* ǎ☁ᩑǍᬱ ǎ☁ᯙ⥥ᩑǍᅙᇡ ࠥಽᱶ₦ᩑǍᖝ░ ([email protected])
Received May 25, 2012/ revised January 26, 2013/ accepted April 9, 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)
ǣǣȀȀǤǤȀͳͲǤͳʹͷʹȀ ǤʹͲͳ͵Ǥ͵͵ǤͷǤͳͻͺͻ Ǥ ǤǤ
㳲Ὢ⡢㢯#P-Median ⬊ኞὪ⽖ⴂ#ⴲⱧ㬚#ⵎⷂሮ⺺ⵣ#㌚ⵃ⽾⛞ⷓ
ࢮ࣪ޭ ȵଲָ ȵౖ׆ச
Park, Bora*, Lee, Kyu Jin**, Choi, Keechoo***
Optimum Location Choice for Bike Parking Lots Using Heuristic P-Median Algorithm
ABSTRACT
As the importance of ‘bike revitalization’ has been emphasized in our society, many cities around the world put enormous efforts to create a bike-oriented transportation system. None the less, the results were not much productive and effective. In this study, to decide the location and number of the bike-parking facilities, the heuristic P-median algorithm has been applied with and without budget constraints. A test network with 30 candidate locations (centroids) were employed. The results show that the optimum number of bike parking lots with and without the budget limits are 9 and 20, respectively. Since the optimum locations determined in this study were congruous with the actual bike parking lots with high utilization rates, it is expected that the proposed methods can be applied for determining the optimum locations of the bike parking facilities elsewhere. Some limitations and future research agenda have also been discussed.
Key words : Bike parking lot, Selecting locations, P-median algorithm
Ⅹಾ
↽ɝ‘ᯱᱥÑᯕᬊ⪽ᖒ⪵’aᕽᕽ⯩ᔍ⫭ᱢᮝಽᵲᖒᯕᇡbࡹ໕ᕽᯱᱥÑᵲᝍ᮹Ʊ☖ℕĥಽ᮹ᱥ⪹ᮥ᭥⦽݅᧲⦽יಆᯕᯩᨕ᪵ᮝӹə
⬉ŝ۵ᇡḥ⦽໕ᯕᯩ݅. ᅙᩑǍᨱᕽ۵ᯱᱥÑᵝ₉ᰆ॒᮹ᖅᮥǍ⇶⦹۵ߑᯩᨕᕽ⦥⦽ᵝ₉ᰆ᯦ḡᖁᱶᮥŖ⦺ᱢᇥᕾᮥ☖⧕ᕽǍ⩥⦹
ᩍᅕᯱ۵ߑᯩ݅. ᷪ, ᯱᱥÑᵝ₉ᰆ᮹↽ᱢ}ᙹၰ᭥⊹ෝ۵ႊჶᮥᱽᦩ⦹۵äᯕ༊ᱢᯙၵ, Ǎ⇶༉⩶ᮡHeuristic P-Median Łญ᷹
ᮥᯕᬊ⦹ᩡᮝ໑, ᩩᔑᱽ᧞᮹ᮁྕᨱ༉⩶ᮥbbᱽ⦹ᩡ݅. ༉⩶᮹ᱢᬊᮥ᭥⦹ᩍ⨹օ✙ᬭⓍෝǍ⇶⦹Ł30}᮹ᩩእᵝ₉ᰆ(ᖝ✙
ಽᯕऽ-ᙹḡ)ᮥǍᖒ⦹ᩡ݅. ᇥᕾđŝᯱᱥÑᵝ₉ᰆ᮹᯦ḡᱱᮡᩩᔑᱽ᧞ᯕᯩ۵Ğᬑ9}, ᩩᔑᱽ᧞ᯕᨧ۵Ğᬑ20}ಽᖁᱶࡹᨩᮝ໑, ᖁ ᱶࡽ᯦ḡᱱॅᮡᝅᱽᯱᱥÑᵝ₉ᰆ᮹ᯕᬊශᯕ׳ᮡŔŝᯝ⊹⦹۵äᮝಽӹ┡ԍ݅. ᯕ్⦽Łญ᷹ᮡᝅᱽŖᬊᯱᱥÑ॒᮹ݡᔢḡ, ੱ۵
ᝁȽᩩᔢ᯦ḡᖁᱶᨱ⪽ᬊࡹᨕḩᙹᯩᮥäᮝಽ❱݉ࡽ݅. ᧞e᮹⦽ĥ᪡⨆⬥ᩑǍŝᱽᨱݡ⧕ᕽࠥם⦹ᩡ݅.
áᔪᨕ ᯱᱥÑᵝ₉ᰆ, ᯦ḡᖁᱶ, P-median Łญ᷹
1. ᕽು
1.1 ֜ࢼլࢫࡧୡ
ᬑญӹ۵ᬊ₉ᵲᝍ᮹ᙹᘂℕĥಽᯙ⧕ʑࡽƱ☖⪝ᰂྙᱽ᪡⪹Ğྙᱽaᯕၙ᪅௹ᱥᇡ░ྙᱽࡹᨕ᪵ᮝ໑, ᯕෝ⧕đ⦹ʑ
İࣀėॡ
Fig. 1. Research Procedure
᭥⦽ᩍ్ႊᦩॅᵲ↽ɝ‘ᯱᱥÑᯕᬊ⪽ᖒ⪵’᮹ᵲᖒᯕⓍí
ᇡbࡹᨩ݅.
ᯱᱥÑᯕᬊ⪽ᖒ⪵ᱶ₦ॅᮥ⧪⧉ᨱᯩᨕℕĥᱢᯕŁƱ☖Ŗ
⦺ᱢᯙᇥᕾᯕᙹၹࡹᨕ⧉ᮡ⦥ᙹᱢᯕḡอ, ə࠺ᦩᯱᱥÑࠥಽ,
ᯱᱥÑᵝ₉ᰆ॒᮹ᯱᱥÑᯙ⥥ᖅᮥǍ⇶⦹۵ߑᯩᨕŖ⦺ᱢ
ᇥᕾᯕᙹၹࡹḡᦫᦹäᯕᔍᝅᯕ݅. ᯕ۵↽ɝ֥eᯱᱥÑ
ᯙ⥥۵᷾a⦹ᩡᮭᨱࠥ᪅⯩ಅᯱᱥÑᙹ݉ᇥݕශᮡqᗭ⦹ᩡᮝ ໑, ༜ᖅ⊹ࡽᯱᱥÑʑၹᖅॅಽᯙ⦹ᩍၽᔾ⦹۵ྙᱽᱱॅᯕ
᯦᷾⧕ᵝŁᯩ݅. ḡญᱢᮝಽᇡᱢᱩ⦽Ŕᨱᖅ⊹ࡽᯱᱥÑᵝ₉ᰆ
ᮡᯱᩑᜅ౩ᯕᬊශᯕๅᬑԏᦥḡíࡹ۵ߑ, ᯕ్⦽ᯱᱥÑᵝ₉ᰆ
ॅᮡ ࠥၙšᮥ ⭝ᗱ┍ ᐱอᦥܩ ᩩᔑ Ԏእ᮹ݡ⢽ᱢᯙ
ᔍಡಽ ᗱΞ⯭݅.
ʑᅙᱢᮝಽᯱᱥÑᵝ₉ᰆᮡᯕᬊᯱ⊂໕ᨱᕽᱲɝᯕᛞŁᙹ
ෝ⬉ŝᱢᮝಽ⡍⧉⧁ᙹᯩ۵ḡᱱᨱ᭥⊹ࡹᨕ⦽݅. ᩍ్ŖŖ
ᖅྜྷ᮹᯦ḡᖁᱶྙᱽᨱݡ⦽ᩑǍaᙹ⧪ࡹŁᯩᮝӹᯱᱥÑᵝ₉ ᰆ᮹᭥⊹ᖁᱶᨱݡ⦽ᩑǍ۵ၙእ⦽ᝅᱶᯕ݅. ᯕᨱᅙᩑǍᨱᕽ۵
ᯱᱥÑᵝ₉ᰆ᮹᭥⊹ᖁᱶ ᅕ݅Ŗ⦺ᱢᮝಽᱲɝ⦹ʑ᭥⦹ᩍ
Heuristic P-Median ༉⩶ᮥ ᱢᬊ⦹ᩍ ᯱᱥÑ ᵝ₉ᰆ᮹ ᭥⊹ෝ
đᱶ⧁ᙹᯩ۵ႊჶುᮥᱽ⦹Ł, ᯕෝ⪽ᬊ⦽ᯱᱥÑᵝ₉ᰆ᮹
⬉ᮉᱢᯙŖɪᮥ☖⧕ᯱᱥÑᯕᬊ⪽ᖒ⪵aᝅḩᱢᮝಽᯕᨕ
ḩ ᙹ ᯩ۵ ☁ݡෝ ษಉ⦹۵ äᯕ ᅙ ᩑǍ᮹ ༊ᱢᯕ݅.
1.2 ֜ࢺ࣑ࢫୣఙ
ᅙᩑǍ۵ə࠺ᦩŖ⦺ᱢᇥᕾʑၹᨧᯕᯱᱥÑᵝ₉ᰆᖅ⊹
᭥⊹ෝđᱶ⧕᪵äᨱݡ⦽ྙᱽᱱᮥḡᱢ⦹Ł, ᯕ్⦽ྙᱽෝ
⧕đ⦹ʑ᭥⦹ᩍ᯦ḡᖁᱶྙᱽ᪡šಉࡽǎԕᩑǍၰᯕುॅᮥ
á☁⦽݅.
