assesment of Taxi Management and Service (Gyeonggi Research Institute, 2010) 2. Efficiency and Economy of Scale for Taxi Industry (Min, Seung Gi, 2003)

3. Improvement of Management and Service of Corporation Owned Taxi (Yoon, Joon Byung, 2009) Taxi Accident 1. Taxi Company Management and Traffic Accident Reduction (Hong, Chang Eui, 1998)

2. Modeling on Taxi Accidents (Jang, Tae Youn, 2003)

Others

1. DEA (Data Envelopment Analysis) Approach for Evaluating the Efficiency of Exclusive Bus Routes (Han, Jin Seok et al., 2010) 2. DEA (Data Envelopment Analysis) Approach for Evaluating the Efficiency of Exclusive Bus Routes (Han, Jin Seok et al., 2009) 3. Efficiency of Airport Operation by DEA in Asia (Lee, Young Hyuk et al., 2004)

4. Efficiency of Airlines' and Cargo Divisions-Using a DEA Model Approach (Hong, Suk Jin, 2004)

5. Efficiency in the Seoul's Urban Bus Industry Using Data Envelopment Analysis (Oh, Mi Young et al., 2002)

7,000 ݡෝʑᵡᮝಽ┾᜽ⅾపᱽෝ᜽⧪⦹Łᯩᮝ໑ᯕ⬥┾᜽

Ŗɪᯕᯕ൉ᨕḡḡᦫŁ72,355ݡᙹᵡ᮹┾᜽aᬕ⧪ࡹŁᯩ݅

(Table 1).

2011֥8ᬵĞʑࠥᨱᕽ᜽⧪⦽“ĞʑࠥၝƱ☖ᯕᬊ⧪┽”᳑ᔍ ᨱᕽࠥԕݡᵲƱ☖ᙹ݉ᄥᯕᬊอ᳒ࠥ۵ᙹࠥǭᱥ℁ᯕ48.7%

ಽ aᰆ׳ᦹŁŲᩎქᜅ(47.0%), eᖁქᜅ(45.2%), ษᮥქᜅ (41.7%), ᯝၹ℁ࠥ(39.7%), ┾᜽(35.9%) ᙽᯕᨩ݅. ᯱaᬊอ᳒

ࠥ۵62.0%ಽݡᵲƱ☖ᅕ݅׳ᦹ݅. ┾᜽᮹ᯕᬊอ᳒ࠥaԏᮡ

ᯕᮁ۵ ݅᧲⪵ ࡹḡ ༜⦽ ᕽእᜅ ၰ ᕽእᜅ ḩ ᱡ⦹ᨱ ʑᯙ⦽

äᮝಽᯕ۵┾᜽ᯕᬊශqᗭಽᯕᨕᲙ┾᜽ᬕᙹšಉ᳦ᔍᯱॅ᮹

ᦩᱶᱢᯙᙹ᯦ၰĞᩢᨱᩢ⨆ᮥᵝŁᯩ۵ᝅᱶᯕ݅(ᘂᱽൂ, 2010).

┾᜽ྙᱽ⧕đᮥ᭥⧕ᕽ۵ ┾᜽ᙹ᫵ݡእ┾᜽Ŗɪ᮹ᇩɁ⩶᮹

ྙᱽ, ┾᜽ᯕᬊᯱ⊂໕ᨱᕽ᮹⠙ญ⦹Łᦩᱥ⦽ᕽእᜅᨱݡ⦽᫶Ǎ,

┾᜽ᨦℕ᮹ Ğᩢ}ᖁ ॒ᯕ ⦥᫵⦹݅.

┾᜽(ʑᔍ)aᱽŖ⦽ᕽእᜅ۵ʑᔍ⊽ᱩࠥ, ₉పᔢ┽, ┾᜽ᬕ⧪,

┾᜽᫵ɩ ॒ 4} ⧎༊ᮝಽ Ǎᖒࡽ݅. ʑᔍ⊽ᱩࠥ۵ ᬕᱥʑᔍ᮹

ᯕ໑, ₉పᔢ┽۵ ₉పℎđࠥ, ₉ప Ԫ/ӽႊ ᱢᱶᖒ, ᜚₉q ၰ

₉పԕᇡᔢ┽॒ᯕ݅. ┾᜽ᬕ⧪ᕽእᜅ۵ᬕ⧪ᵲ᜚₉q, ჶȽᵡᙹ, ᦩᱥᬕ⧪, ᬕ⧪┽ࠥ ॒ᯕ໑, ┾᜽᫵ɩᮡ ᇡݚ᫵ɩ ᫵Ǎ, ᱶ⪶⦽

Ñᜅ෥ࠩ, ᩢᙹ᷾ၽ⧪॒ᯕ݅. ᯕ్⦽ᕽእᜅ۵ᯕᬊ~᮹อ᳒ࠥᨱ

ⓑ ᩢ⨆ᮥ ၙ⊽݅.

(ʑᔍ)᮹ᕽእᜅᨱݡ⦽ᯕᬊᯱa۱ӝ۵⬉ᮉᖒ(อ᳒ࠥ)ෝ᳑ᔍ⦹

Ł, ࠥ᜽e ᕽእᜅ ⬉ᮉᖒ(อ᳒ࠥ)ᨱ ᯩᨕ ₉ᯕa ᯩ۵ḡ እƱ

ᕽĞʑࠥԕbࠥ᜽᮹⬉ᮉᖒᮥᝅ᷾ᱢᮝಽ⊂ᱶ⧕ᅕŁእ⬉ᮉᱢ ᯙࠥ᜽᮹⬉ᮉᖒᮥᱽŁ᜽┍ᙹᯩ۵⩥ᝅᱢᯙႊᦩᮥࠥ⇽⦹Łᯱ

⦽݅. ⬉ᮉᖒ⠪a۵}ᯙ┾᜽᪡ჶᯙ┾᜽ಽbbǍᇥ⦹ᩍᇥᕾ⦹

ᩡ݅.

1.2 টෘ઴֜Ցഠ

Table 2 ᨱᕽ۵ ┾᜽šಉᱶ₦ᨱ ݡ⦽ ᅕŁᕽ᪡ᅙ ᩑǍᨱᕽ

⪽ᬊ⦹íࢁᇥᕾ༉⩶ᯙDEAෝ⪽ᬊ⦽ʑ᳕ᖁ⧪ᩑǍෝᅕᩍᵝŁ

ᯩ۵ߑ, ✚⯩┾᜽šಉᩑǍ۵ݡᇡᇥࠥ᜽ŝᱽ₉ᬱᨱᕽᙹ⧪⦽

äᯕݡᇡᇥᯕ໑, ༉⩶Ǎ⇶ᯕӹᇥᕾႊჶುᮥ☖⦽⦺ᚁᱢ⊂໕ᨱ ᕽ᮹ ᩑǍ۵ ၙၙ⦽ äᮝಽ ӹ┡ԍ݅.

DEAෝᯕᬊ⦽⦺ᚁᱢ⊂໕ᨱᕽ᮹⬉ᮉᖒᇥᕾᮝಽ۵᪅ၙᩢ

॒(2002)ᯕݡᵲƱ☖ᙹ݉᮹݅ෙ⦽⇶ᯙᕽᬙ᜽᜽ԕქᜅᬕᘂᨦᨱ

ݡ⦽⬉ᮉᖒᮥᇥᕾ⦹ᩡᮝ໑, ⦽ḥᕾ॒(2009, 2010)ᮡᕽᬙ᜽

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eᖁქᜅיᖁၰḡᖁქᜅיᖁ᮹⩶⠪ᖒᮥᇥᕾ⦹ᩡ݅. Ŗ⧎ᇥ᧝

ᨱᕽ۵ ᯕᩢ⩢ ॒(2004)ᯕ ᦥ᜽ᦥ Ŗ⧎ ᬕᩢ ⬉ᮉᖒᮥ ⪮ᕾḥ (2004) ᯕ⧎Ŗ⪵ྜྷᇡྙŝ⧎Ŗᔍ⬉ᮉᖒᨱš⦽ᩑǍෝᙹ⧪⦹ᩡ

┅ʑ᭥⦽äᯕအಽ⬉ᮉᖒ⨆ᔢᮥ᭥⧕ྕᨨᮥ⧕᧝⦹۵ḡႊ⨆ᮥ

݅᧲⦽ᱶ₦ᯕḥ⧪ࡹᨩᮭᨱࠥᇩǍ⦹Ł┾᜽aᱽŖ⦹۵ᕽእᜅ

ᱥྕ⦽ ᔢ┽ᯕ݅.

2. ᇥᕾႊჶၰᯱഭ

2.1 DEA ंজ

⦹ʑ ᭥⧕ᕽ aᰆ ձญ ⪽ᬊࡹŁ ᯩ۵ DEAෝ ᅙ ᩑǍᨱᕽࠥ

ᯕᬊ⦹ᩡ݅. DEA۵⚍᯦ŝᔑ⇽šĥa໦⪶⦹íᱶ᮹ࡹḡᦫᮡ

᮹ᔍđᱶ݉᭥᮹ᔢݡᱢ⬉ᮉᖒᮥ ⠪a⦹۵äᮝಽๅᬑ݅᧲⦽

ᇥ᧝ᨱᕽᔍᬊࡹŁᯩ݅. ᅙᩑǍᨱᕽDEAෝ⪽ᬊ⦽ᯕᮁ۵DEA ۵ ᔢݡᱢ ⬉ᮉᖒ ⊂ᱶႊჶᯕʑ ভྙᨱ ⬉ᮉᖒ ᱱᙹ ᯕ᫙ᨱࠥ

݅ෙႊჶುᨱᕽᱽ᜽⦹ḡ༜⦹۵2aḡᮁᬊ⦽ᱶᅕෝᱽ᜽⦽݅.

ෝᱽ᜽⧉ᮝಽᕽᄅ⊹ษ┚᮹ɝÑෝᱽŖ⦽݅۵äᯕŁ, ᕽእᜅ(⚍

᯦)ŝอ᳒ࠥ(ᔑ⇽)᮹᫵ᗭᄥಽእ⬉ᮉ᮹ᱶࠥᨱš⦽ᱶᅕෝᱽŖ

⦽݅۵ äᯕ݅.

DEA ᯕುᨱݡ⦽ᖅ໦ᮥCCR༉⩶ᮝಽᇡ░᜽᯲⦹۵ߑձญ

⪽ᬊࡽ༉⩶ᯕ௝۵ᱱŝ݅᧲⦽⧕ᕾᯕa܆⦹݅۵ᱱভྙᯕ݅.

CCR ༉⩶ᮡ⢽⩥ࡹ۵⩶┽ᨱ঑௝እᮉ༉⩶(Ratio Model), ᜚ᙹ༉

⩶(Multiplier Model), əญŁ ⡍௞༉⩶(Envelopment Model)

॒ᮝಽ Ǎᄥࡹ۵ߑ, ⬉ᮉᖒ ⠪aෝ ᭥⧕ ᮁᔍ⦽ ༊ᱢᮥ ᭥⦹ᩍ

᳑Ḣࡽ᮹ᔍđᱶ݉᭥(Decision Making Units : DMU)e᮹ᔢݡ ᱢ ⬉ᮉᖒᮥ ⠪a⦹۵ ᯝ᳦᮹ ᖁ⩶ĥ⫮ʑჶᯕ݅. ᅙ ᩑǍᨱᕽ

௡b⚍᯦ၰᔑ⇽᫵ᗭᨱݡ⦽bb᮹aᵲ⊹⧊ᨱݡ⦽እᮉᯕ݅.

DMU᮹ ⬉ᮉᖒ = ᔑ⇽ / ⚍᯦

=

_{Ɛ á Î}ā^ƌ

^Ɠ

^Ɛ

^Ɨ

^ƐƉ

^/

_{Ƈ á Î}ā^Ƌ

^Ɣ

^Ƈ

^Ɩ

^ƇƉ

⁽¹⁾

Ɖ ^{= 1, 2,} ⲷ,s (DMU᮹ ᙹ)

ᩍʑᕽnᮡ⬉ᮉᖒᮥ⊂ᱶ⦹Łᯱ⦹۵DMU᮹ᔑ⇽᫵ᗭ᮹ᙹෝ, mᮡ⚍᯦᫵ᗭ᮹ᙹෝӹ┡ԙ݅. Ɨ

ƐƉ

᪡ Ɩ

ƇƉ

۵ᔑ⇽ྜྷŝ⚍᯦ྜྷ᮹

ᝅᱽšₑ⊹ෝӹ┡ԕ۵ᔢᙹᯕ݅. Ɠ

_Ɛ

, _Ɣ

_Ƈ

۵ݡᔢDMU᮹ᔑ⇽

ၰ⚍᯦᫵ᗭ᮹ᔢݡᱢᵲ᫵ࠥෝӹ┡ԕ۵aᵲ⊹ᯙߑ, ₙ᳑Ḳ⧊ᮝ

ಽ ᔍᬊࡽ ༉ु DMU᮹ ᯱഭෝ ᯕᬊ⦹ᩍ Ǎ⧕ḥ݅. ⬉ᮉᖒ᮹

ɚݡ⪵۵ň᜾(1)᮹ɚݡ⪵ෝ☖⧕ᕽa܆⦹໑⬉ᮉᖒsᮡ⧎ᔢ

0 ŝ1ᔍᯕ᮹ჵ᭥ෝ≉⦹íࡽ݅. ᯥ᮹⠪aݡᔢ ¦®

×

᮹⬉ᮉᖒ

↽ݡsᮥ Ɔ

_×

௝ ⧁ ভ ݅ᮭ᮹ እᖁ⩶ĥ⫮ჶᯕ ࡽ݅.

max _Ɔ

_×

=

_{Ɛ á Î}

ā

^ƌ

^Ɠ

^Ɛ

^Ɨ

^Ɛ×

^/

_{Ƈ á Î}

ā

^Ƌ

^Ɣ

^Ƈ

^Ɩ

^Ƈ×

(2) subject to

ā

_{Ɛ á Î}^ƌ

^Ɠ

^Ɛ

^Ɨ

^ƐƉ

^/ ā

_{Ƈ á Î}^Ƌ

^Ɣ

^Ƈ

^Ɩ

^ƇƉ

^ⴌ1, ^Ɖ ^=1, ^ⲷ,s

_Ɠ

_Ɛ

, _Ɣ

_Ƈ

ⴍ Ļ >0, _Ɛ =1,ⲷ,n, Ƈ =1,ⲷ,m

CCR༉⩶ᨱ ᯕᬊࢁ Ɠ

Ɛ

ŝ Ɣ

Ƈ

᮹ sᯕ ᭥ ᜾ᮥ ☖⧕ ᔑ⇽ࡽ݅.

᜾(2)᮹ℌჩṙᱽ᧞᳑Õᮡ ¦®

_Ɖ

᮹⬉ᮉᖒᨱݡ⦽ᱽ᧞᳑Õᮝಽ ᕽĥᔑࡽ⧕a1ᮥⅩŝ⦹ḡᦫࠥಾᅕᰆ⧕ᵡ݅. ࢱჩṙᱽ᧞᳑Õ ᮹ Ļ ᮡๅᬑ᯲ᮡ᧲᮹ᔢᙹಽᕽĥᔑࡽ Ɠ

Ɛ

ŝ Ɣ

Ƈ

a᧲ᙹaࡹࠥಾ

ᅕᰆ⧕ᵡ݅. ᜾(2)۵እᖁ⩶, እᅝಾ(nonconvex)ᯕ໑, ⠪aݡᔢ ᙹaฯᮡĞᬑᨱ۵⧕᮹ĥᔑᯕᨕಅᬕྙᱽaᯩ݅. ĥᔑᔢ᮹

ӽᱱᮥ⧕đ⦹ʑ᭥⧕ๅ}ᄡᙹෝ⪽ᬊ⦹ᩍCCR༉⩶ᮥ᜾(3)⃹ౝ

ᖁ⩶ĥ⫮ྙᱽಽ ݡℕ⧁ ᙹ ᯩ݅.

max ā

_{Ɛ á Î}^ƌ

^Ɠ

^Ɛ

^Ɨ

^Ɛ×

(3) subject to

ā

_{Ƈ á Î}^Ƌ

^Ɣ

^Ƈ

^Ɩ

^ƇƉ

^{= 1}

ā

_{Ɛ á Î}^ƌ

^Ɠ

^Ɛ

^Ɨ

^ƐƉ

^- ā

_{Ƈ á Î}^Ƌ

^Ɣ

^Ƈ

^Ɩ

^ƇƉ

^ⴌ0, ^Ɖ ^=1, ^ⲷ,s

_Ɠ

_Ɛ

, _Ɣ

_Ƈ

ⴍ Ļ >0, _Ɛ =1, ⲷ,n, Ƈ =1, ⲷ,m

CCR༉⩶ᮡȽ༉᮹ᙹᯖᇩᄡ(CRS, Constant Returns to Scale)

ᮥaᱶ⦹ᩡ݅. ⦹ḡอ༉ुDMUॅᯕȽ༉᮹ᙹᯖᇩᄡᔢ┽௝Ł

݉ᱶ⧁ᙹᨧ݅. Ƚ༉᮹ᙹᯖaᄡ(VRS, Variable Returns to Scale)

ᷪ, Ƚ༉ᨱݡ⦽ᙹᯖ᷾a, ᇩᄡ, qᗭ॒༉ࢱ⡍⧉⦹ᩍᔾᔑ⬉ᮉᖒ ŝȽ༉ᨱݡ⦽ᙹᯖᮥ᦭ಅᵝ۵BCC༉⩶(Banker, Charnes, and Cooper 1984)ᯕ ᱽ᜽ࡹᨩ݅.

max _Ɔ

_×

= Þ

_{Ɛ á Î}

ā

^ƌ

^Ɠ

^Ɛ

^Ɨ

^Ɛ×

^{à Ɠ}

^×

^ß ^/ ā

_{Ƈ á Î}^Ƌ

^Ɣ

^Ƈ

^Ɩ

^Ƈ×

(4) subject to

Þ

_{Ɛ á Î}

ā

^ƌ

^Ɠ

^Ɛ

^Ɨ

^ƐƉ

^{à Ɠ}

^×

^ß ^/ ā

_{Ƈ á Î}^Ƌ

^Ɣ

^Ƈ

^Ɩ

^ƇƉ

^ⴌ1,

Ɖ =1,ⲷ,s

Ɠ

Ɛ

, _Ɣ

_Ƈ

ⴍ Ļ >0, _Ɛ =1, ⲷ,n, Ƈ =1, ⲷ,m

(4)

Table 3. Survey

Taxi Monitoring Passenger Satisfaction

Survey Contents Drivers’Kindness, Vehicle Condition, Taxi Operation, and TaxiFare

Passenger Satisfaction based on Drivers’Kindness, Vehicle Condition, Taxi Operation, and Taxi Fare Target Privately Owned Taxies and

Corporation Owned Taxies in Gyeonggido

Passengers Riding Taxis in Garage within Gyeonggido (Deluxe Taxi excluded)

Survey Time 2010.9.6. ~ 2010.10.6 2010.9.6. ~ 2010.10.6

Sample Size 2,300 Corporation Owned Taxies : about 20% of Total Taxis 3,700 Privately Owned Taxies: about 15% of Total Taxis

904 Passengers of Corporation Owned Taxies 2,110 Passengers of Privately Owned Taxies

᜾ (5)ෝ CCR༉⩶ᨱᕽ᪡ zᮡ ႊჶᨱ ঑௝ ᖁ⩶ĥ⫮ྙᱽಽ

ᄡ⪹⦹໕

max

_{Ɛ á Î}

ā

^ƌ

^Ɠ

^Ɛ

^Ɨ

^Ɛ×

^{à Ɠ}

^×

(5) subject to

_{Ɛ á Î}

ā

^ƌ

^Ɠ

^Ɛ

^Ɨ

^Ɛƍ

^{= 1}

_{Ɛ á Î}

ā

^ƌ

^Ɠ

^Ɛ

^Ɨ

^ƐƉ

^- ā

_{Ƈ á Î}^Ƌ

^Ɣ

^Ƈ

^Ɩ

^ƇƉ

^- ^Ɠ

^×

^ⴌ0, ^Ɖ ^=1,ⲷ,s

_Ɠ

_Ɛ

, _Ɣ

_Ƈ

ⴍ Ļ >0, Ɛ =1,ⲷ,n, Ƈ =1,ⲷ,m

Ɠ

_×

۵ ᇡ⪙ᱽ᧞ᮥ ၼḡ ᦫ۵ sᮝಽᕽ Ƚ༉ᨱ ݡ⦽ ᅕᙹḡ⢽

(indicator of returns to scale) ෝ᮹ၙ⦽݅. Ƚ༉ᨱݡ⦽ᅕᙹa

᷾aᯙ Ğᬑᨱ۵ Ɠ

_×

< 0, Ƚ༉ᨱ ݡ⦽ ᅕᙹa ᯝᱶ⦹໕ Ɠ

_×

= 0, Ƚ༉ᨱ ݡ⦽ ᅕᙹa qᗭ⦹໕ Ɠ

_×

> 0ᯕ ࡽ݅.

