89 research outputs found

    ๊ณต์•• ๊ฐ€๋ณ€ ๊ฐ•์„ฑ ์•ก์ถ”์—์ดํ„ฐ๋ฅผ ์ด์šฉํ•œ ์ถฉ๊ฒฉ์„ ํก์ˆ˜ํ•˜๋Š” ๋ฐœ ์ง€์ง€ ์žฅ์น˜

    Get PDF
    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ๊ธฐ๊ณ„๊ณตํ•™๋ถ€, 2021.8. ์กฐ๊ทœ์ง„.์œก์ƒ ์ด๋™์€ ์ผ์ƒ์ƒํ™œ๋™์ž‘ (ADL; activities of daily living) ์— ์žˆ์–ด์„œ ํ•„์ˆ˜๋ถˆ๊ฐ€๊ฒฐํ•œ ์š”์†Œ์ด๋‹ค. ๊ทธ๋ฆฌ๊ณ  ์ด๋Š” ์ถ”์ง„, ๋น„ํ–‰, ์ฐฉ์ง€์˜ ์ผ๋ จ์˜ ์œ„์ƒ๋“ค๋กœ ์ด๋ฃจ์–ด์ง„๋‹ค. ์ด๋™ ๋„์ค‘ ์ง€๋ฉด๋ฐ˜๋ ฅ (GRF; ground reaction force) ์€ ์ถ”์ง„๋ ฅ์œผ๋กœ์จ ์ธ๊ฐ„์˜ ์šด๋™ ์„ฑ๋Šฅ์„ ํ–ฅ์ƒ์‹œํ‚ค์ง€๋งŒ, ๋™์‹œ์— ์ถฉ๋Œ๋ ฅ์œผ๋กœ์จ ์•ˆ์ „์„ ์ €ํ•ดํ•˜๋Š” ์ด์ค‘์„ฑ์„ ๊ฐ–๊ณ  ์žˆ๋‹ค. ์ฟ ์…˜ ์‹ ๋ฐœ์€ ๊ณผ๋„ํ•œ ์ˆ˜์ง ์ง€๋ฉด๋ฐ˜๋ ฅ (vGRF; vertical ground reaction force) ์œผ๋กœ ์ธํ•œ ํ•˜์ง€ ๋ถ€์ƒ์„ ๋ฐฉ์ง€ํ•  ์ˆ˜ ์žˆ๋‹ค๊ณ  ์—ฌ๊ฒจ์ ธ ์™”๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ตœ๊ทผ ์—ฐ๊ตฌ๋“ค์€ ์‹ ๋ฐœ ๊ฐ•์„ฑ์ด ์ตœ๋Œ€ ์ˆ˜์ง ์ง€๋ฉด๋ฐ˜๋ ฅ (PVGRF; peak vGRF) ์„ ๋ณ€ํ™”์‹œํ‚ค์ง€ ๋ชปํ•œ๋‹ค๊ณ  ์ฃผ์žฅํ•˜๋ฉฐ, ์ด๋Ÿฌํ•œ ํ†ต๋…์„ ๋ฐ˜๋ฐ•ํ•˜๊ณ  ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ์ฐจ์ด๋Š” ์‹ ๋ฐœ์„ ํฌํ•จํ•œ ํ•˜๋ฐ˜์‹  ์ „์ฒด ๊ฐ•์„ฑ์ด ํ•ญ์ƒ ์ผ์ •ํ•˜๋ฉฐ ์ฟ ์…˜์˜ ์šฉ์ˆ˜์ฒ ๊ณผ ๊ฐ™์€ ํŠน์„ฑ์ด ๊ทธ ์ถฉ๊ฒฉ ํก์ˆ˜ ๋Šฅ๋ ฅ์„ ์ƒ์‡„์‹œํ‚จ๋‹ค๋Š” โ€œ๊ทผ์œก ์กฐ์œจโ€ ํŒจ๋Ÿฌ๋‹ค์ž„์œผ๋กœ ์„ค๋ช…๋œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ ์ฐฉ์ง€ ๋„์ค‘ ์ˆ˜์ง ์ง€๋ฉด๋ฐ˜๋ ฅ ๊ฐœํ˜•์„ ์žฌ๊ตฌ์„ฑํ•  ์ˆ˜ ์žˆ๋Š” ํ˜์‹ ์ ์ธ ์ถฉ๊ฒฉ ํก์ˆ˜ ๋ฐœ ์ง€์ง€ ์žฅ์น˜๋ฅผ ์ œ์•ˆํ•œ๋‹ค. ์ œ์•ˆ๋œ ์žฅ์น˜๋Š” ๋‹ค์Œ ๋‘๊ฐ€์ง€ ํ•ต์‹ฌ ๊ฐ€์„ค๋“ค์— ํ† ๋Œ€๋ฅผ ๋‘๊ณ  ์žˆ๋‹ค: ์ฐฉ์ง€ ๋„์ค‘ ์‹ ๋ฐœ ๊ฐ•์„ฑ์˜ ๋ณ€ํ™”๋Š” (1) ์ˆ˜์ง ์ง€๋ฉด๋ฐ˜๋ ฅ ๊ฐœํ˜•๊ณผ (2) ์ถฉ๋Œ ์‹œ๊ฐ„์„ ๋ณ€ํ™”์‹œํ‚ฌ ์ˆ˜ ์žˆ๋‹ค. ๋ฐœ ๋งž์ถคํ˜•, ๋ง๋ฐœ๊ตฝ ํ’€๋ฌด ๋ชจ์–‘ ์ฟ ์…˜๋“ค์€ ์ ์ธต์‹ ์ œ์กฐ ๊ณต์ •์„ ํ†ตํ•ด ์ œ์ž‘๋˜์—ˆ์œผ๋ฉฐ, ์‹œํŒ ๋ฏธ๋‹ˆ๋ฉ€๋ฆฌ์ŠคํŠธ ์‹ ๋ฐœ ๋ฐ”๋‹ฅ์— ๋ถ€์ฐฉ๋˜์—ˆ๋‹ค. ๋ณธ ์žฅ์น˜๋Š” ์‹ ๋ฐœ ์•ˆ์ฐฝ ์•„๋ž˜์— ๋‚ด์žฅ๋œ ์„ผ์„œ๋“ค์„ ํ†ตํ•ด ์ ํ”„ ์ฐฉ์ง€ ๋™์ž‘์˜ ์œ„์ƒ๋“ค์„ ์‹๋ณ„ํ•˜๊ณ , ํ•ด๋‹น ์œ„์ƒ๋“ค์— ๋”ฐ๋ผ ์ฟ ์…˜์˜ ์Šคํ”„๋ง ๋ฐ ๊ฐ์‡  ๊ณ„์ˆ˜๋“ค์„ ์กฐ์ ˆํ•œ๋‹ค. ๊ธฐ๊ณ„์  ํŠน์„ฑ ๋ณ€ํ™”๋Š” ์ฟ ์…˜์˜ ๊ณต๊ธฐ ํ๋ฆ„ ์ œ์–ด๋ฅผ ํ†ตํ•ด ๊ตฌํ˜„๋œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ ์ œ์•ˆ๋œ ์žฅ์น˜์˜ ์œ ํšจ์„ฑ์„ ํ‰๊ฐ€ํ•˜๊ณ , ์ด๋ฅผ ๋‹ค์Œ ์„ธ๊ฐ€์ง€ ์‹œํŒ ์‹ ๋ฐœ๋“ค๊ณผ ๋น„๊ตํ•˜๊ธฐ ์œ„ํ•ด ํ•œ ๋ช…์˜ ์—ฐ๊ตฌ์ฐธ์—ฌ์ž์— ๋Œ€ํ•˜์—ฌ ์˜ˆ๋น„ ์‹คํ—˜์„ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค: ๋ฏธ๋‹ˆ๋ฉ€๋ฆฌ์ŠคํŠธ ์‹ ๋ฐœ (MINI), ํผ ์ฟ ์…˜ ์‹ ๋ฐœ (FOAM), ๊ทธ๋ฆฌ๊ณ  ๊ณต๊ธฐ ์ฟ ์…˜ ์‹ ๋ฐœ (AIR). ์ข…ํ•ฉ ์‹คํ—˜ ๊ฒฐ๊ณผ๋Š” ์•ž์„œ ์„ธ์šด ๊ฐ€์„ค๋“ค์„ ํ™•์ฆํ•˜์˜€์œผ๋ฉฐ, ์ œ์•ˆ๋œ ์žฅ์น˜๊ฐ€ ๋ชจ๋“  ์‹ ๋ฐœ๋“ค ์ค‘์—์„œ ๊ฐ€์žฅ ์ข‹์€ ์ถฉ๊ฒฉ ํก์ˆ˜ ๋Šฅ๋ ฅ์„ ๊ฐ€์กŒ์Œ์„ ์‹œ์‚ฌํ•˜์˜€๋‹ค. ๋ณธ ์žฅ์น˜๋Š” ๊ฐ€์žฅ ๋‚ฎ์€ ์ˆ˜์ง ์ˆœ๊ฐ„ ํ•˜์ค‘ ์ฆ๊ฐ€์œจ (VILR; vertical instantaneous loading rate) ์„ ๋‚˜ํƒ€๋‚ด์—ˆ์œผ๋ฉฐ, ์ด๋Š” MINI, FOAM, AIR ๋ณด๋‹ค ๊ฐ๊ฐ 62%, 34%, 24% ๋‚ฎ์•˜๋‹ค. ๋งˆ์ฐฌ๊ฐ€์ง€๋กœ, ๋ณธ ์žฅ์น˜์˜ ์ตœ๋Œ€ ์ˆ˜์ง ์ง€๋ฉด๋ฐ˜๋ ฅ (PVGRF) ์€ MINI, FOAM, AIR ๋ณด๋‹ค ๊ฐ๊ฐ 22%, 17%, 21% ๋‚ฎ์•„, ๋ชจ๋“  ์‹ ๋ฐœ๋“ค ์ค‘ ๊ฐ€์žฅ ๋‚ฎ์•˜๋‹ค. ์ด๋กœ ์ธํ•ด ์ถ”์ง„ ๋ณด์กฐ๊ฐ€ ์•ฝ๊ฐ„ ๊ฐ์†Œํ•˜์˜€์œผ๋‚˜, ์ถฉ๋Œ๋ ฅ์˜ ๊ฐ์†Œ๋Ÿ‰์ด ์ถ”์ง„๋ ฅ์˜ ๊ฐ์†Œ๋Ÿ‰์„ ํฌ๊ฒŒ ์ƒํšŒํ•˜์˜€๋‹ค. ๋ณธ ์žฅ์น˜์˜ ์ตœ๋Œ€ ์ถ”์ง„๋ ฅ (POP; pushoff peak) ์€ MINI, FOAM, AIR ๋ณด๋‹ค ๊ฐ๊ฐ 3.1%, 5.8%, 12% ๋‚ฎ์•„ ์ตœ์†Œ์น˜๋ฅผ ๊ธฐ๋กํ•˜์˜€๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๋ณธ ์žฅ์น˜๋Š” ๋ชจ๋“  ์‹ ๋ฐœ๋“ค ์ค‘ ๊ฐ€์žฅ ๋‚ฎ์€ ์ตœ๋Œ€ ์ถ”์ง„๋ ฅ ๋Œ€๋น„ ์ตœ๋Œ€ ์ˆ˜์ง ์ง€๋ฉด๋ฐ˜๋ ฅ์˜ ๋น„์œจ (PVGRF/POP), ๋‹ค์‹œ ๋งํ•ด ๋™์ผ ์ถ”์ง„๋ ฅ ๋Œ€๋น„ ๊ฐ€์žฅ ๋‚ฎ์€ ์ฐฉ์ง€ ์ถฉ๊ฒฉ์„ ๋‚˜ํƒ€๋‚ด์—ˆ์œผ๋ฉฐ, ์ด๋Š” MINI, FOAM, AIR ๋ณด๋‹ค ๊ฐ๊ฐ 3.1%, 5.8%, 12% ๋‚ฎ์•˜๋‹ค. ์ด๋Ÿฌํ•œ ๊ฒฐ๊ณผ๋Š” ์ถฉ๋Œ ์‹œ๊ฐ„๊ณผ ๋ฐœ๋ชฉ ๊ตฝํž˜ ๋ชจ๋ฉ˜ํŠธ์˜ ์ฆ๊ฐ€์—์„œ ๊ธฐ์ธํ•˜์˜€๋‹ค. ๋ณธ ์žฅ์น˜์˜ ์ถฉ๋Œ ์‹œ๊ฐ„์€ ๋ชจ๋“  ์‹ ๋ฐœ๋“ค ์ค‘ ๊ฐ€์žฅ ๊ธธ์—ˆ์œผ๋ฉฐ, ์ด๋Š” MINI, FOAM, AIR ๋ณด๋‹ค ๊ฐ๊ฐ 12%, 17%, 25% ๊ธธ์—ˆ๋‹ค. ๋˜ํ•œ, ๋ณธ ์žฅ์น˜๋Š” ์ฐฉ์ง€ ๋„์ค‘ ๊ฐ€์žฅ ํฐ ๋ฐœ๋ชฉ ๋ฐœ๋“ฑ ๊ตฝํž˜์„ ์œ ๋ฐœํ•˜์˜€์œผ๋ฉฐ, ์ด๋Š” MINI, FOAM, AIR ๋ณด๋‹ค ๊ฐ๊ฐ 42%, 109%, 124% ํฐ ๊ฐ’์ด์—ˆ๋‹ค. ๊ทธ๋Ÿผ์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ , ์ฐฉ์ง€ ์•ˆ์ •์„ฑ์— ๋ณธ ์žฅ์น˜๊ฐ€ ๋ฏธ์นœ ์˜ํ–ฅ์€ ๋‹ค๋ฅธ ์‹ ๋ฐœ๋“ค๊ณผ ์œ ์‚ฌํ•˜์˜€๋‹ค. ๋ณธ ์žฅ์น˜์˜ ์ฐฉ์ง€ ์‹œ๊ฐ„์€ MINI ๋ณด๋‹ค 18% ์งง์•˜์œผ๋‚˜, FOAM ๊ณผ AIR ๋ณด๋‹ค ๊ฐ๊ฐ 4%, 11% ๊ธธ์—ˆ๋‹ค. ํ•œํŽธ, ๋ณธ ์žฅ์น˜์˜ ์ฐฉ์ง€ ์ถฉ๊ฒฉ๋Ÿ‰์€ ๋ชจ๋“  ์‹ ๋ฐœ๋“ค ์ค‘ ๊ฐ€์žฅ ์ž‘์•˜์œผ๋ฉฐ, ์ด๋Š” MINI, FOAM, AIR ๋ณด๋‹ค ๊ฐ๊ฐ 14%, 4.9%, 2.1% ์ž‘์•˜๋‹ค. ํ–ฅํ›„ ์—ฐ๊ตฌ์—์„œ๋Š” ์ œ์•ˆ๋œ ์žฅ์น˜๋ฅผ ๋…๋ฆฝ๋œ ๋ฌด์„  ์›จ์–ด๋Ÿฌ๋ธ” ์‹œ์Šคํ…œ์œผ๋กœ ์žฌ๊ตฌ์„ฑํ•˜๊ณ , ์ถ”๊ฐ€ ์—ฐ๊ตฌ์ฐธ์—ฌ์ž๋“ค์—๊ฒŒ ์‹คํ—˜์„ ์ˆ˜ํ–‰ํ•˜๋ฉฐ, ๊ฒฉ๋ ฌํ•œ ์Šคํฌ์ธ  ๋„์ค‘ ๋ณธ ์žฅ์น˜์˜ ํšจ๊ณผ๋ฅผ ์ž…์ฆํ•  ๊ฒƒ์ด๋‹ค.The terrestrial locomotion is an indispensable element of the activities of daily living (ADL). This is established by a series of following