ᅙᩑǍᨱᕽᔍᬊ⦽ᯱᱥÑᵝ₉ᰆ᮹᭥⊹ᖁᱶႊჶᮡP-Median Algorithmŝ⡍⧉ྙᱽaɝeᮥᯕŁᯩᨕᯕᨱݡ⦽ᱢᬊ᮹
ɝÑၰႊჶᮥʑᚁ⦽݅. ᯱᱥÑᵝ₉ᰆ᮹᭥⊹ᖁᱶႊჶᮡຝᱡ
ᱢᱶ⦽ⅾᖅ⊹}ᙹෝŁ, ᦥԙᱢᱶᖅ⊹}ᙹᨱݡ⦹ᩍᯱᱥÑ
ᵝ₉ᰆ᮹ᱢᱶ᭥⊹ෝᖁᱶ⦹۵݉ĥಽᙹ⧪ࡹ໑ᯕᨱݡ⦽༉⩶ᮥ
ᱽ⦹ࠥಾ ⦽݅.
ᱽ⦽༉⩶᮹ᱢᬊᮥ᭥⦹ᩍe݉⦽⨹օ✙ᬭⓍෝǍ⇶⦹Ł
ᯱᱥÑ ᵝ₉ᰆ ᱢᱶ ᭥⊹ෝ ᖁᱶ⦹۵ ᇥᕾᮥ ᙹ⧪⦹ࠥಾ ⦽݅.
2. ʑ᳕ᩑǍŁₑ
2.1 ଲߨୡճఝ
᯦ḡᖁᱶྙᱽ۵ᯝၹᱢᮝಽ༊ᱢ⧉ᙹ᪡ᱽ᧞᮹݅᧲⦽⩶┽
ᨱᱽ⦽ᬊపᯕᨧ۵᯦ḡᖁᱶྙᱽ(Uncapacitated Facility Location Problem : UFLP), ᱽ⦽ᬊపᯕ ᯩ۵ ᯦ḡᖁᱶྙᱽ (Capacitated Facility Location Problem : CFLP), P-ᖝ░ྙᱽ (P-Center Problem), P-Median ྙᱽ(P-Median Problem) ॒ᮝಽ
Ǎᇥࡹᨕḥ݅. ✚⯩P-Median ྙᱽ۵Ŗᰆ, Ł, ྜྷඹᖝ░ੱ۵
ŖŖᖅ॒ᮥᖅ⊹⧁ᙹᯩ۵⬥ᅕ᯦ḡaᵝᨕᲙᯩ݅Łaᱶ⦹Ł
b ⬥ᅕ᯦ḡ۵ ᗭእᯱᙹၽᔾḡᩎᮥ ӹ┡ԕ໑bᖅಽᇡ░
bᗭእᯱᨱíᱽ⣩ᮥᙹᘂ⧁ভᗭࡹ۵݉᭥ݚᙹᘂእ᪡ᙹᘂÑญ aᵝᨕᲙᯩ݅Łaᱶ⧁ভ, ↽ᗭ᮹ᙹᘂእᬊᮝಽ༉ुᗭእᯱ᮹
ᙹෝ∊᳒┍ᙹᯩ۵p}ᯕ⦹᮹ᖅᖅ⊹᯦ḡෝđᱶ⦹۵
ྙᱽಽ៉, Ğₑᕽ, ᗭႊᕽ, ᱥ⪵ǎ, ŖŖ᮹ഭᖅ, ⪹Ğญᖅ॒ŝ
zᮡŖŖᖅᯕӹ႒⪵ᱱ, ݡ⩶⧁ᯙๅᰆ, ᯱ࠺₉ᩢᨦᗭ॒ŝzᯕ
Ğᰢᔍॅŝ᮹Ğᰢᯕ⊹ᩕ⦽ၝeᖅ᮹᯦ḡᖁᱶྙᱽӹ☖ᝁၰ
ᱥಆᙹᘂ Ḳᖁᰆ⊹ ᭥⊹ᖁᱶ ྙᱽ, ❭ᯕ⥥ᯙ ᜅ▽ ᖅĥྙᱽ
॒ŝzᮡฯᮡ᮲ᬊᇥᨱᕽᯱᵝၽᔾࡹ۵ྙᱽᯕ݅.(᳑Õ, 2004)
ᯱᱥÑᵝ₉ᰆᮡŖŖᖅಽᕽ٥Ǎӹᱲɝᯕᬊᯕ⦹Ł⠙ญ⦽
Ŕᨱ᭥⊹⧕⦹໑, ᕽᅙᩑǍᨱᕽ۵ᖅྜྷ(ᩍʑᨱᕽ۵ᯱᱥ Ñᵝ₉ᰆ)ᮥᯕᬊ⦹۵ᙹᯱ(ᩍʑᨱᕽ۵ᯱᱥÑᵝ₉ᰆᯕᬊᯱ)ෝ
᭥⧕ᖅྜྷ᮹᭥⊹ᨱᕽ⠪ɁÑญੱ۵⠪Ɂ☖⧪e, ⠪Ɂ☖⧪እ ᬊᯕ↽ᗭ⪵ࡹࠥಾ᭥⊹ෝᖁᱶ⦹۵Łญ᷹ᯙP-Median Algorithm
ᮥᱢᬊ⧉ᯕᱢ⧊⦹݅Ł❱݉⦹ᩡ݅. ᅙᩑǍ᮹ᯕುᱢɝeᮥᯕ
Ł ᯩ۵ P-Median Algorithm᮹ ʑᅙ ༉⩶ᮡ ݅ᮭŝ z݅.
Inputs:
ƆƇ = ᙹḡƇ᮹ ᙹప
ƂƇƈ = ᙹḡƇ᪡ ᖅྜྷ᮹ ᯦ḡᱱƈ᮹ Ñญ Ǝ = ᖅྜྷ᮹ ᙹ
Decision variables:
Ɩƈá 1, อ᧞ יऽƈᨱ ᖅྜྷᯕ ᖅ⊹ࡹ໕, 0, ə౨ḡ ᦫᮝ໕.
ƗƇƈá 1, อ᧞ יऽƈᨱ ᖅྜྷᯕ יऽƇ᮹
ⅾᙹෝ ∊᳒┅໕, 0, ə౨ḡ ᦫᮝ໕.
Subject to¦Ƈƌ
ā
Ƈā
ƈ ƆƇƂƇƈƗƇƈā
ƈ ƗƇƈá Î (for all Ƈ)ā
ƈ Ɩƈá ƎƗƇƈ= Ɩƈ (for all Ƈ, ƈ) ƗƇƈ×ìÎ (for all Ƈ, ƈ) Ɩƈ×ìÎ (for all ƈ)
(1-1) (1-2) (1-3) (1-4) (1-5) (1-6)
Eq. (1-1)ᨱӹ┡ӽ༊ᱢ⧉ᙹ۵ᖅྜྷŝᙹᯱe᮹ⅾ☖⧪Ñ ญෝ ӹ┡ԕ۵ äᯕ໑, ᙹḡƇ᪡ ᖅྜྷ᮹ ᯦ḡᱱƈ᮹ Ñญ
ƂƇƈ۵օ✙ᬭⓍᔢᨱᕽđᱩᱱᔍᯕ᮹↽݉Ğಽෝӹ┡ԙ݅. Eq.
(1-2)᮹ᱽ᧞᳑Õᮡbᙹḡ, ᷪᙹၽᔾḡᱱᮡၹऽ⦹ӹ᮹
ᖅྜྷᨱ᮹⧕ᕽእᜅෝၼᮭᮥ᮹ၙ⦹໑, ᵲᅖᕽእᜅӹᕽእᜅ
ᇡᰍḡᩎᯕ᳕ᰍ⦹ḡᦫᮭᮥӹ┡ԕ۵äᯕ݅. Eq. (1-3)᮹ᱽ᧞᳑
Õᮡᯱᝁ᮹ḡᩎᨱᯩ۵ᖅྜྷᨱ᮹⧕ᕽእᜅෝၼ۵ḡᩎ(ᙹ
ḡ)᮹ᙹ۵ᖅྜྷ᮹ᙹ᪡zᮭᮥӹ┡ԙ݅. Eq. (1-4)᮹ᱽ᧞᳑Õᮡ
อ᧞Ɩƈá ×ᯕ໕ƈḡᩎᨱᖅྜྷᯕ᳕ᰍ⦹ḡᦫᮝ໑Ƈḡᩎᯕᬊᯱ
۵ƈḡᩎ᮹ᖅᨱ⧁ݚࡹḡᦫᮝအಽƗƇƈá ×ᯕࡹŁ, อ᧞Ɩƈá Î ᯕ໕ƈḡᩎᨱᖅྜྷᯕ᳕ᰍ⧉ᮥ᮹ၙ⦹အಽƗƇƈ۵0 ⪚ᮡ1᮹
sᮥaḡíࡹ۵ࢱaḡĞᬑෝ࠺ᨱŁಅ⦹ᩍӹ┡ԙäᯕ݅.
(1-5)ŝ (1-6)ᮡ đᱶᄡᙹƗƇƈ, Ɩƈa 0 ⪚ᮡ 1᮹ sᮥ aḩ ᙹ
ᯩࠥಾ ⦹۵ ᱽ᧞᳑Õॅᯕ݅.
2.2 ׆֜Ցഠ
᯦ḡᖁᱶྙᱽᨱ š⦽ ǎԕ ᩑǍ۵ ݅ᮭŝ z݅.