2.2 ঃ۩ୡตଘনंজ

⬉ᮉᖒ௡ ✚ᱶ ᳑Ḣᯕ ᱽ⦽ࡽ ᯱᬱ ԕᨱᕽ ↽ݡ᮹ ᔑ⇽ྜྷᮥ

₞⇽⧕ԕ۵ᔾᔑʑᚁᮥั⦽݅(ၶอ⯍, 2008). ᅙᩑǍᨱᕽ۵ʑᚁ

⬉ᮉᖒ(Technical Efficiency)ŝȽ༉⬉ᮉᖒ(Scale Efficiency)

ᮥᇥᕾ⦹۵ߑ, ʑᚁ⬉ᮉᖒᮡᯝᱶప᮹ᔑ⇽ྜྷᮥᔾᔑ⧁ভ⚍᯦

ྜྷᮥ aᰆ ᱢí ᔍᬊ⦹۵ ✚ᱶ DMU᮹ ᔾᔑ᫵ᗭ ᄂ░ᨱ ݡ⦽

༉ुDMU᮹ᔾᔑ᫵ᗭᄂ░᮹ᔢݡᱢእᮉಽ⊂ᱶࡽ݅. ʑᚁ⬉ᮉ ᖒᮡ݅᜽Ƚ༉⬉ᮉᖒ(Scale Efficiency)ŝᙽᙹʑᚁ⬉ᮉᖒ(Pure Technical Efficiency) ᮝಽǍᇥ⧁ᙹᯩ݅. Ƚ༉⬉ᮉᖒᮡDMU᮹

ᔾᔑȽ༉aᔍ⫭ᱢᮝಽ↽ᱢȽ༉ᔢ┽ᯙaෝ⊂ᱶ⦹۵äᯕ໑, ᙽᙹʑᚁ⬉ᮉᖒᮡʑᚁ⬉ᮉᖒᨱᕽȽ༉⬉ᮉᖒ᮹⬉ŝෝᱽÑ⦽

äᮝಽȽ༉⬉ᮉᖒᯕ1ᯝভʑᚁ⬉ᮉᖒŝᙽᙹʑᚁ⬉ᮉᖒᮡzᮡ

sᮥ wí ࡽ݅.

⬉ᮉᖒᱱᙹa1ᯙĞᬑ┾᜽᮹ᕽእᜅอ᳒ࠥᨱᯩᨕᔢݡᱢ

⬉ᮉᖒᯕ100%௝Łᅝᙹᯩᮝ໑, 1ᅕ᯲݅ᮡࠥ᜽ᯙĞᬑ┾᜽ᕽ

እᜅᨱᯩᨕᔢݡᱢእ⬉ᮉᖒᮥӹ┡ԙ݅. 1ᅕ᯲݅ᮡ⬉ᮉᖒᮥ

ӹ┡ԕ۵Ğᬑࠥ᜽e᮹⚍᯦ੱ۵ᔑ⇽᮹ᙹᵡᨱᯩᨕᕽəࠥ᜽ෝ

⠪a⦹۵ߑ እƱࡽ ࠥ᜽ᨱ እ⧕ እ⬉ᮉᱢᯙ äᮥ ᮹ၙ⦽݅.

2.3 ंজୀ߹

⦹ᩡᮝ໑ᖙᇡԕᬊᮡTable 3ŝz݅. ┾᜽༉ܩ░ย᳑ᔍ۵ࠥԕ

ჶᯙ┾᜽ၰ}ᯙ┾᜽ෝݡᔢᮝಽ᜚~᯦ᰆᨱᕽ┾᜽ၰ┾᜽ʑᔍ aᱽŖ⦽ᕽእᜅ॒᮹ᝅ┽ෝ᳑ᔍ⦹ᩡ݅. ᳑ᔍԕᬊᮝಽ۵ʑᔍ⊽

ᱩࠥ, ₉పᔢ┽, ┾᜽ᬕ⧪, ┾᜽᫵ɩ॒ ᯕ݅. ᜚~อ᳒ࠥ᳑ᔍ ۵┾᜽ෝᯕᬊ⦽᜽ၝᯕ┾᜽(ʑᔍ)aᱽŖ⦽ᕽእᜅ॒ᨱݡ⧕

อ᳒⦽ᱶࠥෝ᳑ᔍ⦽ᯱഭᯕ݅(Table 4). ┾᜽ၰ┾᜽ʑᔍᱽŖ

ᕽእᜅ(┾᜽༉ܩ░ย᳑ᔍ)᪡᜚~อ᳒ࠥ۵100ᱱอᱱᮥʑᵡᮝ ಽ ⦽݅.

}ᯙ┾᜽᮹ʑᔍ⊽ᱩࠥᱱᙹ۵65.11ᱱ, ₉పᔢ┽۵73.55ᱱ,

┾᜽ᬕ⧪ၰ┾᜽᫵ɩᕽእᜅ۵bb96.9ᱱŝ96.09ᱱᮥᅕᯕŁ

ᯩ݅. ᜚~᮹อ᳒ࠥ۵74.16ᱱᮝಽӹ┡ԍ݅. ʑᔍ⊽ᱩࠥ, ₉పᔢ

┽, ┾᜽᫵ɩᕽእᜅ۵᜽ḡᩎᱱᙹa׳ᦹᮝ໑, ┾᜽ᬕ⧪ᕽእᜅ۵

Ǒḡᩎ ᱱᙹa ׳í ӹ┡ԍ݅. อ᳒ࠥ۵ ᜽ḡᩎᯕ 74.82ᱱᮝಽ

Ǒḡᩎᅕ݅׳íӹ┡ӹ᜽ḡᩎ᮹┾᜽ᯕᬊ~อ᳒ࠥa׳ᮡäᮝ ಽ ӹ┡ԍ݅.

ჶᯙ┾᜽᮹Ğᬑʑᔍ⊽ᱩࠥᱱᙹ۵66.49ᱱ, ₉పᔢ┽۵72.84 ᱱ, ┾᜽ᬕ⧪ၰ┾᜽᫵ɩᕽእᜅ۵bb91.55ᱱŝ95.53ᱱᮥ

ᅕᯕŁᯩ݅. ᜚~᮹อ᳒ࠥ۵72.32ᱱᮝಽӹ┡ԍ݅. ʑᔍ⊽ᱩࠥ,

₉పᔢ┽, ┾᜽᫵ɩᕽእᜅ۵᜽ḡᩎᱱᙹa׳ᦹᮝ໑, ┾᜽ᬕ⧪ᕽ እᜅ۵Ǒḡᩎᱱᙹa׳íӹ┡ԍ݅. อ᳒ࠥ۵᜽ḡᩎᯕ73.88ᱱ ᮝಽǑḡᩎᅕ݅׳íӹ┡ӹ}ᯙ┾᜽᪡ᮁᔍ⦽đŝෝᅕᩡ݅.

┾᜽ၰᬕᱥᯱaᱽŖ⦽ᕽእᜅᵲʑᔍ⊽ᱩࠥ۵}ᯙ┾᜽ᅕ݅

ჶᯙ┾᜽᮹ᱱᙹa׳íӹ┡ԍ݅, ₉పᔢ┽, ┾᜽ᬕ⧪ၰ┾᜽᫵ɩ ᨱݡ⦽⠪a۵ჶᯙ┾᜽ᅕ݅}ᯙ┾᜽᮹ᱱᙹa׳ᦥᱥၹᱢᮝಽ

׳ᮡ ᕽእᜅෝ ᱽŖ⦽݅۵ äᮥ ᦭ ᙹ ᯩ݅.

(5)

Table 4. Statistics of Passenger Satisfaction and Taxi Service Drivers’

Kindness

Vehicle Condition

Taxi Operation

Taxi Fare

Passenger Satisfaction

Privately Owned Taxies

Cities

Mean 68.18 73.59 91.51 96.61 74.82

Min. 57.48 54.76 83.43 80.28 61.97

Max. 83.14 79.88 98.39 100.0 83.94

Std. Dev. 7.14 5.50 4.05 3.74 5.51

Counties

Mean 44.41 73.33 93.94 92.63 69.71

Min. 37.41 69.49 88.11 89.82 64.65

Max. 50.93 78.17 97.22 97.68 76.03

Std. Dev. 4.82 3.53 3.48 3.05 4.21

Total

Mean 65.11 73.55 91.83 96.09 74.16

Min. 37.41 54.76 83.43 80.28 61.97

Max. 83.14 79.88 98.39 100.0 83.94

Std. Dev. 10.71 5.38 4.13 3.96 5.72

Corporation Owned Taxies

Cities

Mean 69.02 73.24 91.32 96.32 73.88

Min. 57.17 57.51 82.34 82.76 64.55

Max. 83.13 80.43 98.10 100.0 81.75

Std. Dev. 7.17 5.25 3.95 3.89 5.26

Counties

Mean 49.38 70.15 93.09 90.21 61.83

Min. 42.75 66.60 88.42 84.69 55.16

Max. 55.99 74.67 96.53 94.33 75.54

Std. Dev. 4.73 3.11 3.04 3.48 8.07

Total

Mean 66.49 72.84 91.55 95.53 72.32

Min. 42.75 57.51 82.34 82.76 55.16

Max. 83.13 80.43 98.10 100.0 81.75

Std. Dev. 9.70 5.21 3.95 4.42 7.11

Table 5. Input and Output Variables Variable

Input Drivers’Kindness, Vehicle Condition, Taxi Operation, Taxi Fare

Output Satisfaction

3. ᜽ · Ǒᄥ⬉ᮉᖒ⠪a

᮹ᔍđᱶ݉᭥(DMU)ಽᖁᱶ⦹ᩍʑᚁ⬉ᮉᖒ, ᙽᙹʑᚁ⬉ᮉᖒ, Ƚ

༉⬉ᮉᖒᮥ⊂ᱶ⦹ᩡ݅. əญŁ┾᜽᮹✚ᖒᨱ঑௝}ᯙ┾᜽᪡

ჶᯙ┾᜽ಽǍᇥ⦹ᩍ⬉ᮉᖒ₉ᯕaᯩ۵ḡෝᇥᕾ⦹ᩡ݅. Table 5 ᨱᕽᅕᩍᵝ۵ä⃹ౝ⚍᯦ᄡᙹ۵┾᜽ᨱ᜚₉⦹ᩍ᜚~᯦ᰆᨱᕽ

┾᜽ၰ┾᜽ʑᔍaᱽŖ⦽ᕽእᜅᯕŁ, ᔑ⇽ᄡᙹ۵᜚~᮹อ᳒ࠥ

ᯕ݅.