phases: thrust, flight, and landing. During the locomotion, the ground reaction force (GRF) has a duality which enhances human motor performance as a thrust force, but simultaneously undermines safety as a collision force. Cushioned shoes have been deemed to prevent lower extremity injuries caused by excessive vertical ground reaction force (vGRF). However, recent studies debunk this common belief, asserting that shoe hardness does not change the peak vGRF (PVGRF). This discrepancy is explained by the โ€œmuscle tuningโ€ paradigm that the overall stiffness of the lower body including the shoes remains constant and spring-like function of the cushions offsets their own shock absorption capacity. This paper presents a novel shock-absorbing foot orthotic device capable of re-profiling the vGRF profile during landing. The proposed device is founded on two main hypotheses: change in the shoe hardness during landing can modify (1) the vGRF profile and (2) the collision time. The foot-tailored, horseshoe bellows cushions are made through the layered manufacturing process and affixed to the bottom of commercial minimalist shoes. The proposed device identifies the phases of jump landing through sensors mounted under insoles and modulates the spring and damping coefficients of the cushions in compliance with those phases. The change in mechanical properties is realized by adjusting the airflow of the cushions. This paper conducted a preliminary experiment on one subject to evaluate the validity of the proposed device and compare it with that of three following commercial shoes: minimalist shoes (MINI), foam-cushioned shoes (FOAM), and air-cushioned shoes (AIR). The overall results substantiate the hypotheses, and also indicate that the proposed device has the best shock absorption capability. The proposed device exhibited the lowest vertical instantaneous loading rate (VILR) of all footwear conditions, which was 62%, 34%, and 24% lower than that of MINI, FOAM, and AIR, respectively. Likewise, its PVGRF was the lowest of all conditions, which was 22%, 17%, and 21% lower than that of MINI, FOAM, and AIR, respectively. This led to a slight decrease in the propulsion assistance, but a diminution in the collision force considerably surpassed that in the