Son, M. C.(1986)ᮡ ࠥℎʭḡ ᯕ࠺⦹۵ ᗭࡹ۵ ↽ᗭእᬊ
ḡᱱᮥ⪶ᯙ⦹ʑ᭥⧕ᱲɝࠥᇥᕾᮝಽLocation-Allocation ༉ߙ
ᮥ ᔍᬊ⦹ᩡŁ, Ñญ}ֱᨱ ෙ ᱲɝࠥෝ ᔑ⇽⦽ ⬥ ᯕ sᮥ
z-scoreಽ ᄡ⪹⦹ᩍ ᔢ⪙šಉᖒᮥ á☁⦹ᩡ݅.
Kim, K. S.(1991)ᮡᯙᱲḡᩎᨱᕽᩍ్aḡŖŖᕽእᜅෝ
ၼŁᯩ۵⯆ෝݡᔢᮝಽℎᔍᖅ᭥⊹đᱶᨱš⦽ᩑǍෝ
⦹ᩡ݅. Werber༉⩶ŝRawls༉⩶ᮥ₥┾⦹ᩍŖe⩶┽ෝᮁⓕญ ऽ⠪໕ᮝಽᖅᯕᬊᯱᙹ۵ᯙǍⓍʑᨱእಡ⦹ࠥಾ, ↽ᱢ᯦ḡᱱ
ᮡ ᨕ۱ Ŕࠥ ⬥ᅕḡa ࢁ ᙹ ᯩ݅Ł aᱶ⦹Ł ᯙǍᯱഭ᪡ b
࠺ ᵲᝍḡ᮹ Ŗe᳭⢽ෝ Ǎ⦹ᩍ ༉⩶ᨱ ᯦ಆ⦹ᩍ ᇥᕾ⦹ᩡ݅.
Kim, K. S. and Hwang(1992)ᮡ ݡǍ Ǎℎᮥ ݡᔢᮝಽ
Werber༉⩶ŝRawls༉⩶ᮥ₥┾⦹ᩍᯕෝ⥥ಽəఉ⪵⦽Locator
ෝ}ၽ⦹ᩍ↽ᱢ᯦ḡෝᇥᕾ⦹ᩡᮝ໑ᯕ۵bḡᩎᨱݡ⦽ᯙǍȽ
༉᪡ ᳕ᄥᵲᝍḡ ᳭⢽ෝ ᯦ಆ⦹໕↽ᱢ ᯦ḡෝ ᱽ⦹ᩍᵝ۵
äᮝಽ ࡹᨕ ᯩ݅.
Seo, M. A.(2000)۵ᦩ᧲ℎᮥݡᔢᮝಽ↽ᱢ᯦ḡෝ᭥⦽
ʑᵡᮝಽ ḡ⩶, ᯙǍ, ࠥಽ, ḡa, ☁ḡᯕᬊᮥ ᖅᱶ⦹ᩍ ᯙǍ
Potential modelŝGIS ʑჶᮥ⪽ᬊ⦹ᩍᩍ్aḡ᯦ḡʑᵡᨱ
ݡ⦽ ᵲℊᇥᕾᮥ ࠥ⦹ᩡ݅.
Choi, G. J. et al.(2001)ᮡᮁඹᱡᰆ╒ⓍෝݡᔢᮝಽGIS᪡
OR᮹☖⧊ᱢᬊʑჶᮥᯕᬊ⦽⮕ญᜅ❒Łญ᷹ᮥ⪽ᬊ⦹ᩍᱢḡ ᇥᕾᮥᙹ⧪⦹Ł, ᱽࡽႊჶᨱෙđŝ᮹እƱá᷾ᮥ᭥⦹ᩍ
⪝⧊ᱶᙹĥ⫮ჶᮥ ᯕᬊ⦽ ⣡ᯕđŝෝ ᱽ⦹ᩡ݅.
Joo, S. A.(2007)۵GIS⪹Ğᨱᕽ᯦ḡ-႑ᇥྙᱽ⧕đᮥᔍಡಽ
ᵝᨕḥᩑǍḡᩎᮥ༊ᱢᨱᱢ⧊⦽Ŗe݉᭥ಽ᳑Ḣ⦽अᯕෝ
ʑⅩಽᙹߑᯕ░ෝǍ⇶⦹Ł, Ŗe݉᭥᮹ᖅᱶᯕᇥᕾ᮹đŝᨱ
ၙ⊹۵ᵲᖒᮥḡญᱢšᱱᨱᕽᔕ⠕ᅕᦹ݅. ᖅྜྷ᮹᯦ḡ-႑ᇥ
ᇥᕾᮥ᭥⧕ᱱ⩶ᮝಽ☖⧊ࡽᙹߑᯕ░ᯕᬊႊჶುၰߑᯕ░ෝ
Ǎ⇶⦹ʑ᭥⦽Ŗe݉᭥ᖅᱶႊჶᮥ}ၽ⦹ᩡ݅. ੱ⦽}ၽࡽ
ႊჶುᮥ Ğᔢԉࠥ ᬱ᮹ ᔍಡᨱ ᱢᬊ⦹ᩍ á☁⦹ᩡ݅.
Yoo, J. H. et al.(2008)ᮡ ᩑaᜅქᜅ ∊ᱥᗭ ᯦ḡᖁᱶᮥ
᭥⧕ᩑaᜅᙹḡᱱŝŖɪḡᱱe᮹Ŗeᱢ ᇥᕾŝ∊ᱥᨱ
ᗭࡹ۵☖⧪እᬊ↽ᱢ⪵ෝ⪽ᬊ⦽ĥపᱢᇥᕾᮥ☖⧕~šᱢᯕ Ł ⧊ญᱢᯙ ∊ᱥᗭ ᖅ⊹᮹ ❱݉ ɝÑෝ ᱽ⦹ᩡ݅.
3. ༉⩶᮹Ǎ⇶
3.1 ୢ୪ࢫ׆ஜ
ᅙᩑǍᨱᕽ۵ݡᵲƱ☖ᱶÑᰆᮥᯕᬊ⦹ʑ᭥⧕ၼᦥॅᩍḩ
อ⦽ᅕ⧪ᱲɝÑญ᮹ᯥĥ⊹(᧞400m-500m)ෝչᨕᕽíࡹ໕
ݡᵲƱ☖ᯕᬊශᯕɪĊ⯩qᗭ⦽݅۵ǎԕᩑǍđŝෝၵ┶ᮝ ಽ, ᯱᱥÑᵝ₉ᰆ᮹ᯕᬊᩍᇡ۵ᯱᱥÑᵝ₉ᰆʭḡ᮹ᅕ⧪☖⧪
ᱲɝÑญaaᰆᵲ⦽đᱶᄡᙹŁ❱݉⧉ᨱᯱᱥÑᵝ₉ ᰆ᮹ ᭥⊹ᖁᱶ ᯕෝ ᵲᱱᱢᮝಽ Łಅ⦹ᩡ݅. ੱ⦽ ᖅྜྷ᮹
ᖅ⊹⧕ݚᖅྜྷᨱݡ⦽ᯕᬊᙹෝŁಅ⦹۵äᮡ⦥ᙹᱢᯕအ ಽ, ᯱᱥÑᵝ₉ᰆᯕᬊᙹ۵ᯱᱥÑᵝ₉ᰆ᮹᭥⊹ᖁᱶၹऽ
Łಅ⧕ ⧁ ᄡᙹŁ ❱݉⦹ᩡ݅.
ᅙᩑǍᨱᕽ۵Tables 1 and 2᪡zᯕᯱᱥÑ☖⧪༊ᱢᨱ
ᨦྕ/እᨦྕ☖⧪༊ᱢḡಽǍᇥ⦹ᩍb༊ᱢᨱᇡ⧊⦽ᅕ⧪☖⧪᮹
⢽ᵡea⊹ෝ༉⩶ᨱၹᩢ⦹ᩡ݅. ᅕ⧪☖⧪᮹⢽ᵡea⊹۵
ݡᵲƱ☖ᯕᬊᯱ᮹₉ԕea⊹᪡₉ea⊹ᵲࠥᅕᱲɝ
ea⊹᮹ᔢݡᱢaᵲ⊹ಽᱶ᮹⦹ᩡᮝ໑, ᯕෝʑၹᮝಽᅕ⧪᮹
ᨦྕ☖⧪ŝእᨦྕ☖⧪᮹ea⊹۵bb᧞18,626ᬱ/ᯙe, 4,885ᬱ/ᯙeᮝಽ⇵ᱶࡹᨩ݅. ᨦྕ☖⧪ŝእᨦྕ☖⧪᮹እᮉ
ᮥʑၹᮝಽᨦྕ☖⧪ŝእᨦྕ☖⧪᮹ea⊹ෝaᵲ⠪Ɂ⦽ᅕ⧪
⦽☖⧪᮹ ⠪Ɂea⊹۵᧞5,183ᬱ/ᯙeᮝಽ⇵ᱶࡹᨩ݅.