3.1 Թ଴೿ਏ 3.1.1 ตଘন

6 ŝ z݅. ᩍʑᕽ ⊂ᱶ⦹۵ ⬉ᮉᖒ sᮡ ʑᔍ⊽ᱩࠥ, ₉పᔢ┽,

┾᜽ᬕ⧪, ┾᜽᫵ɩᮥ⚍᯦ྜྷಽ⦹ᩍᔑ⇽ྜྷᯙᕽእᜅอ᳒ࠥෝ

ǑŝእƱ⦹ᩍᔢݡᱢᮝಽ⊂ᱶ⦽sᯕ݅. ⊂ᱶࡽsᯕ1ᯙĞᬑ

⦽݅.

CCR ༉⩶ᨱ ᮹⦽ ʑᚁ⬉ᮉᖒ ᇥᕾđŝᨱ ঑෕໕ Ğʑࠥԕ

31}ḡᯱℕᵲᔢݡᱢᮝಽ⬉ᮉᱢᯙࠥ᜽۵⬉ᮉ⊹a1᮹sᮥ

aḡ۵⠪┾᜽, ⡍⃽᜽, ᪅ᔑ᜽, ᧲⠪Ǒ, a⠪Ǒ॒5}ಽӹ┡ԍ݅.

እ⬉ᮉᱢᯙࠥ᜽۵ᔢݡᱢᮝಽaᰆ⬉ᮉ⊹aԏᮡ᮹ᱶᇡෝ⡍⧉

⦹ᩍ26}ಽӹ┡ԍ݅. ᮹ᱶᇡ᮹Ğᬑ0.703ᮝಽ⩥ᰍ᮹อ᳒ࠥ۵

┾᜽(ʑᔍ)aᱽŖ⦽ᕽእᜅ᮹70.3%ᮥᯕᬊ⦹ᩍݍᖒ⧁ᙹᯩᮭᮥ

᮹ၙ⦽݅. ᷪᱽŖ⦽ᕽእᜅᨱእ⧕᜚~᮹อ᳒ࠥaᔢݡᱢᮝಽ

(6)

Table 6. Efficiency of Privately Owned Taxies

DMU Technical

Efficiency

Pure Technical

Efficiency Scale Efficiency Return to Scale Reference

1 Suwon 0.785 0.937 0.838 IRS 6,22,29

2 Seongnam 0.939 0.948 0.990 IRS 10,12,22,23

3 Goyang 0.961 0.988 0.973 DRS 22,23

4 Bucheon 0.808 0.896 0.901 IRS 12,22

5 Yongin 0.944 0.947 0.997 IRS 22,23,30

6 Ansan 0.912 1.000 0.912 IRS 6

7 Anyang 0.844 0.942 0.897 IRS 6,22,29

8 Namyangju 0.904 0.942 0.960 IRS 10,12,22,23

9 Uijeongbu 0.703 0.911 0.771 IRS 10,12,22,23

10 Pyeongtaek 1.000 1.000 1.000 CRS 10

11 Siheung 0.887 0.977 0.908 IRS 6,22,23

12 Hwaseong 0.874 1.000 0.874 IRS 12

13 Gwangmyeong 0.808 0.919 0.879 IRS 6,22,29

14 Paju 0.868 0.896 0.969 IRS 10,12,22,23

15 Gunpo 0.875 0.940 0.931 IRS 6,22,29,30

16 Gwangju 0.945 0.980 0.964 IRS 6,23,29,30

17 Gimpo 0.869 0.946 0.918 IRS 6,22,29,30

18 Icheon 0.807 0.960 0.841 IRS 6,22,29,30

19 Guri 0.896 0.976 0.918 IRS 6,22,23

20 Yangju 0.880 0.919 0.957 IRS 6,10,22,23,30

21 Anseong 0.844 0.949 0.889 IRS 6,22,23

22 Pocheon 1.000 1.000 1.000 CRS 22

23 Osan 1.000 1.000 1.000 CRS 23

24 Hanam 0.959 0.986 0.973 DRS 22,23

25 Uiwang 0.967 0.989 0.979 IRS 22,23,30

26 Dongducheon 0.897 0.934 0.961 IRS 10,12,22,23

27 Gwacheon 0.968 0.973 0.996 IRS 22,23,30

28 Yangpyeong 1.000 1.000 1.000 CRS 28

29 Yeoju 0.841 1.000 0.841 IRS 29

30 Gapyeong 1.000 1.000 1.000 CRS 30

31 Yeoncheon 0.916 0.982 0.933 IRS 22,28

Mean 0.900 0.962 0.934

Min. 0.703 0.896 0.771

Max. 1.000 1.000 1.000

Std. Dev. 0.073 0.033 0.059

Note : CRS means Constant Return of Scale DRS means Decreasing Return of Scale IRS means Increasing Return of Scale

ԏᮭᮥ ᮹ၙ⦽݅.

Table 7 ᮡ 31} ࠥ᜽᮹ ⬉ᮉᖒs ⠪Ɂᮡ 0.900ᮝಽ ᧞ 0.1

ᷪ10% ᱶࠥ⬉ᮉᖒ}ᖁ᮹ᩍḡaᯩᮭᮥᅕᩍᵝ໑, ⠪Ɂsᮥ

ʑᵡᮝಽ ࠥ᜽ᄥಽ ᇥඹ⦽ đŝᯕ݅.

BCC ༉⩶ᮥᯕᬊ⦹ᩍʑᚁ⬉ᮉᖒᮥᙽᙹʑᚁ⬉ᮉᖒŝȽ༉⬉

ᮉᖒᮝಽᇥญ⧁ᙹᯩ݅. ᷪbࠥ᜽᮹እ⬉ᮉᖒᯕᙽᙹ⦽ʑᚁᱢ

(7)

Table 7. Classification of Cites by Technical Efficiency

DMU

Below Average Suwon, Bucheon, Anyang, Uijeongbu, Siheung, Hwaseong, Gwangmyeong , Paju, Gunpo, Gimpo, Icheon, Guri, Yangju, AnSeong, Dongducheon, Yeoju

Above Average Seongnam, Goyang, Yongin, Ansan, Namyangju, Gwangju, Hanam, Uiwang, Gwacheon, Yeoncheon Efficient Cities Pyeongtaek, Pocheon, Osan, Yangpyeong, Gapyeong

Table 8. Cities by Return of Scale

Return of Scale DMU

CRS Pyeongtaek, Pocheon, Osan, Yangpyeong , Gapyeong

DRS Goyang, Hanam

IRS Suwon, Seongnam, Bucheon, Yongin, Ansan, Anyang, Namyangju, Uijeongbu, Siheung, Hwaseong, Gwangmyeong, Paju, Gunpo, Gwangju, Gimpo, Icheon, Guri, Yangju, Anseong, Uiwang, Dongducheon, Gwacheon, Yeoju, Yeoncheon

᫵ᯙᨱ ᮹⦽ äᯙḡ Ƚ༉᫵ᯙᨱ ᮹⦽ äᯙḡ ᇥᕾ ⧁ ᙹ ᯩ݅.

ᙽᙹʑᚁ⬉ᮉᖒᮥᇥᕾ⦽đŝ⠪┾᜽, ⡍⃽᜽, ᪅ᔑ᜽, ᧲⠪Ǒ, a⠪Ǒ᫙ᨱᦩᔑ᜽, ⪵ᖒ᜽ၰᩍᵝǑᯕ⬉ᮉᖒᯕᯩ۵ࠥ᜽ಽ

Ƚ༉⬉ᮉᖒ ⠪Ɂᮡ 0.934ಽ ӹ┡ԍ݅. ᯕ۵ ʑᚁᱢ እ⬉ᮉᖒ᮹

ᬱᯙᯕᙽᙹʑᚁ⬉ᮉᖒᅕ݅۵Ƚ༉⬉ᮉᖒᨱᕽʑᯙ⦹Łᯩᮭᮥ

᦭ᙹᯩ݅. ᙽᙹʑᚁ⬉ᮉᖒᯕȽ༉⬉ᮉᖒᅕ݅ԏᮡ8}ࠥ᜽۵

እ⬉ᮉᖒ᮹ᬱᯙᯕȽ༉᫵ᯙᅕ݅۵እ܆ශᱢᯙᕽእᜅᱽŖᨱ᮹

⧕ၽᔾ⦹Łᯩᮭᮥᅕᩍᵡ݅. ᯕॅࠥ᜽۵⬉ᮉᖒᱽŁෝ᭥⦹ᩍ

Ƚ༉ෝ⪶ݡ⦹Ñӹ⇶ᗭ⦹ʑᅕ݅۵ᔢݡᱢᮝಽ┾᜽(ʑᔍ)᮹ᕽእ ᜅෝ⬉ᮉᱢᮝಽᱽŖ⦹۵ႊჶᮥ₟ᦥ᧝⧁äᯕ݅. ӹນḡ18}

ḡᯱℕ۵እ⬉ᮉᖒ᮹ᬱᯙᯕእ܆ශᱢᯙᕽእᜅᱽŖᅕ݅۵Ƚ༉

᫵ᯙᨱ ᮹⧕ ၽᔾࡹŁ ᯩᮭᮥ ᦭ ᙹ ᯩ݅.

3.1.2 ஜՋுۚࢫָࡦ৤ଳ

Table 6᮹aᰆ᫝἞ᨱᯩ۵“ჩ⪙”ᩕᮡbࠥ᜽ᨱᙽᕽݡಽ

1 ᇡ░31ʭḡᇡᩍ⦽ჩ⪙ᯕ໑ᯕჩ⪙۵“ᵡÑḲ݉”ᩕᨱᕽb

ࠥ᜽ᄥᵡÑḲ݉Ǎᖒᬱᮥḡ⋎⧁ভᔍᬊ⦹ᩡ݅. ᩩෝॅᨕ1ჩ

ᙹᬱ᜽᮹ᵡÑḲ݉ᮥᔕ⠕ᅕ໕6, 22, 29ಽӹ┡ӹᯩ۵ߑᯕ۵

6ჩ ᦩᔑ᜽, 22ჩ ⡍⃽᜽᪡ 29ჩ ᩍᵝǑᯕ ᙹᬱ᜽᮹ ⬉ᮉᖒᮥ

ĥᔑ⦹۵ߑ ₙ᳑ࡹᨩᮭᮥ ӹ┡ԙ݅.

DEAᨱᯩᨕᕽᵡÑḲ݉ᮡݡ݉⯩ᵲ᫵⦽᮹ၙෝḡܭ݅. ᪽Ա

⦹໕b᮹ᔍđᱶ݉᭥᮹⬉ᮉᖒŝእ⬉ᮉᖒ᮹ᱶࠥəญŁእ⬉

ᮉᱢᇡྙᯕᯕᵡÑḲ݉ᮥ☖⧕ᕽᔢݡᱢᮝಽ⊂ᱶࡹʑভྙᯕ݅.