thrust force. The pushoff peak (POP), or maximum thrust force, of the proposed device was the lowest of all conditions, which was 3.1%, 5.8%, and 12% lower than that of MINI, FOAM, and AIR, respectively. However, the PVGRF to POP ratio (PVGRF/POP), or the landing impact at the same thrust force, of the proposed device was the lowest among all conditions, which was 20%, 11%, and 9.4% lower than that of MINI, FOAM, and AIR, respectively. This is accomplished by increase in the collision time and the ankle flexion moment. The collision time of the proposed device was the longest of all conditions, which was 12%, 17%, and 25% longer than that of MINI, FOAM, and AIR, respectively. Moreover, the proposed device aroused the largest ankle dorsiflexion during landing, which was 42%, 109%, and 124% larger than that of MINI, FOAM, and AIR, respectively. Nevertheless, the influence of the proposed device upon landing stability was analogous to that of the other shoes. The landing time of the proposed device was 18% shorter than that of MINI, while 4% and 11% longer than that of FOAM and AIR, respectively. Meanwhile, its landing impulse was the smallest of all conditions, which was 14%, 4.9%, 2.1% smaller than that of MINI, FOAM, and AIR, respectively. Future work will involve reorganizing the proposed device into an untethered wearable system, testing with additional subjects, and validating its efficacy on strenuous sport activities.Chapter 1. Introduction 1 1.1. Research Background 1 1.2. Research Objectives and Contributions 2 Chapter 2. Methods 5 2.1. Mathematical Modelling 5 2.2. Actuator Design 12 2.3. Manufacturing Process 15 2.4. System Integration 18 2.5. Control Mechanism 21 2.6. Experimental Setup and Protocol 24 2.7. Data Collection and Reduction 26 Chapter 3. Results 30 3.1. vGRF Profile 30 3.2. Shock Absorption 32 3.3. Landing Stability 35 3.4. Kinematic Analysis 37 Chapter 4. Discussion 41 Chapter 5. Conclusion 43 Bibliography 44 Abstract in Korean 49์„

    - Case of next-generation transportation market -

    Get PDF