Table 1. Standard of Trip Purpose According to the Destination of Bicycle-Trip
Destination Trip Purpose
Subway / Train station
Business use/
Non-Business use Bus stop
Apartment·House (living accommodation)
Public office
Business use Company (office facility)
Elementary·Middle·High school University
Discount store·Department store (commercial facility)
Non-Business use Culture center / Lyceum
Hospital (medical facility) Library Community institution
Park / Square Stream / Amusement park
Table 2. The Value of Time for Trip Purpose
Trip Purpose Time Value
(won/persontime)
Business use 18,626
Non-Business use 4,885
Average(business/
Non-business) 5,183
3.2 ુॺ୪ઊଲ଼ۀՍଭࡦ෴֜ౠ
✚ᱶḡᩎ᮹ᯱᱥÑᵝ₉ᰆᖅĥᩩᔑᱽ᧞ᯕ᳕ᰍ⦹۵Ğᬑ,
ᯱᱥÑ ᵝ₉ᰆ ⅾ ᖅ⊹}ᙹᨱ ᱽ᧞ᯕ í ࡽ݅. ᯝၹᱢᮝಽ
ᯱᱥÑᵝ₉ᰆ᮹ᖅ⊹}ᙹa ฯᦥḩᙹಾ᳢ᮡeĊᮝಽᯱᱥÑ
ᵝ₉ᰆᮥ ᖅ⊹⦹۵ äᯕ a܆⦹໑, ᯕᨱ ᅕ⧪ ᱲɝÑญa
ṈᦥḡŁᅕ⧪ᯝၹ⪵እᬊᩎqᗭ⦹íࡽ݅. ᷪ, ᅕ⧪ᯝၹ⪵እᬊ
ᮥ↽ᗭ⪵⦹۵ႊჶᮡ↽ݡ⦽ฯᮡ}ᙹ᮹ᯱᱥÑᵝ₉ᰆᮥᱢᱶ
᭥⊹ᨱ ᖅ⊹⦹۵ äᯕŁ ᅝ ᙹ ᯩ݅.
ə్ӹ ᯱᱥÑ ᵝ₉ᰆ᮹ ᖅ⊹}ᙹ᮹ ᷾aᨱ እᬊ ੱ⦽
᷾a⦹íࡹ۵ߑ, ᯕ۵ᮭ᮹⬉ᬊ(-)ᮥ᮹ၙ⦹໑ᅕ⧪ᯝၹ⪵እᬊᮝ ಽ í ࡹ۵ ᧲(+)᮹ ⬉ᬊᮥ ᔢᘥ⍽ đŝᱢᮝಽ ⅾ ⬉ᬊᮥ
qᗭ┍ ᙹ ᯩ݅. ᕽ ᅕ⧪ ᯝၹ⪵እᬊ( Ƈƈ)ŝ ᖅ⊹እᬊ (ƖƇƈZ )ᮥ ༉ࢱ Łಅ⦹ᩍ ⅾ እᬊᮥ ↽ᗭ⪵⦹۵ ᖅ⊹}ᙹ ၰ
᭥⊹ෝ ᦥԕ ⦽݅.
ᯕෝ P-Median Algorithm᮹ ʑᅙ ༉⩶ᮥ ᄡ⩶⦹ᩍ ᔩಽᬕ
༊ᱢ⧉ᙹಽ ӹ┡ԙ ᙹᮡ ݅ᮭŝ z݅.
¦Ƈƌ
ā
Ƈ á Î Ƈƈâā
ƈ á ÎÞƖƈZ ßá ¦ƇƌƇ á Î
ā
Þ¡ƇZ ƒƇƈß âā
ƈ á ÎÞƖƈZ ß (2)ᩍʑᕽ, ᙹḡƇa ᨦྕ☖⧪༊ᱢḡᯙ Ğᬑ:
¡Ƈá ƆƇZ ¯¨├㓠ㄌ㓠
ᙹḡƇa እᨦྕ☖⧪༊ᱢḡᯙ Ğᬑ:
¡Ƈá ƆƇZ ¯¨ᶛ├㓠ㄌ㓠 Ƈ: ☖⧪᮹ ⇽ၽᱱ
ƈ: ᯱᱥÑ ᵝ₉ᰆ ᖅ⊹ḡᱱ ƆƇ: ᙹḡƇ᮹ ᙹప(ᯙ)
¯¨: ᅕ⧪☖⧪ ea⊹(ᬱ/hr) ƒƇƈ: Ƈᨱᕽƈʭḡ᮹ ᅕ⧪☖⧪e(hr)
ƒƇƈá ćÐÓ××Ƒ ƂƇƈ
ƂƇƈ: Ƈᨱᕽƈʭḡ᮹ Ñญ(m) Ƒ: ᅕ⧪ᗮࠥ(m/sec)
Ɩƈ: ƈᨱ ᖅ⊹ࡽ ᯱᱥÑ ᵝ₉ᰆ(}ᗭ)
: ᯱᱥÑ ᵝ₉ᰆ ⠪Ɂ ᖅ⊹እᬊ(ᬱ/}ᗭ)
P-Median Algorithmᮡᙹపŝ↽݉Ğಽๅ✙ฎᜅߑᯕ░ෝ
Łಅ⦹ᩍ↽ᱢ᯦ḡෝᖁᱶ⧁ᙹᯩᮝӹ, đᱶ⦹ᩍ⧁↽᳦⬥ᅕḡ
ᙹa݅ᙹᯙĞᬑ݉ᯝ᮹ᖅྜྷᮥᖁᱶ⦹۵äŝ۵᳑ɩ₉ᯕa
ᯩ݅. ᅙᩑǍᨱᕽ۵݅ᙹ᮹ᯱᱥÑᵝ₉ᰆ᮹᭥⊹ෝᖁᱶ⦹Łᯱ
⦹۵äᯕ༊ᱢᯕအಽ, ⮕ญᜅ❒(Heuristic)⦽ႊჶᮥᯕᬊ⦹ᩡ݅.
Heuristic P-Median Algorithmᮡຝᱡ⦹ӹ᮹ᖅྜྷᮥᖁᱶ⦹۵
ᯝၹᱢᯙ1-Median Łญ᷹ᮥ☖⧕ᖁᱶ⦽⬥⮕ญᜅ❒⦹í↽ᗭ᮹
እᬊᮥaḡ۵ḡᱱᮥᱢᱶ᭥⊹ಽᖁᱶ⦹۵⮱ᮥaḡŁᯩ݅.
ᦥ௹۵ Myopic Algorithmᮝಽ ᯱᱥÑ ᵝ₉ᰆ᮹ ↽ᱢ }ᙹ
ၰ ᭥⊹ෝ ʑ ᭥⦽ ⮕ญᜅ❒ P-Median Algorithmᮡ ᦥ௹᪡
zᮡ ᱩ₉ಽ ⣡ᯕࡽ݅.
Step 1Ƌ á ×ᮝಽⅩʑ⪵⧉.ƌÞƖƋß á ×ᮝಽƖƋᮥŖḲ⧊ᮝ ಽᱶ᮹⧉. Ƌᮡ⩥ᰍʭḡᯱᱥÑᵝ₉ᰆᮝಽᖁᱶࡽ
᯦ḡᱱॅ᮹ᙹෝĥᔑ(count)⦽äᯕ໑, ƖƋ۵Łญ
᷹᮹b݉ĥᨱᕽᯱᱥÑᵝ₉ᰆ᭥⊹ಽᖁᱶࡽƋ}᮹
ᯱᱥÑ ᵝ₉ᰆᯕ݅.
Step 2Ƌᮥ᷾a┕. ᯱᱥÑᵝ₉ᰆᮝಽᖁᱶࡽיऽॅ᮹
ᙹෝ ĥᔑ⧉.
Step 3ƖƋàÎ᮹Ḳ⧊ᨱॅᨕᯩḡᦫ۵b⬥ᅕיऽƈᨱݡ⧕
Ƈ á Î
ā
Ƈƈᮥĥᔑ⧉. יऽƈᨱᕽƋჩṙᯱᱥÑᵝ₉ᰆᮝ ಽ ຝᱡ Ƌ à Î}᮹ ᯱᱥÑ ᵝ₉ᰆᯕ ᵝᨕḥ݅໕,Fig. 2. Algorithm Flow Chart for Positioning of Bike-Parking Lot on Condition of Budget Constraint
ƖƋàÎ᮹Ḳ⧊ᮡຝᱡᖁᱶࡽᖅྜྷᯕ໑, P-Median 1₉༊ᱢ⧉ᙹ᮹sᮝಽƇ á Î
ā
Ƈƈaᵝᨕḱᮥӹ┡ԙ݅.Step 41₉༊ᱢ⧉ᙹ
ā
Ƈ á Î Ƈƈෝ↽ᗭ⪵⦹۵יऽƈÝÞƋßෝ۵݅.
ᷪ,ƈÝÞƋß á åƇ á Î
ā
Ƈƈæᯕ݅. ຝᱡƋ à Î}᮹ᯱᱥÑ ᵝ₉ᰆॅᯕ ᖁᱶࡹᨕḡŁ, Ƌჩṙ ᖅྜྷᨱ
ݡ⦽↽ᱢ᮹᯦ḡಽƈÝÞƋßaᵝᨕḱᮥӹ┡ԥ. Ḳ⧊
ƖƋᮥʑ᭥⧕ƖƋàÎ Ḳ⧊ᨱיऽƈÝÞƋßᮥ⇵a⧉.
ᷪ, ƖƋá ƖƋàÎ ƈÝÞƋßಽ ᖅᱶ⦽݅.
Step 52₉ ༊ᱢ⧉ᙹ
³ƈƋá Þ
ā
Ƈ á Î ƇƈßÝâā
Ƌƈ ÞƖƈZ ßෝ ĥᔑ⧉. ᯕভ, Þā
Ƈ á Î ƇƈßÝ۵1₉༊ᱢ⧉ᙹ᮹⧕ಽ, ᅕ⧪☖⧪ᯝၹ⪵እᬊ᮹ ↽ᗭsᮥ ᮹ၙ⦽݅.
Step 62₉༊ᱢ⧉ᙹ³ƈƋa³ƈƋï ³ƈƋàÎᯕ໕<Step 2ಽ
࠭ᦥq.