ᵡÑḲ݉ᮡ እ⬉ᮉᱢᯙ ᳑Ḣᯕ ₙ᳑⧁ ᙹ ᯩ۵ ༉ߙᯕ ࡽ݅۵

ᱱᨱᕽ᮹᮹ෝw۵ߑ, əäᮡᵡÑḲ݉ᯕࡹ۵᮹ᔍđᱶ݉᭥a

⠪aݡᔢᯕࡹ۵᮹ᔍđᱶ݉᭥᪡⚍᯦ၰᔑ⇽Ǎ᳑ᨱᯩᨕᕽእƱ ᱢ ࠺ḩᖒᮥ ḡܭ Ḳ݉ॅಽ Ǎᖒࡹʑ ভྙᯕ݅.

⬉ᮉᖒsᯕ1.000ᮝಽĥᔑࡽ⡍⃽᜽, ᪅ᔑ᜽abb22, 15⫭

᮹ₙ᳑Ḳ݉⇽⩥ኩࠥෝӹ┡ԕᨩ݅. ⡍⃽᜽᮹ₙ᳑Ḳ݉⇽⩥ኩࠥ

22⫭۵⡍⃽᜽a݅ෙ22}ࠥ᜽᮹⬉ᮉᖒĥᔑŝᱶᨱₙ᳑Ḳ݉

Ǎᖒᬱᩎ⧁ᮥ⦹ᩡ݅۵᮹ၙᯕ݅. ₙ᳑⬀ᙹa׳݅Ł⧕ᕽaᰆ

⬉ᮉᖒᯕ׳ᮡʑšᯕ௝Ł⠪a⧁ᙹ۵ᨧḡอᵡÑḲ݉ᮡ⚍᯦᫵

ᗭၰᔑ⇽᫵ᗭ᮹᳑⧊ᨱᯩᨕᕽእ⬉ᮉᱢᯙʑšॅᯕᄅ⊹ษ┚᮹

ݡᔢᮝಽ ᔝᦥ᧝ ⧁ ʑšᯕ௝۵ ᱱᨱᕽ ᵲ᫵⦹݅.

Table 8 ᮹Ƚ༉ᙹᯖᮡᔾᔑపᯕᨕਜíᄡ⪵⦹۵aෝᖅ໦⦹ʑ

᭥⦽ }ֱᯕ݅. ༉ु ᔾᔑ᫵ᗭෝ ࠺᜽ᨱ ᷾a᜽┍ ভ ᔑ⇽పᯕ

ᯕᨱእಡ⦹ᩍ࠺ᯝ⦹í᷾a⦹۵ĞᬑෝȽ༉ᨱݡ⦽ᙹᯖᇩᄡ (CRS), ޵qᗭ⦹۵ĞᬑෝȽ༉ᨱݡ⦽ᙹᯖℕq(DRS), ޵᷾a⦹

۵ĞᬑෝȽ༉ᨱݡ⦽ᙹᯖℕ᷾(IRS)ᯕ௝Ł⦽݅. Ƚ༉ᙹᯖℕ᷾ᯙ

ĞᬑᨱȽ༉᮹Ğᱽa᳕ᰍ⦽݅Ł⦹Ł, Ƚ༉ᙹᯖℕqᯙĞᬑᨱ

Ƚ༉᮹ እĞᱽa ᳕ᰍ⦽݅Ł ⦽݅.

ᇥᕾđŝᨱ঑෕໕⠪aݡᔢ31}ࠥ᜽ᵲȽ༉ᙹᯖᇩᄡ(CRS) ᯙࠥ᜽a5}, Ƚ༉ᙹᯖℕq(DRS)ᯙࠥ᜽a2}, Ƚ༉ᙹᯖℕ᷾

(IRS)ᯙࠥ᜽a24}ಽӹ┡ԍ݅. Ƚ༉ᙹᯖᇩᄡᯙࠥ᜽۵aᰆ

⬉ᮉᱢᯙࠥ᜽ᯥᮥ᮹ၙ⦹໑, Ƚ༉ᙹᯖℕ᷾ᯙࠥ᜽ॅᮡ⨆⬥⚍᯦

పᮥ ᷾ݡ⧉ᮝಽ៉ ⬉ᮉᖒᮥ }ᖁ᜽┍ ᙹ ᯩ۵ a܆ᖒᮥ ᯩ۵

ࠥ᜽ॅᯕ݅.

3.2 ࣑଴೿ਏ 3.2.1 ตଘন

Table 9ŝz݅. ʑᚁ⬉ᮉᖒᇥᕾđŝᨱ঑෕໕Ğʑࠥԕ31}

ࠥ᜽ᵲᔢݡᱢᮝಽ⬉ᮉᱢᯙࠥ᜽۵4}, እ⬉ᮉᱢᯙࠥ᜽۵27}

ಽӹ┡ԍ݅. ⬉ᮉᱢᯙࠥ᜽۵⠪┾᜽, ⡍⃽᜽, ᪅ᔑ᜽, a⠪Ǒᯕ݅.

ᯕ۵ᯕॅ4}ࠥ᜽aᯝᱶ⦽ᙹᵡ᮹อ᳒ࠥෝᔾᔑ⧉ᨱᯩᨕᕽ

݅ෙ ࠥ᜽ᅕ݅ ᱢᮡ ᧲᮹ ᕽእᜅෝ ᔍᬊ⦹۵ ܆ಆᮥ ⪶ᅕ⦹Ł

ᯩᮭᮥ ᮹ၙ⦽݅.

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Table 9. Efficiency of Corporation Owned Taxis

DMU Technical

Efficiency

Pure Technical

Efficiency Scale Efficiency Return to Scale Reference

1 Suwon 0.815 0.921 0.884 IRS 6,22,29

2 Seongnam 0.959 0.965 0.994 IRS 10,12,22,23

3 Goyang 0.953 0.956 0.996 DRS 22,23

4 Bucheon 0.832 0.898 0.926 IRS 12,22

5 Yongin 0.973 0.974 0.999 DRS 22,23,30

6 Ansan 0.969 1.000 0.969 IRS 6

7 Anyang 0.891 0.929 0.959 IRS 6,22,29

8 Namyangju 0.946 0.966 0.980 IRS 10,12,22,23

9 Uijeongbu 0.799 0.910 0.878 IRS 10,12,22,23

10 Pyeongtaek 1.000 1.000 1.000 CRS 10

11 Siheung 0.909 0.979 0.928 IRS 6,22,23

12 Hwaseong 0.816 0.970 0.841 IRS 12

13 Gwangmyeong 0.803 0.919 0.874 IRS 6,22,29

14 Paju 0.911 0.915 0.996 IRS 10,12,22,23

15 Gunpo 0.916 0.943 0.971 IRS 6,22,29,30

16 Gwangju 0.964 0.970 0.993 IRS 6,23,29,30

17 Gimpo 0.871 0.933 0.934 IRS 6,22,29,30

18 Icheon 0.802 0.986 0.814 IRS 6,22,29,30

19 Guri 0.919 0.949 0.969 IRS 6,22,23

20 Yangju 0.962 0.967 0.995 IRS 6,10,22,23,30

21 Anseong 0.787 0.922 0.854 IRS 6,22,23

22 Pocheon 1.000 1.000 1.000 CRS 22

23 Osan 1.000 1.000 1.000 CRS 23

24 Hanam 0.995 1.000 0.995 DRS 22,23

25 Uiwang 0.838 0.900 0.931 IRS 22,23,30

26 Dongducheon 0.944 0.946 0.998 DRS 10,12,22,23

27 Gwacheon 0.945 0.951 0.993 IRS 22,23,30

28 Yangpyeong 0.790 1.000 0.790 IRS 28

29 Yeoju 0.755 0.975 0.775 IRS 29

30 Gapyeong 1.000 1.000 1.000 CRS 30

31 Yeoncheon 0.827 1.000 0.827 IRS 22,28

Mean 0.900 0.960 0.938

Min. 0.755 0.898 0.775

Max. 1.000 1.000 1.000

Std. Dev. 0.077 0.033 0.070

Note : CRS means Constant Return of Scale DRS means Decreasing Return of Scale IRS means Increasing Return of Scale

aᰆእ⬉ᮉᱢᯙࠥ᜽۵⬉ᮉᖒᯕ0.75ᯙᩍᵝǑᮝಽӹ┡ԍŁ, 31 }ࠥ᜽᮹⬉ᮉᖒ⠪Ɂᮡ0.900ᯕᨩ݅. }ᯙ┾᜽᪡ษ₍aḡಽ

᧞ 0.1 ᷪ 10% ᱶࠥ ⬉ᮉᖒ }ᖁ᮹ ᩍʑa ᯩᮭᮥ ᅕᩍᵡ݅.

⠪Ɂsᮥ ʑᵡᮝಽ ࠥ᜽ᄥಽ ᇥඹ⦽ đŝ۵ Table 10ŝ z݅.

BCC ༉⩶ᮥᯕᬊ⦹ᩍᙽᙹʑᚁ⬉ᮉᖒᮥᇥᕾ⦽đŝ⠪┾᜽,

⡍⃽᜽, ᪅ᔑ᜽, ᧲⠪Ǒ, a⠪Ǒ᫙ᨱᦩᔑ᜽, ⦹ԉ᜽ၰᩑ⃽Ǒᯕ

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Table 10. Classification of Cites by Technical Efficiency DMU

Below Average

Suwon, Bucheon, Anyang, Uijeongbu, Hwaseong, Gwangmyeong, Gimpo, Icheon, Uiwang, AnSeong, Yangpyeong, Yeoju, Yeoncheon Above Average

Seongnam, Goyang, Yongin, Ansan, Namyangju, Siheung, Paju, Gunpo, Gwangju, Hanam,

Dongducheon, Gwacheon, Guri, Yangju Efficient Cities Pyeongtaek, Pocheon, Osan, Gapyeong

Table 11. Cities by Return of Scale

Return of Scale DMU

CRS Pyeongtaek, Pocheon, Osan, Gapyeong DRS Goyang, Yongin, Hanam, Dongducheon

IRS

Suwon, Seongnam, Bucheon, Ansan, Anyang, Namyangju, Uijeongbu, Siheung, Hwaseong, Gwangmyeong, Paju, Gunpo, Gwangju, Gimpo,

Icheon, Guri, Yangju, Anseong, Uiwang, Gwacheon, Yangpyeong, Yeoju, Yeoncheon

Table 12. Technical Statistics

No. of data Mean Std. Dev. Min. Max.

Efficiency 62 0.900 0.075 0.703 1.000

DMU 62 1.5 0.504 1 2

Table 13. Mann-Whitney Test

No. of Data Average Rank Rank-Sum Privately

Owned Taxis 31 31.52 977.0

Corporation

Owned Taxis 31 31.48 976.0

Total 62

Table 14. Test Statistics

Statistics

Mann-Whitney U 480.0

Wilcoxon W 976.0

Z -0.007

Sig. Probability 0.994

᮹ ⠪Ɂᮡ 0.960ᯕŁ, Ƚ༉⬉ᮉᖒ᮹ ⠪Ɂᮡ 0.938ಽ ӹ┡ԍ݅.

ᯕ۵}ᯙ┾᜽᪡ษ₍aḡಽʑᚁᱢእ⬉ᮉᖒ᮹ᬱᯙᯕᙽᙹʑᚁ

⬉ᮉᖒᅕ݅۵Ƚ༉⬉ᮉᖒᨱᕽʑᯙ⦹Łᯩᮭᮥ᦭ᙹᯩ݅. ᙽᙹʑ ᚁ⬉ᮉᖒᯕȽ༉⬉ᮉᖒᅕ݅ԏᮡ15}ࠥ᜽۵እ⬉ᮉᖒ᮹ᬱᯙᯕ

Ƚ༉᫵ᯙᅕ݅۵እ܆ශᱢᯙᕽእᜅᱽŖᨱ᮹⧕ၽᔾ⦹Łᯩᮭᮥ

ᅕᩍᵡ݅. ᯕॅࠥ᜽۵⬉ᮉᖒᱽŁෝ᭥⦹ᩍȽ༉ෝ⪶ݡ⦹Ñӹ

⇶ᗭ⦹ʑᅕ݅۵ᔢݡᱢᮝಽ┾᜽(ʑᔍ)᮹ᕽእᜅෝ⬉ᮉᱢᮝಽ

ᱽŖ⦹۵ႊჶᮥ₟ᦥ᧝⧁äᯕ݅. ӹນḡ12}ࠥ᜽۵እ⬉ᮉᖒ᮹

ᬱᯙᯕእ܆ශᱢᯙᕽእᜅᱽŖᅕ݅۵Ƚ༉᫵ᯙᨱ᮹⧕ၽᔾࡹŁ

ᯩᮭᮥ ᦭ ᙹ ᯩ݅.

3.2.2 ஜՋுۚࢫָࡦ৤ଳ

Table 9 ᨱᕽₙ᳑Ḳ݉⇽⩥ኩࠥෝᅕ໕ᦩᔑ᜽a20⫭, ⡍⃽᜽

a18⫭, ᪅ᔑ᜽a10⫭᮹ኩࠥෝᅕᯕŁᯩ݅. ₙ᳑⬀ᙹa׳ᮡ

ᵡÑḲ݉ᮡ⚍᯦᫵ᗭၰ ᔑ⇽᫵ᗭ᮹᳑⧊ᨱᯩᨕᕽእ⬉ᮉᱢᯙ

ʑšॅᯕᄅ⊹ษ┚᮹ݡᔢᮝಽᔝᦥ᧝⧁ʑšᯕ௝۵ᱱᨱᕽᵲ᫵

⦹݅. Table 11᮹ ᇥᕾđŝᨱ ঑෕໕ ⠪a ݡᔢ 31} ࠥ᜽ ᵲ

Ƚ༉ᙹᯖᇩᄡ(CRS)ᯙࠥ᜽a4}, Ƚ༉ᙹᯖℕq(DRS)ᯙࠥ᜽a

4 }, Ƚ༉ᙹᯖℕ᷾(IRS)ᯙࠥ᜽a13}ಽӹ┡ԍ݅. Ƚ༉ᙹᯖℕq ᯙࠥ᜽۵Ƚ༉᮹እĞᱽa᳕ᰍ⦹۵ḡᩎᮝಽȽ༉ᨱݡ⦽á☁a

⦥᫵⦹໑Ƚ༉ᙹᯖℕ᷾ᯙࠥ᜽ॅᮡ⚍᯦పᮥ᷾ݡ⧉ᮝಽ៉⬉ᮉᖒ

ᮥ }ᖁ᜽┍ ᙹ ᯩ۵ a܆ᖒᮥ ᯩ۵ ࠥ᜽ॅᯕ݅.

3.3 ০଍෍Ցஹ

ᕽಽ݅ෙə൚ᨱᗮ⦹۵DMUॅ᮹⬉ᮉᖒeᨱ₉ᯕaᯩ۵

ḡෝ❭ᦦ⦹ʑ᭥⧕ᕽ۵ࢱə൚eᨱ☖ĥᱢá᷾ᯕ⦥᫵⦹݅.

ᯱഭ⡍௞ᇥᕾᮡᯕುᱢᮝಽ⬉ᮉᖒᯕᨕਅᇥ⡍ෝ঑෕۵ḡ༉෕

ʑ ভྙᨱ s᮹ ᇥ⡍ᨱ ࠦพᱢᯙ እ༉ᙹᱢ ☖ĥపᮥ ᯕᬊ⦹۵

äᯕၵ௭Ḣ⦹݅. እ༉ᙹᱢ☖ĥపᵲࢱə൚e᮹⬉ᮉᖒ₉ᯕa

ᮁ᮹⦽ḡᩍᇡෝá᷾⦹ʑ᭥⧕ᕽ۵Wilcoxon-Mann-Whitneyᨱ

᮹⧕}ၽࡽᙽ᭥⧊á᷾(Rank-sum Test)ᯕᮁ⬉⦽ႊჶᯕ݅. ᅙ

ᩑǍᨱᕽ۵ᙽ᭥⧊á᷾ᮥᯕᬊ⦹ᩍ}ᯙ┾᜽᪡ჶᯙ┾᜽eDMU

ॅ᮹☖ĥᱢ₉ᯕaᯩ۵ḡෝá᷾⦹ᩡ݅. Table 12ᮡʑᅙ☖ĥԕ ᬊᯕ݅. }ᯙ┾᜽᪡ჶᯙ┾᜽ෝᙽ᭥ෝๅĉᕽǍ⦽ᙽ᭥᮹⠪Ɂs

ᮡTable 13ŝzᮝ໑, }ᯙ┾᜽᮹⠪Ɂᙽ᭥۵31.52ᯕŁჶᯙ┾

᜽᮹⠪Ɂᙽ᭥۵31.48ಽӹ┡ԍ݅. Z ☖ĥపᨱݡ⦽ɝᔍᮁ᮹⪶ශ (᧲⊂áᱶ)ᯕ 0.994ಽ 0.05ᅕ݅ Ⓧအಽ }ᯙ┾᜽᪡ ჶᯙ┾᜽᮹

ᕽእᜅᵡᯕz݅۵ȡྕaᖅᮥ₥┾⦹íࡽ݅(Table 14). bࠥ᜽

ᨱᕽ}ᯙ┾᜽aᱽŖ⦹۵อ᳒ࠥ᪡ჶᯙ┾᜽aᱽŖ⦹۵อ᳒ࠥ

᮹ ₉ᯕ۵ ᨧ۵ äᮝಽ ❱݉⧁ ᙹ ᯩ݅.

4. đುၰ᜽ᔍᱱ

ḡᗮᱢᯙ┾᜽᫵ɩᯙᔢŝᱶʑᱢᯙƱᮂᨱእ⧕ᯕᬊᯱॅ᮹

ᕽእᜅℕqอ᳒ࠥ۵ᩍᱥ⯩ᱽᯱญÙᮭᮝಽԏᮡᕽእᜅอ᳒ࠥ,

┡Ʊ☖ᙹ݉᮹ၽݍ, ᨦĥᯱǍיಆᇡ॒᳒ᮝಽĞᰢಆᱡ⦹ၰ

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┾᜽ᔑᨦ᮹⋉ℕaḡᗮࡹŁᯩ݅. ┾᜽᪡šಉ⧕ᕽ۵᫵ɩ, ᱽࠥ, ᕽእᜅ}ᖁႊᦩ॒݅᧲⦽᜽bᨱᕽᩑǍaḥ⧪ࡹᨕ᪅Łᯩ݅.

ࠥᇩǍ⦹Ł┾᜽aᱽŖ⦹۵ᕽእᜅ᪡ᯕᬊ~᮹อ᳒ࠥෝŁಅ⦽

ḡᩎᄥ ⬉ᮉᖒᮥᇥᕾ⦽ ᩑǍa ᨧ݅۵ᱱᨱᕽ ᅙ ᩑǍaw۵

᮹᮹۵ Ⓧ݅.

31 } ࠥ᜽᮹ ʑᚁ⬉ᮉᖒ ⠪a đŝෝ ᔕ⠕ᅕ໕, }ᯙ┾᜽᮹

Ğᬑ ⬉ᮉᱢᯙ ࠥ᜽۵ ⠪┾᜽, ⡍⃽᜽, ᪅ᔑ᜽, ᧲⠪Ǒ, a⠪Ǒ

॒5}ᯕ݅. ᯕ۵ᯕॅ5}ࠥ᜽aᯝᱶ⦽ᙹᵡ᮹อ᳒ࠥෝᔾᔑ⧉ᨱ

ᯩᨕᕽ݅ෙࠥ᜽ᅕ݅ᱢᮡ᧲᮹ᕽእᜅෝᔍᬊ⦹۵܆ಆᮥ⪶ᅕ⦹

Ł ᯩᮭᮥ ᮹ၙ⦽݅. aᰆ እ⬉ᮉᱢᯙ ࠥ᜽۵ ⬉ᮉᖒᯕ 0.70ᯙ

᮹ᱶᇡ᜽ಽ ӹ┡ԍ݅. ჶᯙ┾᜽᮹ Ğᬑ ʑᚁ⬉ᮉᖒ ᇥᕾđŝᨱ

঑෕໕Ğʑࠥԕ31}ࠥ᜽ᵲᔢݡᱢᮝಽ⬉ᮉᱢᯙࠥ᜽۵4}, እ⬉ᮉᱢᯙ ࠥ᜽۵ 27}ಽ ӹ┡ԍ݅. ⬉ᮉᱢᯙ ࠥ᜽۵ ⠪┾᜽,

⡍⃽᜽, ᪅ᔑ᜽, a⠪Ǒᯕ݅. aᰆእ⬉ᮉᱢᯙࠥ᜽۵⬉ᮉᖒsᯕ

0.75ᯙᩍᵝǑᮝಽӹ┡ԍ݅, 31}ࠥ᜽᮹}ᯙ┾᜽᪡ჶᯙ┾᜽᮹

⬉ᮉᖒ⠪Ɂᮡ0.900(90%)ᯕᨩ݅. ᯕ۵᧞0.1 ᷪ10% ᱶࠥ⬉ᮉᖒ

}ᖁ᮹ ᩍʑa ᯩᮭᮥ ᅕᩍᵡ݅.