    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ํ˜‘๋™๊ณผ์ • ๊ธฐ์ˆ ๊ฒฝ์˜ยท๊ฒฝ์ œยท์ •์ฑ…์ „๊ณต, 2020. 8. ์ด์ข…์ˆ˜.The present dissertation aims to provide insights into the application of different artificial neural network models in the analysis of consumer choice regarding next-generation transportation services (NGT). It categorizes consumers decisions regarding the adoption of new services according to Deweys buyer decision process and then analyzes these decisions using a variety of different methods. In particular, various artificial neural network (ANN) models are applied to predict consumers intentions. Also, the dissertation proposes an attention-based ANN model that identifies the key features that affect consumers choices. Consumers preferences for different types of NGT services are analyzed using a hierarchical Bayesian model. The analyzed consumer preferences are utilized to forecast demand for NGT services, evaluate government policies within the transportation market, and provide evidence regarding the social conflicts among traditional and new transportation services. The dissertation uses the Multiple Discrete-Continuous Extreme Value (MDCEV) model to analyze consumers decisions regarding the use of different transportation modes. It also utilizes this MDCEV model analysis to estimate the effect of NGT services on consumers travel mode selection behavior and the environmental effects of the transportation sector. Finally, the findings of the dissertations analyses are combined to generate marketing and policy insights that will promote NGT services in Korea.๋ณธ ์—ฐ๊ตฌ๋Š” ๊ธฐ๊ณ„ํ•™์Šต ๊ธฐ๋ฐ˜์˜ ์ธ๊ณต์ง€๋Šฅ๋ง๊ณผ ๊ธฐ์กด์˜ ํ†ต๊ณ„์  ๋งˆ์ผ€ํŒ… ์„ ํƒ๋ชจํ˜•์„ ํ†ตํ•ฉ์ ์œผ๋กœ ํ™œ์šฉํ•˜์—ฌ ์ œํ’ˆ ๋ฐ ์„œ๋น„์Šค ์ˆ˜์šฉ ์ด๋ก ์œผ๋กœ ์ •์˜๋œ ์†Œ๋น„์ž๋“ค์˜ ์ œํ’ˆ ์ˆ˜์šฉ ํ–‰์œ„๋ฅผ ๋ถ„์„ํ•˜์˜€๋‹ค. ๊ธฐ์กด์˜ ์ œํ’ˆ ์ˆ˜์šฉ ์ด๋ก ๋“ค์€ ์†Œ๋น„์ž๋“ค์˜ ์„ ํƒ์— ๋ผ์น˜๋Š” ์˜ํ–ฅ์„ ๋‹จ๊ณ„๋ณ„๋กœ ์ •์˜ํ•˜์˜€์ง€๋งŒ, ๋Œ€๋ถ€๋ถ„์˜ ์ด๋ก ์€ ์ œํ’ˆ ํŠน์„ฑ์ด ์†Œ๋น„์ž ์„ ํƒ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ๋ถ„์„ํ•˜๊ธฐ ๋ณด๋‹ค๋Š” ์†Œ๋น„์ž๋“ค์˜ ์˜ํ–ฅ, ์ œํ’ˆ์˜ ๋Œ€ํ•œ ์˜๊ฒฌ, ์ง€๊ฐ ์ˆ˜์ค€๊ณผ ์†Œ๋น„์ž ์„ ํƒ์˜ ๊ด€๊ณ„ ๋ถ„์„์— ์ง‘์ค‘ํ•˜์˜€๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ ์—ฐ๊ตฌ๋Š” ์†Œ๋น„์ž์˜ ์ œํ’ˆ ์ˆ˜์šฉ ์˜ํ–ฅ, ๋Œ€์•ˆ ํ‰๊ฐ€ ๊ทธ๋ฆฌ๊ณ  ์ œํ’ˆ ๋ฐ ์‚ฌ์šฉ๋Ÿ‰ ์„ ํƒ์„ ํฌํ•จํ•˜์—ฌ ๋”์šฑ ํฌ๊ด„์ ์ธ ์ธก๋ฉด์—์„œ ์†Œ๋น„์ž ์ œํ’ˆ ์ˆ˜์šฉ ํ–‰์œ„๋ฅผ ๋ถ„์„ํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์†Œ๋น„์ž์˜ ์ œํ’ˆ ์ˆ˜์šฉ ๊ด€๋ จ ์„ ํƒ์„ ์ด ์„ธ ๋‹จ๊ณ„๋กœ ๋ถ„๋ฅ˜ํ•˜์˜€๋‹ค. ์ฒซ ๋ฒˆ์งธ๋Š” ์†Œ๋น„์ž์˜ ์ œํ’ˆ ์‚ฌ์šฉ ์˜ํ–ฅ์„ ๊ฒฐ์ •ํ•˜๋Š” ๋‹จ๊ณ„, ๋‘ ๋ฒˆ์งธ๋Š” ์ œํ’ˆ๋“ค์˜ ๋Œ€์•ˆ์„ ํ‰๊ฐ€ํ•˜๋Š” ๋‹จ๊ณ„, ์„ธ ๋ฒˆ์งธ๋Š” ์ œํ’ˆ์˜ ์‚ฌ์šฉ๋Ÿ‰์„ ์„ ํƒํ•˜๋Š” ๋‹จ๊ณ„๋กœ, ๊ฐ ๋‹จ๊ณ„๋ฅผ ๋ถ„์„ํ•˜๊ธฐ ์œ„ํ•ด์„œ ๋ณธ ์—ฐ๊ตฌ๋Š” ์ธ๊ณต์ง€๋Šฅ๋ง๊ณผ ํ†ต๊ณ„์  ๋งˆ์ผ€ํŒ… ์„ ํƒ๋ชจํ˜•์„ ํ™œ์šฉํ•˜์˜€๋‹ค. ์ธ๊ณต์ง€๋Šฅ๋ง์€ ์˜ˆ์ธก๊ณผ ๋ถ„๋ฅ˜ํ•˜๋Š” ์ž‘์—…์—์„œ ์›”๋“ฑํ•œ ์„ฑ๋Šฅ์„ ๊ฐ€์ง„ ๋ชจํ˜•์œผ๋กœ ์†Œ๋น„์ž๋“ค์˜ ์ œํ’ˆ ์ˆ˜์šฉ ์˜ํ–ฅ์„ ์˜ˆ์ธกํ•˜๊ณ , ์˜ํ–ฅ ์„ ํƒ์— ์˜ํ–ฅ์„ ์ฃผ๋Š” ์ฃผ์š” ๋ณ€์ˆ˜๋“ค์„ ์‹๋ณ„ํ•˜๋Š” ๋ฐ ํ™œ์šฉ๋˜์—ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ ์ œ์•ˆํ•œ ์ฃผ์š” ๋ณ€์ˆ˜ ์‹๋ณ„์„ ์œ„ํ•œ ์ธ๊ณต์ง€๋Šฅ๋ง์€ ๊ธฐ์กด์˜ ๋ณ€์ˆ˜ ์„ ํƒ ๊ธฐ๋ฒ• ๋ณด๋‹ค ๋ชจํ˜• ์ถ”์ • ์ ํ•ฉ๋„ ์ธก๋ฉด์—์„œ ๋†’์€ ์„ฑ๋Šฅ์„ ๋ณด์˜€๋‹ค. ๋ณธ ๋ชจํ˜•์€ ํ–ฅํ›„ ๋น…๋ฐ์ดํ„ฐ์™€ ๊ฐ™์ด ๋งŽ์€ ์–‘์˜ ์†Œ๋น„์ž ๊ด€๋ จ ๋ฐ์ดํ„ฐ๋ฅผ ์ฒ˜๋ฆฌํ•˜๋Š”๋ฐ ํ™œ์šฉ๋  ๊ฐ€๋Šฅ์„ฑ์ด ํด ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ, ๊ธฐ์กด์˜ ์„ค๋ฌธ ์„ค๊ณ„ ๊ธฐ๋ฒ•์„ ๊ฐœ์„ ํ•˜๋Š”๋ฐ ์šฉ์ดํ•œ ๋ฐฉ๋ฒ•๋ก ์œผ๋กœ ํŒ๋‹จ๋œ๋‹ค. ์†Œ๋น„์ž ์„ ํ˜ธ๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•œ ๋Œ€์•ˆ ํ‰๊ฐ€ ๋ฐ ์‚ฌ์šฉ๋Ÿ‰์„ ๋ถ„์„ํ•˜๊ธฐ ์œ„ํ•ด์„œ ํ†ต๊ณ„์  ์„ ํƒ ๋ชจํ˜• ์ค‘ ๊ณ„์ธต์  ๋ฒ ์ด์ง€์•ˆ ๋ชจํ˜•๊ณผ ํ˜ผํ•ฉ MDCEV ๋ชจํ˜•์„ ํ™œ์šฉํ•˜์˜€๋‹ค. ๊ณ„์ธต์  ๋ฒ ์ด์ง€์•ˆ ๋ชจํ˜•์€๊ฐœ๋ณ„์ ์ธ ์†Œ๋น„์ž ์„ ํ˜ธ๋ฅผ ์ถ”์ •ํ•  ์ˆ˜ ์žˆ๋Š” ์žฅ์ ์ด ์žˆ๊ณ , ํ˜ผํ•ฉ MDCEV ๋ชจํ˜•์˜ ๊ฒฝ์šฐ ์†Œ๋น„์ž๋“ค์˜ ์„ ํ˜ธ๋ฅผ ๊ธฐ๋ฐ˜ํ•˜์—ฌ ์„ ํƒ๋œ ๋Œ€์•ˆ๋“ค๋กœ ๋‹ค์–‘ํ•œ ํฌํŠธํด๋ฆฌ์˜ค๋ฅผ ๊ตฌ์„ฑํ•  ์ˆ˜ ์žˆ๊ณ , ๊ฐ ๋Œ€์•ˆ์— ๋Œ€ํ•œ ์‚ฌ์šฉ๋Ÿ‰์„ ๋ถ„์„ํ•  ์ˆ˜ ์žˆ๋‹ค. ์ œ์•ˆ๋œ ๋ชจํ˜•๋“ค์˜ ์‹ค์ฆ ์—ฐ๊ตฌ๋ฅผ ์œ„ํ•ด ์ฐจ์„ธ๋Œ€ ์ž๋™์ฐจ ์ˆ˜์†ก ์„œ๋น„์Šค์— ๋Œ€ํ•œ ์†Œ๋น„์ž๋“ค์˜ ์‚ฌ์šฉ ์˜ํ–ฅ, ์„œ๋น„์Šค ๋Œ€์•ˆ์— ๋Œ€ํ•œ ์„ ํ˜ธ, ์ˆ˜์†ก ์„œ๋น„์Šค๋ณ„ ์‚ฌ์šฉ๋Ÿ‰์„ ๋ถ„์„ํ•˜์˜€๋‹ค. ์‹ค์ฆ ์—ฐ๊ตฌ์—์„œ๋Š” ์ฐจ์„ธ๋Œ€ ์ž๋™์ฐจ ์ˆ˜์†ก ์„œ๋น„์Šค๋ฅผ ์ˆ˜์šฉํ•˜๊ธฐ๊นŒ์ง€ ์†Œ๋น„์ž๋“ค์ด ๊ฒฝํ—˜ํ•˜๋Š” ๋‹จ๊ณ„๋ณ„ ์„ ํƒ ์ƒํ™ฉ์„ ๋ฐ˜์˜ํ•˜์˜€์œผ๋ฉฐ, ๊ฐ ๋‹จ๊ณ„์—์„œ ๋„์ถœ๋œ ๊ฒฐ๊ณผ๋ฅผ ํ†ตํ•ด ํ–ฅํ›„ ์ฐจ์„ธ๋Œ€ ์ž๋™์ฐจ ์ˆ˜์†ก ์„œ๋น„์Šค์˜ ์„ฑ์žฅ ๊ฐ€๋Šฅ์„ฑ๊ณผ ์†Œ๋น„์ž๋“ค์˜ ์ด๋™ ํ–‰์œ„ ๋ณ€ํ™”์— ๋Œ€ํ•ด ์˜ˆ์ธกํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•ด ์ธ๊ณต์ง€๋Šฅ๋ง์ด ์†Œ๋น„์ž ๊ด€๋ จ ์—ฐ๊ตฌ์—์„œ ์œ ์šฉํ•˜๊ฒŒ ํ™œ์šฉ๋  ์ˆ˜ ์žˆ์Œ์„ ๋ณด์˜€์œผ๋ฉฐ, ์ธ๊ณต์ง€๋Šฅ๋ง๊ณผ ํ†ต๊ณ„์  ๋งˆ์ผ€ํŒ… ์„ ํƒ๋ชจํ˜•์ด ๊ฒฐํ•ฉ๋  ๊ฒฝ์šฐ ์†Œ๋น„์ž๋“ค์˜ ์ œํ’ˆ ์„ ํƒ ํ–‰์œ„๋ฟ๋งŒ ์•„๋‹ˆ๋ผ, ์ œํ’ˆ ์„ ํƒ ์˜์‚ฌ๊ฒฐ์ • ๊ณผ์ • ์ „๋ฐ˜์— ๊ฑธ์ณ ์†Œ๋น„์ž ์„ ํ˜ธ๋ฅผ ํฌ๊ด„์ ์œผ๋กœ ๋ถ„์„ํ•  ์ˆ˜ ์žˆ์Œ์„ ํ™•์ธํ•˜์˜€๋‹ค.Chapter 1. Introduction 1 1.1 Research Background 1 1.2 Research Objective 7 1.3 Research Outline 12 Chapter 2. Literature Review 14 2.1 Product and Technology Diffusion Theory 14 2.1.1. Extension of Adoption Models 19 2.2 Artificial Neural Network 22 2.2.1 General Component of the Artificial Neural Network 22 2.2.2 Activation Functions of Artificial Neural Network 26 2.3 Modeling Consumer Choice: Discrete Choice Model 32 2.3.1 Multinomial Logit Model 32 2.3.2 Mixed Logit Model 34 2.3.3 Latent Class Model 37 2.4 Modeling Consumer Heuristics in Discrete Choice Model 39 2.4.1 Consumer Decision Rule in Discrete Choice Model: Compensatory and Non-Compensatory Models 39 2.4.2 Choice Set Formation Behaviors: Semi-Compensatory Models 42 2.4.3 Modeling Consumer Usage: MDCEV Model 50 2.5 Difference between Artificial Neural Network and Choice Modeling 53 2.6 Limitations of Previous Studies and Research Motivation 58 Chapter 3. Methodology 63 3.1 Artificial Neural Network Models for Prediction 63 3.1.1 Multiple Perceptron Model 63 3.1.2 Convolutional Neural Network 69 3.1.3 Bayesian Neural Network 72 3.2 Feature Identification Model through Attention 77 3.3 Hierarchical Bayesian Model 83 3.4 Multiple Discrete-Continuous Extreme Value Model 86 Chapter 4. Empirical Analysis: Consumer Preference and Selection of Transportation Mode 98 4.1 Empirical Analysis Framework 98 4.2 Data 101 4.2.1 Overview of the Survey 101 4.3 Empirical Study I: Consumer Intention to New Type of Transportation 110 4.3.1 Research Motivation and Goal 110 4.3.2 Data and Model Setup 114 4.3.3 Result and Discussion 123 4.4 Empirical Study II: Consumer Choice and Preference for New Types of Transportation 142 4.4.1 Research Motivation and Goal 142 4.4.2 Data and Model Setup 144 4.4.3 Result and Discussion 149 4.5 Empirical Study III: Impact of New Transportation Mode on Consumers Travel Behavior 163 4.5.1 Research Motivation and Goal 163 4.5.2 Data and Model Setup 164 4.5.3 Result and Discussion 166 Chapter 5. Discussion 182 Bibliography 187 Appendix: Survey used in the analysis 209 Abstract (Korean) 241Docto

    Full-time parents' own-vehicle user behavior and transportation modes in neighborhood unit