³ƈƋð ³ƈƋàÎᯕ໕stop. ᯕভ, Ƌ à Î á Ǝᯕ໑ᖁᱶࡽ
Ǝ}᮹ᯱᱥÑᵝ₉ᰆᮡ༊ᱢ⧉ᙹ³ƈƋෝ↽ᗭ⪵⦹۵
ᯱᱥÑᵝ₉ᰆ᮹↽ᱢⅾ}ᙹᯕ݅.Ǝ âÎ ᯕ⬥ᇡ░۵
ᯱᱥÑᵝ₉ᰆ᮹⇵aᖅ⊹ಽᯙ⦽ᅕ⧪ᯝၹ⪵እᬊ᮹
qᗭᅕ݅, ᖅ⊹እᬊ᮹ ᷾aᇥᯕ ⍅ᕽ ⅾ እᬊᯕ
qᗭ⦹íࢉᮥ᮹ၙ⦽݅.ƖƎ᮹Ḳ⧊ᯕMyopic Algorithm ᮹ ⧕ᯕ݅.
3.3 ુॺ୪ઊଲઢۀՍଭࡦ෴֜ౠ
ḡᩎԕᯱᱥÑᵝ₉ᰆ᮹ᖅ⊹}ᙹaฯᦥḩᙹಾ᳢ᮡeĊᮝಽ
ᯱᱥÑ ᵝ₉ᰆᮥ ᖅ⊹⦹۵ äᯕ a܆⦹݅. ᯕᨱ ᅕ⧪☖⧪
ᱲɝÑญaṈᦥḡ໑ᅕ⧪☖⧪᮹ᯝၹ⪵እᬊᩎqᗭ⦹íࡽ݅.
ᷪ, ᯱᱥÑᵝ₉ᰆ᮹ᖅ⊹}ᙹa᷾a⧁ᙹಾᅕ⧪☖⧪ᯝၹ⪵እᬊ
ᮡ↽ᗭsᮥwíࡹ۵ߑ, ᩩᔑᱽ᧞ᯕᨧ۵Ğᬑᖅ⊹⧁ᙹᯩ۵
ᯱᱥÑᵝ₉ᰆ᮹}ᙹ۵ྕ⦽ݡ(=⳻)Łaᱶ⧁ᙹᯩᮝအಽᅕ⧪
☖⧪ᯝၹ⪵እᬊᮥ↽ᗭ⪵⦹۵༊ᱢ⧉ᙹෝอ᳒┅۵ ⧕ᩎ
ྕ⦽ݡ(=⳻)aࡽ݅. ᯕ్⦽ྙᱽෝ⧕đ⦹ʑ᭥⧕, ᯱᱥÑᵝ₉ᰆ ᮹↽ᱢ}ᙹෝǍ⦹ʑ᭥⦹ᩍ⡍⧉ྙᱽ(Coverage Problem)ෝ
ᱢᬊ⦹ᩡ݅. ⡍⧉ྙᱽᨱᕽ۵ ᯕᬊᯱa ᯱᱥÑ ᵝ₉ᰆᯕ ⍅ქ⧁
ᙹᯩ۵ᵝᨕḥÑญԕᨱᯩ݅໕ᅕ⧪ᱲɝᮥ☖⧕ᯱᱥÑᵝ₉ᰆᮥ
ᯕᬊ⦹íࡹ໑, Ñญaᨕਅᯥĥ⊹ෝⅩŝ⦽݅໕ᯱᱥÑᵝ₉ᰆᮥ
ᯕᬊ⦹ḡᦫ۵݅Łeᵝࡹᨕḥ݅. ᯕෝʑᅙᮝಽ⡍⧉ྙᱽ᮹ᙹ
ᮥ ᯕҭᨕԕʑ ᭥⦹ᩍ ᙹḡƇ, ᯱᱥÑ ᵝ₉ᰆ᮹ ᯦ḡᱱƈᨱ
᭥⊹⦽ᯱᱥÑᵝ₉ᰆᮡƖƈ,Ɩƈa⍅ქ⦹۵Ƈ᮹ᇡᇥḲ⧊(Subset)
ᮡ¬Ƈᮝಽᖅᱶ⦹ᩡ݅. ᯕḲ⧊ᮡᯕḥᔢᙹſƇƈಽᱶ᮹ࢁᙹᯩ۵ߑ,
᯦ḡᱱƈᨱ᭥⊹⦽⦽ᯱᱥÑᵝ₉ᰆƖƈᨱᕽᙹḡƇ᮹ᯕᬊᯱॅᮥ
⍅ქ⧁ᙹᯩ݅໕1᮹sᮥaḡŁ, ə౨ḡᦫᮝ໕0᮹sᮥaḥ݅.
ᷪ, ᙹḡ᪡ᯱᱥÑᵝ₉ᰆe᮹↽݉Ñญaᩢ⨆ǭÑญ(ᅕ⧪☖⧪
ᯥĥÑญ)ᅕ᯲݅Ñӹz݅໕⧕ݚᙹḡ۵ᯱᱥÑᵝ₉ᰆᨱ᮹⧕
⍅ქࡹᨕḥ݅Łั⧁ᙹᯩᮝ໑, ə౨ḡᦫᮝ໕ᙹḡ۵ᯱᱥÑ
ᵝ₉ᰆ᮹ ᩢ⨆ǭ ԕᨱ ⡍⧉ࡹḡ ༜⦹Ł ᕽእᜅෝ ᱽŖၼᮥ ᙹ
ᨧ݅Ł ั⧁ ᙹ ᯩ݅.
ᅕ⧪☖⧪ᱲɝÑญ᮹ᯥĥ⊹ෝᖅᱶ⦹ʑ᭥⦹ᩍǎԕᩑǍॅ
ᮥá☁⦽đŝ, ݡᵲƱ☖ᙹ݉ᮝಽ᮹ᅕ⧪ᱲɝÑญᯥĥ⊹۵᧞
500m ԕᯙäᮝಽӹ┡ԍᮝ໑ᯱᱥÑᵝ₉ᰆᮥᯕᬊ⦹ʑ᭥⦽ᅕ⧪
ᱲɝÑญᯥĥ⊹ᩎᯕ᪡ᮁᔍ⦽ᙹᵡᯝäᮝಽ❱݉ࡽ݅. ᕽ
ᯱᱥÑ ᵝ₉ᰆ᮹ ᩢ⨆ǭ ᯥĥÑญƂƇƈÝ۵ 500mಽ ᖅᱶ⦹ᩡ݅.
ᯕෝ☖⧕ᯱᱥÑᯕᬊݡᔢǭᩎᨱᕽᯱᱥÑᵝ₉ᰆᮥᯕᬊ⧁
ᙹᯩࠥಾ⦹ʑ᭥⧕ၹऽ⦥⦽ᯱᱥÑᵝ₉ᰆ᮹ᙹ, ᷪᯱᱥÑ
ᵝ₉ᰆᖅ⊹}ᙹ᮹↽ᗭs(Lower Bound)ŝᅕ⧪☖⧪ᯝၹ⪵እᬊ
ᮥ↽ᗭ⪵⦹۵↽ᱢ᯦ḡᱱᯕǍ⧕ḥ݅. ᅕ⧪☖⧪ᯝၹ⪵እᬊᮥ
↽ᗭ⪵⦹۵ᯱᱥÑᵝ₉ᰆ᮹᯦ḡᱱၰ⡍⧉ྙᱽ᮹⧕ෝǍ⦹ʑ
᭥⦽Heuristic P-Median algorithm᮹ʑᅙ༉⩶ᮥᄡ⩶⦹ᩍᔩಽ ᬕ ༊ᱢ⧉ᙹಽ ӹ┡ԙ ᙹᮡ ݅ᮭŝ z݅.
¦Ƈƌ
ā
Ƈ á Î Ƈƈá ¦Ƈƌā
Ƈ á ÎÞ¡ƇZ ƒƇƈß (3)Fig. 3. Algorithm Flow Chart for Positioning of Bike-Parking Lot Without Budget Constraint Condition
ᩍʑᕽ, ᙹḡƇa ᨦྕ☖⧪༊ᱢḡᯙ Ğᬑ:
¡Ƈá ƆƇZ ¯¨├㓠ㄌ㓠
ᙹḡƇa እᨦྕ☖⧪༊ᱢḡᯙ Ğᬑ:
¡Ƈá ƆƇZ ¯¨ᶛ├㓠ㄌ㓠
Ƈ: ☖⧪᮹ ⇽ၽᱱ
ƈ: ᯱᱥÑ ᵝ₉ᰆ ᖅ⊹ḡᱱ ƆƇ: ᙹḡƇ᮹ ᙹప(ᯙ)
¯¨: ᅕ⧪☖⧪ ea⊹(ᬱ/hr) ƒƇƈ: Ƈᨱᕽƈʭḡ᮹ ᅕ⧪☖⧪e(hr)
ƒƇƈá ćÐÓ××Ƒ ƂƇƈ
ƂƇƈ: Ƈᨱᕽƈʭḡ᮹ Ñญ(m) Ƒ: ᅕ⧪ᗮࠥ(m/sec)
ᅕ⧪☖⧪ ᯝၹ⪵እᬊᮥ ↽ᗭ⪵⦹۵ ᯱᱥÑ ᵝ₉ᰆ᮹ ᯦ḡᱱ
ၰ⡍⧉ྙᱽ᮹⧕ෝǍ⦹ʑ᭥⦽⮕ญᜅ❒P-Median Algorithmᮡ
ᦥ௹᪡ zᮡ ᱩ₉ಽ ⣡ᯕࡽ݅.