Ƚ༉ᙹᯖᮥᇥᕾđŝᨱ঑෕໕}ᯙ┾᜽۵⠪aݡᔢ31}ࠥ

᜽ᵲȽ༉ᙹᯖᇩᄡ(CRS)ᯙࠥ᜽a5}, Ƚ༉ᙹᯖℕq(DRS)ᯙ

ࠥ᜽a2}, Ƚ༉ᙹᯖℕ᷾(IRS)ᯙࠥ᜽a24}ಽӹ┡ԍ݅. ჶᯙ┾

᜽۵ Ƚ༉ᙹᯖᇩᄡ(CRS)ᯙ ࠥ᜽a 4}, Ƚ༉ᙹᯖℕq(DRS)ᯙ

ࠥ᜽a4}, Ƚ༉ᙹᯖℕ᷾(IRS)ᯙࠥ᜽a13}ಽӹ┡ԍ݅. Ƚ༉ᙹ ᯖᇩᄡᯙࠥ᜽۵aᰆ⬉ᮉᱢᯙࠥ᜽ᯥᮥ᮹ၙ⦹໑, Ƚ༉ᙹᯖℕq ᯙࠥ᜽۵Ƚ༉᮹እĞᱽa᳕ᰍ⦹۵ḡᩎᮝಽȽ༉ᨱݡ⦽á☁a

⦥᫵⦹໑Ƚ༉ᙹᯖℕ᷾ᯙࠥ᜽ॅᮡ⚍᯦పᮥ᷾ݡ⧉ᮝಽ៉⬉ᮉᖒ

ᮥ }ᖁ᜽┍ ᙹ ᯩ۵ a܆ᖒᮥ ᯩ۵ ࠥ᜽ॅಽ ӹ┡ԍ݅.

bࠥ᜽ᨱᕽ}ᯙ┾᜽aᱽŖ⦹۵อ᳒ࠥaჶᯙ┾᜽aᱽŖ⦹

۵อ᳒ࠥᨱእ⧕׳ᮥäᮝಽᔾbࡹᨕࢱə൚e₉ᯕෝᙽ᭥⧊

á᷾(Rank-sum Test)ᮥ᜽⧪⦽đŝ}ᯙ┾᜽᪡ჶᯙ┾᜽e᮹

ᕽእᜅᱽŖᨱ঑ෙอ᳒ࠥ₉ᯕ۵ᨧ۵äᮝಽᇥᕾࡹᨩ݅. ┾᜽᮹

ᕽእᜅ۵݅᧲⦽᫵ᗭᨱ᮹⧕⊂ᱶࢁᙹᯩ۵ߑ, ᅙᩑǍᨱᕽ۵

4 aḡ ⚍᯦ᄡᙹอᮥ ʑᵡᮝಽ ⦹ᩍ ⬉ᮉᖒᮥ ⊂ᱶ⦹ᩡ݅. ⨆⬥

݅᧲⦽ ᫵ᗭෝ Łಅ⦽ ⬉ᮉᖒ ⠪aa ᫵Ǎࡽ݅.

References

Kang, S. U. (2009). “Road transport : Taxi Problems and Solutions.”

Urban Problems, pp. 64-67 (in Korean).

Kang, S. U. (2009). “Road transport : Study on Actual Taxi Total Quantity of each Region.” Urban Problems, pp. 59-61 (in

Korean).

Kim, K. H. and Sun, J. W. (1998). “Optimal Taxi Quantity in Small and Medium Size Cities.” Journal of Korean Society of Transpor- tation, Vol. 16, No. 4, pp. 7-20 (in Korean).

Kim, R. G., Kim, C. M. and Goo, J. E. (2007). “Characteristics on corporation owned taxis through the tachometer in cities and counties of gyeonggido.” Proc. of 56th Conference, Korean Society of Transportation, pp. 459-468 (in Korean).

Min, S. G. (2003). “Efficiency and economy of scale for taxi industry.” Regional Development Research, Vol. 35, No. 1, pp.

55-84 (in Korean).

Gyeonggi Research Institute (2010). Assesment of Taxi Management and Service, Gyeonggido (in Korean).

Song, J. R. and Lee, S. H. (2009). Simplifing Taxi Fare System, Gyeonggi Research Institute (in Korean).

Yoon, J. B. (2009). “Improvement of management and service of corporation owned taxi.” Transport Technology and Policy, Vol.

6, No. 3, pp. 179-198 (in Korean).

Lee, Y. H., Kim, E. J. and Kim, D. H. (2004). “Study on efficiency of airport operation by DEA in Asia.” Journal of Korean Society of Transportation, Vol. 22, No. 4, pp. 7-18 (in Korean).

Lee, S. J., Kim, J. H. and Choi, I. J. (2001). “Study on Taxi Revenue in Seoul.” Journal of Korean Society of Transportation, Vol. 19, No. 6, pp. 241-251 (in Korean).

Oh, M. Y., Kim, S. S. and Kim, M. J. (2002). “Analyzing efficiency in the Seoul’s urban bus industry using data envelopment analysis.” Journal of Korean Society of Transportation, Vol. 20, No. 2, pp. 59-68 (in Korean).

Jang, T. Y. (2003). “Modeling on taxi accidents through overdispersion test.” Journal of Korean Society of Civil Engineers, Vol. 23, No.

1-D, pp. 27-34 (in Korean).

Han, J. S., Kim, H. R. and Gho, S. Y. (2010). “Study on fairness of local bus line through DEA(Data Envelopment Analysis).”

Journal of Korean Society of Transportation, Vol. 29, No. 2, pp.

15-24 (in Korean).

Han, J. S., Kim, H. R. and Gho, S. Y. (2009). “A DEA (Data Envelopment Analysis) Approach for evaluating the efficiency of exclusive bus routes.” Journal of Korean Society of Transportation, Vol. 27, No. 6, pp. 45-53 (in Korean).

Hong, S. J. (2004). “Study of the efficiency of airlines’ and cargo divisions-using a DEA model approach.” Journal of Korean Society of Transportation, Vol. 22, No. 3, pp. 17-26 (in Korean).

Hong, C. E. (1998). “Study on taxi company management and traffic accident reduction in Incheon.” Proc. of 34th Fall Conference, Journal of Korean Society of Transportation, pp.

Relative Efficiency of Taxi Services by Data Envelopment Analysis among Cities and Counties in Gyeonggi Province

Ǥ ǤǤ

׌۩ผ ȵୋ೾઴ ȵ৉୍߾

Kim, Dae Hoon*, Jang, Tae Youn**, Song, Je Ryong***

Relative Efficiency of Taxi Services by Data Envelopment Analysis among Cities and Counties in Gyeonggi Province

ABSTRACT

This study analyzes the relative efficiences through satisfaction on taxi services of thirty-one cities and counties in Gyeonggi Province.

The DEA (Data Envelopment Analysis) is used to measure efficiencies on the privately owned taxies and the corporate taxies respectively. Efficiency in the DEA is measured relative to the highest observed performance rather than against some average.

Key words : Data Envelopment Analysis (DEA), Taxi Services, Relative Efficiency, Gyeonggi-Do

Ⅹಾ

ᅙᩑǍ۵Ğʑࠥԕ31}᜽Ǒᨱᕽᬕ⧪ࡹŁᯩ۵┾᜽᮹ᕽእᜅᨱݡ⦽อ᳒ࠥෝʑⅩಽḡᩎᄥ┾᜽᮹⬉ᮉᖒᮥ⠪a⦹ᩡ݅. ⬉ᮉᖒ⠪a۵

ᯱℕᯕ݅. ᯕॅḡᯱℕॅᮡᯝᱶ⦽ᙹᵡ᮹อ᳒ࠥෝᔾᔑ⧉ᨱᯩᨕᕽ݅ෙḡᯱℕᅕ݅ᱢᮡ᧲᮹ᕽእᜅෝᔍᬊ⦹۵܆ಆᮥ⪶ᅕ⦹Łᯩᮭᮥ᮹

ၙ⦽݅. }ᯙ┾᜽᪡ჶᯙ┾᜽eDMUॅ᮹☖ĥᱢ₉ᯕaᯩ۵ḡෝá᷾⦹ʑ᭥⦹ᩍᙽ᭥⧊á᷾(Rank- sum Test)ᮥ᜽⧪⦽đŝࢱḲ݉e ᨱᕽእᜅᱽŖᨱ঑ෙอ᳒ࠥ₉ᯕ۵ᨧ۵äᮝಽᇥᕾࡹᨩ݅.

áᔪᨕ ᯱഭ⡍௞ᇥᕾ(DEA), ┾᜽ᕽእᜅ, ᔢݡᱢ⬉ᮉᖒ, Ğʑࠥ

1. ᕽು

1.1 ઴֜ଭࢼլࢫࡧୡ

ྙᱥᬕ⧪ ᕽእᜅ(door to door)ෝ ᱽŖ⦹۵ Łɪᕽእᜅ᮹ ʑ܆ᮥ aḥ ┾᜽a ᯕᬊᯱॅ᮹ ݅᧲⦽ ᫶Ǎෝ ∊᳒᜽┅ḡ ༜⦽ ₥ ᵡ

ݡᵲƱ☖ᙹ݉ᮝಽ ᱥ௞ࡹŁᯩ݅. 2011֥ʑᵡᮝಽ Ğʑࠥ۵ 35,778ݡa ᬕ⧪ࡹŁᯩ۵ ၹ໕, ᕽᬙ᜽۵2001֥ᇡ░ ┾᜽ᱢᱶȽ༉

7,000 ݡෝʑᵡᮝಽ┾᜽ⅾపᱽෝ᜽⧪⦹Łᯩᮝ໑ᯕ⬥┾᜽

Ŗɪᯕᯕ൉ᨕḡḡᦫŁ72,355ݡᙹᵡ᮹┾᜽aᬕ⧪ࡹŁᯩ݅

(Table 1).

2011֥8ᬵĞʑࠥᨱᕽ᜽⧪⦽“ĞʑࠥၝƱ☖ᯕᬊ⧪┽”᳑ᔍ ᨱᕽࠥԕݡᵲƱ☖ᙹ݉ᄥᯕᬊอ᳒ࠥ۵ᙹࠥǭᱥ℁ᯕ48.7%

ಽ aᰆ׳ᦹŁŲᩎქᜅ(47.0%), eᖁქᜅ(45.2%), ษᮥქᜅ (41.7%), ᯝၹ℁ࠥ(39.7%), ┾᜽(35.9%) ᙽᯕᨩ݅. ᯱaᬊอ᳒

ࠥ۵62.0%ಽݡᵲƱ☖ᅕ݅׳ᦹ݅. ┾᜽᮹ᯕᬊอ᳒ࠥaԏᮡ

ᯕᮁ۵ ݅᧲⪵ ࡹḡ ༜⦽ ᕽእᜅ ၰ ᕽእᜅ ḩ ᱡ⦹ᨱ ʑᯙ⦽

äᮝಽᯕ۵┾᜽ᯕᬊශqᗭಽᯕᨕᲙ┾᜽ᬕᙹšಉ᳦ᔍᯱॅ᮹

ᦩᱶᱢᯙᙹ᯦ၰĞᩢᨱᩢ⨆ᮥᵝŁᯩ۵ᝅᱶᯕ݅(ᘂᱽൂ, 2010).