Step 1Ƌ á ×ᮝಽⅩʑ⪵⧉. ƌÞƖƋß á ×ᮝಽƖƋᮥŖḲ⧊ᮝ ಽᱶ᮹⧉.Ƌᮡ⩥ᰍʭḡᯱᱥÑᵝ₉ᰆᮝಽᖁᱶࡽ
᯦ḡᱱॅ᮹ᙹෝĥᔑ(count)⦽äᯕ໑,ƖƋ۵Łญ᷹
᮹b݉ĥᨱᕽᯱᱥÑᵝ₉ᰆ᭥⊹ಽᖁᱶࡽƋ}᮹
ᯱᱥÑᵝ₉ᰆᯕ݅. ੱ⦽, ᯱᱥÑᵝ₉ᰆ᮹ᩢ⨆ǭᯥĥ ÑญƂƇƈÝᨱ ݡ⦽ ƇƈÝෝ ĥᔑ⧉.
Step 2Ƌᮥ᷾a┕. ᯱᱥÑᵝ₉ᰆᮝಽᖁᱶࡽיऽॅ᮹
ᙹෝ ĥᔑ⧉.
Step 3ƖƋàÎ᮹Ḳ⧊ᨱॅᨕᯩḡᦫ۵b⬥ᅕיऽƈᨱݡ⧕
³ƈƋá
ā
Ƈ á Î Ƈƈᮥĥᔑ⧉. יऽƈᨱᕽƋჩṙᯱᱥÑᵝ₉ᰆᮝಽຝᱡƋ à Î}᮹ᯱᱥÑᵝ₉ᰆᯕᵝᨕḥ݅
໕,ƖƋàÎ᮹Ḳ⧊ᮡຝᱡᖁᱶࡽᖅྜྷᯕ໑, P-Median
༊ᱢ⧉ᙹ᮹ sᮝಽ³ƈƋa ᵝᨕḱᮥ ӹ┡ԙ݅.
Step 4༊ᱢ⧉ᙹ³ƈƋෝ↽ᗭ⪵⦹۵יऽƈÝÞƋßෝ۵݅.
ᷪ,ƈÝÞƋß á å³ƈƋæᯕ݅. ຝᱡƋ à Î}᮹ᯱ
ᱥÑᵝ₉ᰆॅᯕᖁᱶࡹᨕḡŁ,Ƌჩṙᖅྜྷᨱݡ⦽
↽ᱢ᮹᯦ḡಽƈÝÞƋßaᵝᨕḱᮥӹ┡ԥ. Ḳ⧊ƖƋᮥ
ʑ᭥⧕ƖƋàÎ Ḳ⧊ᨱיऽƈÝÞƋßᮥ⇵a⧉. ᷪ, ƖƋá ƖƋàÎ ƈÝÞƋßಽ ᖅᱶ⧉.
Step 5ᯱᱥÑᵝ₉ᰆ᮹ᩢ⨆ǭԕ⡍⧉ࡹ۵ᙹḡƇ᮹ᇡᇥḲ
⧊(Subset) ¬Ƈෝ Ǎ⇶⧉. ¬ƇáåƇç Ƈƈï Ýæ. Step 6ᖁᱶࡽ¬Ƈᨱᗮ⦽ᙹḡƇ۵ᯱᱥÑᵝ₉ᰆᩢ⨆ǭ
ԕ⡍⧉ݡᔢᨱᕽᱽ⦽݅. ᷪ,¬Ƈᨱᗮ⦽ᙹḡƇ۵ ⷞStep 4ᨱᕽᖁᱶࡽᯱᱥÑᵝ₉ᰆᮥᯕᬊ⧁ᙹ
ᯩᮝအಽᯱᱥÑᵝ₉ᰆᩢ⨆ǭԕ⡍⧉ݡᔢᨱᕽᱽ
⦽݅.
Step 7ԉᮡᙹḡƇaᯩ݅໕(®Ƈà ¬Ƈd v)ⷞStep 2ಽ
࠭ᦥq. ԉᮡᙹḡƇaᨧ݅໕(®Ƈà ¬Ƈá v) Stop.
ᯕভ,Ƌ á Ǝᯕ໑ᖁᱶࡽƎ}᮹ᯱᱥÑᵝ₉ᰆᮡ༊ᱢ
⧉ᙹ³ƈƋෝ↽ᗭ⪵⦹໑⡍⧉ྙᱽෝอ᳒┅۵ᯱᱥÑ
ᵝ₉ᰆ᮹ ↽ᱢ ⅾ }ᙹᯕ݅. ƖƎ᮹ Ḳ⧊ᯕ Myopic Algorithm᮹ ⧕ᯕ݅.
4. ༉⩶᮹ᱢᬊ
4.1 ंজୢ୪
ᯱᱥÑᵝ₉ᰆ᮹᯦ḡᖁᱶྙᱽ۵݅ෙᖅྜྷॅ᮹᯦ḡᖁᱶྙ
ᱽ᪡۵aḡ₉ᯕᱱᮥw۵݅. ℌṙ, ᯱᱥÑᵝ₉ᰆᮡ᯦ḡ⬥ᅕḡ aᖅྜྷᨱอǎ⦽ࡹ۵äᯕᦥܩᅕࠥ, Ŗ░॒ᨱࠥᖅ⊹a
a܆⦹݅. ࢹṙ, ᯱᱥÑᵝ₉ᰆʭḡ᮹ᱲɝၽᔾ⦹۵ᅕ⧪☖⧪ᮡ
Õྜྷԕᇡੱ۵ᵝ₉ᰆŝzᯕࠥಽaᦥܭŖeᯕӹօ✙ᬭⓍᔢᨱ
ӹ┡ӹḡ ᦫ۵ ⩲ᗭ⦽ ʼn༊ʙ ॒ᮥ ☖⧪Ğಽಽ ᯕᬊ⦹۵ äᯕ
a܆⦹݅. ᕽ ᯱᱥÑ ᵝ₉ᰆ᮹ ᯦ḡᖁᱶྙᱽᨱᕽ۵ ᯕ్⦽
✚ᖒॅᮥၹᩢ⦹۵äᯕ┡ݚ⦹໑, ᅙᩑǍᨱᕽ۵Ċᯱ᮹օ✙ᬭ
Ⓧaᅕ⧪☖⧪᮹Ğಽෝaᰆᮁᔍ⦹íᰍ⩥⧁ᙹᯩ݅Ł❱݉⦹ᩡ
݅. ᯕᨱ⨹օ✙ᬭⓍ۵50m݉᭥ಽ90Z90᮹Ċᯱօ✙
Table 4. The Result From Heuristic Snalysis for Optimal Number of Bicycle Parking Lot
A number of Bike-Parking
Lots
Walking Generalization
Cost(won)
Installation
Cost(won) Total Cost(won)
1 6,782,706 200,000 6,982,706
8 1,568,873 1,600,000 3,168,873
9 1,283,943 1,800,000 3,083,943
10 1,148,211 2,000,000 3,148,211
Fig. 4. Construction of Testing-Network Table 3. Bicycle Traffic and Value of Time According to the
Demand Area
ID Origin Destination Trips
Trips for Bike
ƆƇ ¡Ƈ
1 Residence
(single) 370 6 31,813
2 Residence
(coalition) 1,270 59 307,646
3 Residence
(apartment 1) 2,700 35 179,684
4 Residence
(apartment 2) 2,500 32 166,374
5 Residence
(apartment 3) 7,000 90 465,848
6 Subway 1 30,000 270 1,399,410
7 Subway 2 40,000 360 1,865,880
8 Bus stop 1 10,000 90 466,470
9 Bus stop 2 5,000 45 233,235
10 Bus stop 3 4,000 36 186,588
11 Elementary school 2,400 68 1,273,659 12 Middle school 1 2,000 57 1,061,382 13 Middle school 2 2,000 57 1,061,382 14 High school 2,100 60 1,114,452 15 Department store 2,000 55 269,083 16 Discount store 1 2,000 55 269,083 17 Discount store 2 3,000 83 403,625
18 Public office 1 400 5 95,663
19 Public office 2 6,000 77 1,434,947 20 Public office 3 2,000 26 478,316
21 Lyceum 1 600 21 104,585
22 Lyceum 2 500 18 87,154
23 Church 400 11 53,817
24 Park 1 2,000 55 269,083
25 Park 2 1,500 41 201,813
26 Hospital 5,000 64 313,617
27 Library 8,000 103 501,787
28 Community
institution 1 2,300 30 144,264
29 Community
institution 2 2,300 30 144,264
30 Community
institution 3 1,300 17 81,540
ᬭⓍෝǍᖒ⦹ᩡᮝ໑ᯕෝၵ┶ᮝಽʑᅙࠥಽᮥǍ⇶⦹Ł, ᙹ
ḡ۵ 30}᮹ ᖝ✙ಽᯕऽಽ Ǎᖒ⦹ᩡ݅.
ᯕভ30}ᗭᙹḡ᮹ᗮᖒᮡTable 3ŝzᯕaᱶ⦹Ł༉⩶ᮥ
ᱢᬊ⦹ᩡ݅. ᯱᱥÑၽᔾపƆƇ۵ⅾᙹ݉ၽᔾపᨱᵝÑᮁ⩶ᄥ/☖⧪
༊ᱢḡᄥᯱᱥÑᙹ݉ᇥݕᮉᮥbbᱢᬊ⦹ᩍǍ⦽sᮥᔍᬊ⦹ᩡ݅.
4.2 ࡦ෴ଭୡէր 4.2.1 ુॺ୪ઊଲ଼ۀլ
༊ᱢ⧉ᙹෝ↽ᗭ⪵⦹۵ᯱᱥÑᵝ₉ᰆ᮹ᱢᱶ}ᙹၰ᭥⊹ෝ
ʑ᭥⦹ᩍᯱᱥÑᵝ₉ᰆ᮹ᖅ⊹እᬊᮡ1}ᗭݚ⠪Ɂ20อᬱ1)ᮝ ಽᖅᱶ⦹ᩡ݅. ⮕ญᜅ❒⦽ႊჶᮥᯕᬊ⦹ᩍᯱᱥÑᵝ₉ᰆ᮹↽ᱢ
}ᙹ᪡ əᨱ ෙ ⅾ እᬊᮥ ᇥᕾ⦽ đŝ۵ Table 4᪡ z݅.
ᯱᱥÑᵝ₉ᰆ ↽ᱢ ᯦ḡᨱݡ⦽ ᇥᕾ đŝ۵Table 5, Fig.
5᪡zᮝ໑, ḡ⦹℁ᩎ(2}ᗭ), ⅩᵲŁ॒⦺Ʊ(4}ᗭ), šŖᕽ(1 }ᗭ), ࠥಽᵝᄡ(1}ᗭ), ქᜅᱶඹᰆᵝᄡ(1}ᗭ)ᮝಽӹ┡ԍ݅.
ᯕ۵እᬊᱽ᧞⦹ᨱᕽᅕ⧪☖⧪እᬊᮥ↽ᗭ⪵⦹۵ḡᱱॅᮥᬑᖁ ᱢᮝಽᖁᱶ⦽đŝᯕ໑, ᖁᱶࡽ᯦ḡᱱᮡᯱᱥÑᵝ₉ᰆᮥᯕᬊ
1)ӹᰆ░(http://www.g2b.go.kr/)ᨱᕽᯱᱥÑᅕšݡaĊᮥ
ₙŁ⦹ᩍ ᖅᱶ⦹ᩡᮭ
Table 6. The Result From Heuristic Analysis for Optimal Number of Bicycle Parking Lot
A number of Bike Parking
Lots
Walking Generalization
Cost(won)
Installation
Cost(won) Total Cost(won)
1 6,782,706 1 3.3
19 378,069 25 83.3
20 342,755 27 90.0
21 322,827 27 90.0
Table 5. An Analysis Result of Walking-Generalize Cost for Bicycle Parking Lots
ID Origin Destination
Identical area of influences
The least expense
Parking lots Selection
1 Residence(single) 1 7,512 X
2 Residence(coalition) 1 72,639 X
3 Residence
(apartment 1) 2 43,024 X
4 Residence
(apartment 2) 2 46,215 X
5 Residence
(apartment 3) 2 77,641 X
6 Subway 1 3 19,436 O
7 Subway 2 4 25,915 O
8 Bus stop 1 5 116,618 X
9 Bus stop 2 5 6,479 O
10 Bus stop 3 4 33,690 X
11 Elementary school 6 17,690 O
12 Middle school 1 7 14,741 O
13 Middle school 2 1 14,741 O
14 High school 8 15,478 O
15 Department store 2 29,898 X
16 Discount store 1 2 40,363 X
17 Discount store 2 3 89,694 X
18 Public office 1 4 26,573 X
19 Public office 2 9 19,930 O
20 Public office 3 6 139,509 X
21 Lyceum 1 7 26,146 X
22 Lyceum 2 4 30,262 X
23 Church 1 10,464 X
24 Park 1 2 43,427 X
25 Park 2 5 75,680 X
26 Hospital 7 117,606 X
27 Library 5 55,754 X
28 Community institution 1 4 30,055 X 29 Community institution 2 3 22,040 X 30 Community institution 3 3 14,723 X Note 1) When the figures in bike-parking lots influence area are same, it
means they use the same place.
Note 2) The bike-parking lot that doesn’t mark on table is located among the No.5, 15 and 24 network roads
Fig. 5. Result for Positioning 9 Bicycle-Parking Lots
⦹۵ᙹపᯕฯŁ, ⦺ƱၰšŖᕽ᪡zᯕᨦྕ☖⧪༊ᱢḡಽ
ᇥඹࡹᨕ ׳ᮡ ᅕ⧪☖⧪ ea⊹a ᱢᬊࡽ ḡᱱॅᯙ äᮝಽ
ӹ┡ԍ݅.
4.2.2 ુॺ୪ઊଲઢۀլ
⡍⧉ྙᱽෝอ᳒┅۵ᯱᱥÑᵝ₉ᰆ᮹ᱢᱶ}ᙹၰ༊ᱢ⧉ᙹ
ෝ↽ᗭ⪵⦹۵᭥⊹ෝ۵ᇥᕾᮥᙹ⧪⦹ᩡ݅. ⡍⧉ྙᱽอ᳒᳑Õ
ᮡᩢ⨆ǭԕᙹḡ᮹90% ᯕᔢᮥ⡍⧉┉݅໕ᯱᱥÑᵝ₉ᰆᮥ
∊ᇥ⯩ Ŗɪ⧩݅Ł eᵝ⦹ᩡᮝ໑, ⮕ญᜅ❒⦽ ႊჶᮥ ᯕᬊ⦹ᩍ
ᯱᱥÑᵝ₉ᰆ᮹↽ᱢᖅ⊹}ᙹ᪡ⅾእᬊᮥᇥᕾ⦽đŝ۵Table 6ŝ z݅.
ᯱᱥÑ ᵝ₉ᰆ ᩢ⨆ǭ ԕ ᙹḡ᮹ 90% ᯕᔢᮥ ⡍⧉┅۵
ᯱᱥÑᵝ₉ᰆ᮹↽ᗭ}ᙹ۵20}ᯙäᮝಽӹ┡ԍ݅. 20}᮹
ᯱᱥÑᵝ₉ᰆᨱݡ⦹ᩍᅕ⧪ᯝၹ⪵እᬊ᮹↽ᗭእᬊŝᯥĥእᬊ
ᮥᇥᕾ⦽đŝ۵Table 7ŝzᮝ໑, əᯝၹ⪵እᬊᮥ↽ᗭ⪵⦹۵
ᱢᱶ ᭥⊹۵ Fig. 6ŝ z݅.
ᯱᱥÑᵝ₉ᰆ᮹ᱢᱶ᭥⊹ෝᇥᕾ⦽đŝ, ᯱᱥÑᵝ₉ᰆ᮹
᯦ḡᱱᮡƱ⫭, ⦺ᬱ॒ŝzᯕᙹపᯕᱢᮡḡᱱᮥᱽ⦹Ł
Table 7. An Analysis Result of Walking-Generalize Cost for Bicycle Parking Lots
ID Classification The least Expense
Critical Expense
Inclusion in the Influences
Area
Parking Lots Selection
1 Residence
(single)) 6,628 4,419 X X
2 Residence
(coalition) 4,273 42,729 O O 3 Residence
(apartment 1) 17,469 24,956 O X 4 Residence
(apartment 2) 2,311 23,108 O O 5 Residence
(apartment 3) 6,470 64,701 O O
6 Subway 1 19,436 194,363 O O
7 Subway 2 25,915 259,150 O O
8 Bus stop 1 6,479 64,788 O O
9 Bus stop 2 29,154 32,394 O X
10 Bus stop 3 2,592 25,915 O O
11 Elementary school 17,690 176,897 O O 12 Middle school 1 14,741 147,414 O O 13 Middle school 2 14,741 147,414 O O 14 High school 15,478 154,785 O O 15 Department store 22,424 37,373 O X 16 Discount store 1 7,475 37,373 O O 17 Discount store 2 5,606 56,059 O O 18 Public office 1 11,958 13,287 O X 19 Public office 2 19,930 199,298 O O 20 Public office 3 6,643 66,433 O O
21 Lyceum 1 11,621 14,526 O X
22 Lyceum 2 10,894 12,105 O X
23 Church 10,464 7,475 X X
24 Park 1 3,737 37,373 O O
25 Park 2 2,803 28,030 O O
26 Hospital 4,356 43,558 O O
27 Library 6,969 69,693 O O
28 Community
institution 1 2,004 20,037 O O 29 Community
institution 2 20,037 20,037 O X 30 Community
institution 3 12,458 11,325 X X Fig. 6. Result for Positioning 20 Bicycle Parking Lots
݅᧲⦹íᖁᱶࡹᨩ݅. ᯕ۵ᅕ⧪☖⧪እᬊᮥ↽ᗭ⪵⦹۵ḡᱱॅᮥ
ᬑᖁᱢᮝಽᖁᱶ⦹໕ᕽ⡍⧉ྙᱽෝอ᳒┅ʑ۵⧕ෝǍ⦽đŝᯕ ໑, ᖁᱶࡽ ᯦ḡᱱᮡ ᯱᱥÑ ᵝ₉ᰆᮥ ᯕᬊ⦹۵ ᙹపᯕ ฯᮡ
ݡᵲƱ☖ ᱶÑᰆ, ⦺Ʊ, šŖᕽ, ⧁ᯙᱱ ᵝᄡ ॒ᮝಽ ӹ┡ԍ݅.