┾᜽ྙᱽ⧕đᮥ᭥⧕ᕽ۵ ┾᜽ᙹ᫵ݡእ┾᜽Ŗɪ᮹ᇩɁ⩶᮹

ྙᱽ, ┾᜽ᯕᬊᯱ⊂໕ᨱᕽ᮹⠙ญ⦹Łᦩᱥ⦽ᕽእᜅᨱݡ⦽᫶Ǎ,

┾᜽ᨦℕ᮹ Ğᩢ}ᖁ ॒ᯕ ⦥᫵⦹݅.

┾᜽(ʑᔍ)aᱽŖ⦽ᕽእᜅ۵ʑᔍ⊽ᱩࠥ, ₉పᔢ┽, ┾᜽ᬕ⧪,

┾᜽᫵ɩ ॒ 4} ⧎༊ᮝಽ Ǎᖒࡽ݅. ʑᔍ⊽ᱩࠥ۵ ᬕᱥʑᔍ᮹

ᯕ໑, ₉పᔢ┽۵ ₉పℎđࠥ, ₉ప Ԫ/ӽႊ ᱢᱶᖒ, ᜚₉q ၰ

₉పԕᇡᔢ┽॒ᯕ݅. ┾᜽ᬕ⧪ᕽእᜅ۵ᬕ⧪ᵲ᜚₉q, ჶȽᵡᙹ, ᦩᱥᬕ⧪, ᬕ⧪┽ࠥ ॒ᯕ໑, ┾᜽᫵ɩᮡ ᇡݚ᫵ɩ ᫵Ǎ, ᱶ⪶⦽

Ñᜅ෥ࠩ, ᩢᙹ᷾ၽ⧪॒ᯕ݅. ᯕ్⦽ᕽእᜅ۵ᯕᬊ~᮹อ᳒ࠥᨱ

ⓑ ᩢ⨆ᮥ ၙ⊽݅.

(ʑᔍ)᮹ᕽእᜅᨱݡ⦽ᯕᬊᯱa۱ӝ۵⬉ᮉᖒ(อ᳒ࠥ)ෝ᳑ᔍ⦹

Ł, ࠥ᜽e ᕽእᜅ ⬉ᮉᖒ(อ᳒ࠥ)ᨱ ᯩᨕ ₉ᯕa ᯩ۵ḡ እƱ

ᕽĞʑࠥԕbࠥ᜽᮹⬉ᮉᖒᮥᝅ᷾ᱢᮝಽ⊂ᱶ⧕ᅕŁእ⬉ᮉᱢ ᯙࠥ᜽᮹⬉ᮉᖒᮥᱽŁ᜽┍ᙹᯩ۵⩥ᝅᱢᯙႊᦩᮥࠥ⇽⦹Łᯱ

⦽݅. ⬉ᮉᖒ⠪a۵}ᯙ┾᜽᪡ჶᯙ┾᜽ಽbbǍᇥ⦹ᩍᇥᕾ⦹

ᩡ݅.

1.2 টෘ઴֜Ցഠ

Table 2 ᨱᕽ۵ ┾᜽šಉᱶ₦ᨱ ݡ⦽ ᅕŁᕽ᪡ᅙ ᩑǍᨱᕽ

⪽ᬊ⦹íࢁᇥᕾ༉⩶ᯙDEAෝ⪽ᬊ⦽ʑ᳕ᖁ⧪ᩑǍෝᅕᩍᵝŁ

ᯩ۵ߑ, ✚⯩┾᜽šಉᩑǍ۵ݡᇡᇥࠥ᜽ŝᱽ₉ᬱᨱᕽᙹ⧪⦽

äᯕݡᇡᇥᯕ໑, ༉⩶Ǎ⇶ᯕӹᇥᕾႊჶುᮥ☖⦽⦺ᚁᱢ⊂໕ᨱ ᕽ᮹ ᩑǍ۵ ၙၙ⦽ äᮝಽ ӹ┡ԍ݅.

DEAෝᯕᬊ⦽⦺ᚁᱢ⊂໕ᨱᕽ᮹⬉ᮉᖒᇥᕾᮝಽ۵᪅ၙᩢ

॒(2002)ᯕݡᵲƱ☖ᙹ݉᮹݅ෙ⦽⇶ᯙᕽᬙ᜽᜽ԕქᜅᬕᘂᨦᨱ

ݡ⦽⬉ᮉᖒᮥᇥᕾ⦹ᩡᮝ໑, ⦽ḥᕾ॒(2009, 2010)ᮡᕽᬙ᜽

eᖁქᜅיᖁၰḡᖁქᜅיᖁ᮹⩶⠪ᖒᮥᇥᕾ⦹ᩡ݅. Ŗ⧎ᇥ᧝

ᨱᕽ۵ ᯕᩢ⩢ ॒(2004)ᯕ ᦥ᜽ᦥ Ŗ⧎ ᬕᩢ ⬉ᮉᖒᮥ ⪮ᕾḥ (2004) ᯕ⧎Ŗ⪵ྜྷᇡྙŝ⧎Ŗᔍ⬉ᮉᖒᨱš⦽ᩑǍෝᙹ⧪⦹ᩡ

┅ʑ᭥⦽äᯕအಽ⬉ᮉᖒ⨆ᔢᮥ᭥⧕ྕᨨᮥ⧕᧝⦹۵ḡႊ⨆ᮥ

݅᧲⦽ᱶ₦ᯕḥ⧪ࡹᨩᮭᨱࠥᇩǍ⦹Ł┾᜽aᱽŖ⦹۵ᕽእᜅ

ᱥྕ⦽ ᔢ┽ᯕ݅.

2. ᇥᕾႊჶၰᯱഭ

2.1 DEA ंজ

⦹ʑ ᭥⧕ᕽ aᰆ ձญ ⪽ᬊࡹŁ ᯩ۵ DEAෝ ᅙ ᩑǍᨱᕽࠥ

ᯕᬊ⦹ᩡ݅. DEA۵⚍᯦ŝᔑ⇽šĥa໦⪶⦹íᱶ᮹ࡹḡᦫᮡ

᮹ᔍđᱶ݉᭥᮹ᔢݡᱢ⬉ᮉᖒᮥ ⠪a⦹۵äᮝಽๅᬑ݅᧲⦽

ᇥ᧝ᨱᕽᔍᬊࡹŁᯩ݅. ᅙᩑǍᨱᕽDEAෝ⪽ᬊ⦽ᯕᮁ۵DEA ۵ ᔢݡᱢ ⬉ᮉᖒ ⊂ᱶႊჶᯕʑ ভྙᨱ ⬉ᮉᖒ ᱱᙹ ᯕ᫙ᨱࠥ

݅ෙႊჶುᨱᕽᱽ᜽⦹ḡ༜⦹۵2aḡᮁᬊ⦽ᱶᅕෝᱽ᜽⦽݅.

ෝᱽ᜽⧉ᮝಽᕽᄅ⊹ษ┚᮹ɝÑෝᱽŖ⦽݅۵äᯕŁ, ᕽእᜅ(⚍

᯦)ŝอ᳒ࠥ(ᔑ⇽)᮹᫵ᗭᄥಽእ⬉ᮉ᮹ᱶࠥᨱš⦽ᱶᅕෝᱽŖ

⦽݅۵ äᯕ݅.

DEA ᯕುᨱݡ⦽ᖅ໦ᮥCCR༉⩶ᮝಽᇡ░᜽᯲⦹۵ߑձญ

⪽ᬊࡽ༉⩶ᯕ௝۵ᱱŝ݅᧲⦽⧕ᕾᯕa܆⦹݅۵ᱱভྙᯕ݅.

CCR ༉⩶ᮡ⢽⩥ࡹ۵⩶┽ᨱ঑௝እᮉ༉⩶(Ratio Model), ᜚ᙹ༉

⩶(Multiplier Model), əญŁ ⡍௞༉⩶(Envelopment Model)

॒ᮝಽ Ǎᄥࡹ۵ߑ, ⬉ᮉᖒ ⠪aෝ ᭥⧕ ᮁᔍ⦽ ༊ᱢᮥ ᭥⦹ᩍ

᳑Ḣࡽ᮹ᔍđᱶ݉᭥(Decision Making Units : DMU)e᮹ᔢݡ ᱢ ⬉ᮉᖒᮥ ⠪a⦹۵ ᯝ᳦᮹ ᖁ⩶ĥ⫮ʑჶᯕ݅. ᅙ ᩑǍᨱᕽ

௡b⚍᯦ၰᔑ⇽᫵ᗭᨱݡ⦽bb᮹aᵲ⊹⧊ᨱݡ⦽እᮉᯕ݅.

DMU᮹ ⬉ᮉᖒ = ᔑ⇽ / ⚍᯦

=

Ɠ

Ɨ

/

Ɣ

Ɩ

(1)

Ɖ = 1, 2, ⲷ,s (DMU᮹ ᙹ)

ǤǤǤ

׌۩ผȵୋ೾઴ȵ৉୍߾

Kim, Dae Hoon, Jang, Tae Youn, Song, Je Ryong

áᔪᨕ ᯱഭ⡍௞ᇥᕾ(DEA), ┾᜽ᕽእᜅ, ᔢݡᱢ⬉ᮉᖒ, Ğʑࠥ

^Ɠ

^Ɨ

^/

^Ɣ

^Ɩ

⁽¹⁾

Ɖ ^{= 1, 2,} ⲷ,s (DMU᮹ ᙹ)

, _Ɣ

0 ŝ1ᔍᯕ᮹ჵ᭥ෝ≉⦹íࡽ݅. ᯥ᮹⠪aݡᔢ ¦®

max _Ɔ

^Ɠ

^Ɨ

^/

^Ɣ

^Ɩ

^Ɠ

^Ɨ

^/ ā

^Ɣ

^Ɩ

^ⴌ1, ^Ɖ ^=1, ^ⲷ,s

_Ɠ

, _Ɣ

ⴍ Ļ >0, _Ɛ =1,ⲷ,n, Ƈ =1,ⲷ,m

᜾(2)᮹ℌჩṙᱽ᧞᳑Õᮡ ¦®

^Ɠ

^Ɨ

^Ɣ

^Ɩ

^{= 1}

^Ɠ

^Ɨ

^- ā

^Ɣ

^Ɩ

^ⴌ0, ^Ɖ ^=1, ^ⲷ,s

_Ɠ

, _Ɣ

ⴍ Ļ >0, _Ɛ =1, ⲷ,n, Ƈ =1, ⲷ,m

max _Ɔ