Korea Environment Institute(2007)᮹ᩑǍᅕŁᕽᨱᕽᯱᱥÑ
ᵝ₉ᰆ᮹ᵝᯕᬊᰆᗭᨱݡ⦽ᖅྙ᳑ᔍđŝ, ⧁ᯙᱱ, ḡ⦹℁ᩎ
ᵝᄡ, ქᜅᱶඹᰆᵝᄡ, ⦺Ʊ॒᮹ᙽᮝಽᯕᬊශᯕ׳ᮡäᮝಽ
ӹ┡ӽၵᯩ݅. ᯕ్⦽ݡȽ༉ᙹᮁၽᖅྜྷ(⧁ᯙᱱ॒)ŝ┡
ݡᵲƱ☖ᙹ݉ŝ ᩑĥࡹ۵ ḡ⦹℁ᩎ, ქᜅᱶඹᰆ ᵝᄡᮡ ᯱᱥÑ
ᵝ₉ᰆᯕၹऽᖅ⊹ࡹᨕ⦹۵ḡᱱॅᯕŁᅝᙹᯩ݅. ⨹
օ✙ᬭⓍ ᇥᕾ đŝ ᖁᱶࡽ ᯱᱥÑ ᵝ₉ᰆ᮹ ᯦ḡᱱॅᮡ ᝅᱽ
ᯕᬊශᯕ׳ᮡŔŝᯝ⊹⦽݅Ł⧁ᙹᯩᮝ໑, ᕽᅙᩑǍᨱᕽ
ᱽ⦽ᯱᱥÑᵝ₉ᰆ᮹᭥⊹ᖁᱶႊჶᮡ⧊ญᱢᯙđŝෝࠥ⇽⦽
݅Ł ᅝ ᙹ ᯩ݅.
5. đುၰ⨆⬥ᩑǍŝᱽ
݅᧲⦽ ŖŖᖅྜྷ᮹ ᯦ḡᖁᱶྙᱽᨱ ݡ⦽ ᩑǍa ᙹ⧪ࡹᨕ
᪵ᮝӹᯱᱥÑᵝ₉ᰆ᮹᭥⊹ᖁᱶᨱݡ⦽ᩑǍ۵ၙእ⦽ᝅᱶᯕ݅.
ᯱᱥÑᵝ₉ᰆᮡᱲɝᯕᛞŁᙹෝ⬉ŝᱢᮝಽ⡍⧉⧁ᙹᯩ۵
ḡᱱᨱ᭥⊹⧕⦹ḡอᯕ్⦽ḡᱱᯕ}ᯕ໑ᨕॵᯙḡᮥ
ᙹᯩ۵❱݉᮹ɝÑ۵⪶⦹ḡᦫ݅. ᯕᨱᅙᩑǍᨱᕽ۵ᯱᱥÑ
ᵝ₉ᰆ᮹᭥⊹ᖁᱶHeuristic P-Median ༉⩶ᮥᱢᬊ⦹ᩍ, ᩩᔑ ᱽ᧞ᯕ ᯩ۵Ğᬑ᪡ ᨧ۵ ĞᬑಽǍᇥ⦹ᩍ bbᨱ ݡ⦽ႊჶᮥ
ᱽ⦹ᩡ݅. ຝᱡᩩᔑᱽ᧞ᯕᯩ۵Ğᬑᨱ۵ᅕ⧪☖⧪ᯝၹ⪵እᬊ
ၰᖅ⊹እᬊᮥ↽ᗭ⪵⦹۵༊ᱢ⧉ᙹෝอ᳒┅۵ ⧕ෝǍ⦹۵
Łญ᷹ᮥ, ᩩᔑᱽ᧞ᯕ ᨧ۵ Ğᬑᨱ۵ ᅕ⧪☖⧪ ᯝၹ⪵እᬊᮥ
↽ᗭ⪵⦹Ł⡍⧉ྙᱽෝอ᳒┅۵⧕ෝǍ⦹۵Łญ᷹ᮥ}ၽ
⦹ᩡ݅. ༉⩶ᨱᕽᔍᬊࡹ۵ᄡᙹॅ᮹᯦ಆsᮥǍ⧁ᨱ۵ᯱᱥÑ
ᵝ₉ᰆᮥᯕᬊ⦹ʑ᭥⦽ ☖⧪༊ᱢᮥၹᩢ⦹۵äᯕ┡ݚ⦹݅Ł
❱݉⦹ᩍᅕ⧪☖⧪ea⊹ෝ☖⧪༊ᱢᨱ݅íᱢᬊ⦹ࠥ
ಾ⦹ᩡ݅. ᯕෝ᭥⦹ᩍᯱᱥÑ☖⧪᮹ʑ᳦ᱱᮥᇥඹ⦹Łᯕෝ
☖⧪༊ᱢᨱᨦྕ/እᨦྕ☖⧪༊ᱢḡಽǍᇥ⦹ᩍᯱᱥÑ☖⧪
᮹ ʑ᳦ᱱ ᇥඹ ʑᵡᮥ ᱽ⦹ᩡ݅.
༉⩶᮹ᱢᬊᮥ᭥⦹ᩍ⨹օ✙ᬭⓍෝ50m݉᭥ಽ90Z90᮹
ĊᯱᮝಽǍ⇶⦹ᩡᮝ໑, ᯕෝၵ┶ᮝಽʑᅙࠥಽᮥǍ⇶⦹Ł
30}᮹ᙹḡෝᖅᱶ⦹ᩍᖝ✙ಽᯕऽಽǍᖒ⦹ᩡ݅. ᇥᕾđŝ,
ᯱᱥÑᵝ₉ᰆ᮹᯦ḡᱱᮡᩩᔑᱽ᧞ᯕᯩ۵Ğᬑ9}, ᩩᔑᱽ᧞ᯕ
ᨧ۵Ğᬑ20}ಽᖁᱶࡹᨩᮝ໑ᖁᱶࡽ᯦ḡᱱॅᮡᝅᱽᯱᱥÑ
ᵝ₉ᰆ᮹ᯕᬊශᯕ׳ᮡŔŝᯝ⊹⦹۵äᮝಽӹ┡ԍ݅. ᅙᩑǍ۵
1}ࠥӹ1}ḡᩎᨱᕽᯱᱥÑᵝ₉ᰆᖅĥ, ᯱᱥÑᵝ₉ᰆ᮹
↽ᱢ}ᙹၰ᯦ḡᱱᮥđᱶ⦹۵ߑᔍᬊࢁᙹᯩᮥäᮝಽʑݡࡽ݅.
ə్ӹᩩᔑᱽ᧞ᯕᯩ۵Ğᬑ, ᅙᩑǍᨱᕽ۵ᅕ⧪☖⧪እᬊŝ
⧉̹ᯱᱥÑᵝ₉ᰆ᮹ᖅ⊹እᬊᮥŁಅ⦹ᩡ۵ߑᝅᱽ༉⩶᮹ᱢᬊ
ᨱᯱᱥÑᵝ₉ᰆ1}ᗭ᮹⠪Ɂᖅ⊹እᬊᮥ20อᬱᮝಽᖅᱶ⦹ᩍ
ᇥᕾ⦹ᩡ݅. ᯕ ভ ᯱᱥÑ ᅕšݡ ⩶┽ ၰ ᰍḩᨱ እᬊᮡ
႑ ᯕᔢᮝಽ ᷾a ⪚ᮡ qᗭࢁ ᙹ ᯩʑ ভྙᨱ ᅙ ᇥᕾ
ᯱᱥÑᵝ₉ᰆ᮹⠪Ɂᖅ⊹እᬊᮥᔍᬊ⧉ᮝಽᯙ⧕እᬊ⊂໕ᨱᕽ
᪅₉aၽᔾ⧁ᙹᯩ݅۵⦽ĥෝw۵݅. ੱ⦽ᯱᱥÑᵝ₉ᰆᖅ⊹
ḡᱱᄥᵝ₉໕}ᙹᨱəᔑ⇽እᬊŝᇥᕾđŝ۵ᅙᇥᕾŝ
ݍญ ӹ┡ԁ ᙹ ᯩʑ ভྙᨱ ⇵⬥ ᱽᦩ ༉⩶ᨱ ᵝ₉໕ }ᙹෝ
⡍⧉⧁ᙹᯩᨕ⦹ā݅. ษḡสᮝಽᅙᩑǍᨱᕽ۵ᯱᱥÑ☖⧪᮹
ʑ᳦ᱱॅᮥ☖⧪༊ᱢᨱᨦྕ/እᨦྕ☖⧪༊ᱢḡಽǍᇥ⦹ᩍ
ᅕ⧪☖⧪ea⊹ෝᱢᬊ⦹ᩡ۵ߑ, ⨆⬥ᨱ۵☖⧪༊ᱢᮥᖙᇥ⪵⦹
ᩍᯕᨱݡ⦽ᝍࠥᯩ۵ᩑǍa⦥⧁äᯕ݅. əญŁᯱᱥÑᵝ₉ᰆ
ᮥᯕᬊ⦹íࢁᙹపᮥᩩ⊂⦹۵äᯕๅᬑᵲ⦹ӹᙹᩩ⊂ŝ
šಉ⧕ᕽ۵ᅙᩑǍᨱᕽᩑǍჵ᭥ಽ݅ḡᦫᦹᮝအಽ, ᯱᱥÑ
ᵝ₉ᰆ᮹ ᯕᬊᙹ ᩩ⊂ʑჶᨱ ݡ⦽ ⨆⬥ ᩑǍa Ǎࡽ݅.
qᔍ᮹ɡ
ᯕםྙᮡ2012֥ࠥᱶᇡ(ၙ௹᳑ŝ⦺ᇡ)᮹ᰍᬱᮝಽ⦽ǎᩑ Ǎᰍ݉᮹ ḡᬱᮥ ၼᦥ ᙹ⧪ࡽ ᩑǍᯥ(NRF-2010-0029446).
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