    Get PDF
    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› ๊ณต๊ณผ๋Œ€ํ•™ ํ˜‘๋™๊ณผ์ • ๋„์‹œ์„ค๊ณ„ํ•™์ „๊ณต, 2017. 8. ๋ฐ•์†Œํ˜„.์ฃผ๋ถ€๋“ค์€ ์–ธ์ œ, ์™œ ์ž๊ฐ€์šฉ์„ ํƒ€๋Š”๊ฐ€? ์ฃผ๋ถ€๋“ค์˜ ์ž๊ฐ€์šฉ ์ด์šฉ ์—ฐ๊ตฌ๋Š” ๊ฐ€๊ตฌ ๋‚ด ์„ธ์ปจ๋“œ์นด์— ๋Œ€ํ•œ ์ˆ˜์š”๊ฐ€ ์ฆ๊ฐ€ํ•˜๋Š” ์ง€์ ๊ณผ ๋งž๋‹ฟ์•„ ์žˆ์œผ๋ฉฐ, ๋™์‹œ์— ๊ทผ๋ฆฐ์ƒํ™œ๊ถŒ ๋‚ด ์ด๋™ํ–‰ํƒœ ์—ฐ๊ตฌ์— ์žˆ์–ด ์ฃผ์š”ํ•œ ์‹œ์‚ฌ์ ์„ ์ค€๋‹ค. ์„œ์šธ์‹œ์˜ ์ž๊ฐ€์šฉ ๋ณด์œ ๋Ÿ‰์€ ๊ณ„์†ํ•ด์„œ ์ฆ๊ฐ€ํ•ด์™”์œผ๋ฉฐ, ์ด๋Š” ์ฃผ์ฐจ๊ณต๊ฐ„์˜ ๋ถ€์กฑ, ๊ตํ†ต์ฒด์ฆ์˜ ๊ฐ€์ค‘, ๊ฐ€๋กœ ๋ฐ ์ฃผ๊ฑฐํ™˜๊ฒฝ์˜ ์•…ํ™”๋กœ ์ด์–ด์ง€๊ณ  ์žˆ๋‹ค. ์„œ์šธ์‹œ๋Š” ๋งŽ์€ ๊ตํ†ต์ •์ฑ…๊ณผ ๋„์‹œํ™˜๊ฒฝ๊ฐœ์„ ์„ ํ†ตํ•ด ๋…ธ๋ ฅํ•˜๊ณ  ์žˆ์ง€๋งŒ, ์ด ๋ฌธ์ œ๋Š” ๊ณต๊ธ‰์ž๊ฐ€ ์•„๋‹Œ ์ˆ˜์š”์ธต์˜ ์ž…์žฅ์—์„œ ๊ณ ๋ คํ•  ํ•„์š”๊ฐ€ ์žˆ๋‹ค. ๋‹ค์‹œ ๋งํ•ด, ์ด์šฉ๊ฐ์˜ ์ž…์žฅ์—์„œ ์ž๊ฐ€์šฉ์ด ๊ฐ–๋Š” ์žฅ๋‹จ์ ์„ ์ดํ•ดํ•˜๊ณ  ๋‹ค๋ฅธ ์ด๋™์ˆ˜๋‹จ์˜ ๋Œ€์ฒด๊ฐ€๋Šฅ์„ฑ์„ ๋†’์—ฌ์•ผ ํ•œ๋‹ค. ๊ทธ๋Ÿฌํ•œ ์˜๋ฏธ์—์„œ ์ฃผ๋ถ€์ง‘๋‹จ์€ ๊ทผ๋ฆฐ์ƒํ™œ๊ถŒ์—์„œ ํฐ ์˜ํ–ฅ๋ ฅ์„ ๊ฐ–๊ณ  ์žˆ๋Š” ์ž๊ฐ€์šฉ ์ˆ˜์š”์ธต์ด๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ์„œ์šธ์‹œ ์ฃผ๊ฑฐ์ง€์—ญ 4๊ณณ์˜ ์ฃผ๋ถ€ 40๋ช…์˜ ํ†ตํ–‰์ผ์ง€๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ๊ทธ๋“ค์˜ ์ž๊ฐ€์šฉ ์ด์šฉ ์–‘์ƒ์„ ๋ถ„์„ํ•˜๊ณ , ์ด๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ๊ธฐ์กด ๋Œ€์ค‘๊ตํ†ต ์‹œ์Šคํ…œ์ด ํ•ด์†Œํ•  ์ˆ˜ ์—†์—ˆ๋˜ ์ž๊ฐ€์šฉ ์ˆ˜์š”๋ฅผ ์ฐพ์•„, ๋Œ€์ค‘๊ตํ†ต์„ ํ†ตํ•ด ์ž๊ฐ€์šฉ ์ด์šฉ์„ ๋Œ€์ฒดํ•  ๋ฐฉ์•ˆ์„ ๋ชจ์ƒ‰ํ•˜๋Š”๋ฐ ๋ชฉํ‘œ๋ฅผ ๋‘”๋‹ค. ์—ฐ๊ตฌ๊ฒฐ๊ณผ ์ฃผ๋ถ€๋“ค์˜ ์ž๊ฐ€์šฉ ์ด์šฉ ์–‘์ƒ์€ ๋‹ค์Œ๊ณผ ๊ฐ™์€ ํŠน์ง•์„ ๊ฐ–๋Š”๋‹ค. ์ฃผ๋ถ€๋“ค์€ ๋ชฉ์ ์ง€๋“ค์„ ์—ฐ์†์ ์œผ๋กœ ์—ฐ๊ฒฐํ•˜๋Š” ์—ฐ๊ณ„๊ฒฝ๋กœ๋ฅผ ์ข…ํ•ฉ์ ์œผ๋กœ ๊ณ ๋ คํ•œ๋‹ค. ๋”ฐ๋ผ์„œ ๋Œ€์ค‘๊ตํ†ต์ด๋‚˜ ํƒ์‹œ๊ฐ€ ๋‹จ์ผ ๊ฒฝ๋กœ(์ถœ๋ฐœ์ง€-๋ชฉ์ ์ง€) ๋‹จ์œ„๋กœ ์›€์ง์ด๋Š” ๋ฐ˜๋ฉด์—, ์ž๊ฐ€์šฉ์€ ์—ฐ๊ณ„๊ฒฝ๋กœ๋“ค์„ ํ•˜๋‚˜์˜ ์ด๋™์ˆ˜๋‹จ์œผ๋กœ ํ•ด๊ฒฐํ•  ์ˆ˜ ์žˆ๋Š” ์žฅ์ ์ด ์žˆ๋‹ค. ์ž๊ฐ€์šฉ์˜ ๊ฐœ์ธ๊ณต๊ฐ„ ํ™œ์šฉ ์—ญ์‹œ ์ปค๋‹ค๋ž€ ์žฅ์ ์ด๋‹ค. ์ž๊ฐ€์šฉ ์ด๋™๊ฒฝ๋กœ ์ค‘ ๋Œ€๋ถ€๋ถ„์€ ์ง์ด๋‚˜ ๋™์Šน์ž๊ฐ€ ํ•จ๊ป˜ ํƒ‘์Šนํ•˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ด๋™๋ชฉ์ ์œผ๋กœ๋Š” ์‡ผํ•‘์ด๋‚˜ ์ž๋…€ ๋“ฑํ•˜๊ต ๋“ฑ ์ฃผ๋ถ€๋“ค๊ณผ ๊ด€๊ณ„๋œ ์ด์šฉ ๋น„์ค‘์ด ๋‘๋“œ๋Ÿฌ์กŒ์œผ๋ฉฐ, ๋Œ€๋ถ€๋ถ„์˜ ์ž๊ฐ€์šฉ ์ด์šฉ์ด ์˜ค์ „ 9์‹œ๋ถ€ํ„ฐ ์˜คํ›„ 6์‹œ ์‚ฌ์ด์˜ ์ฃผ๊ฐ„์— ์ด๋ฃจ์–ด์กŒ๋‹ค. ๋”ฐ๋ผ์„œ ๊ทผ๋ฆฐ์˜์—ญ์—์„œ ์ฃผ๋ถ€๋“ค์˜ ์ž๊ฐ€์šฉ ์ˆ˜์š”๋ฅผ ํ•ด์†Œํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ๋Œ€์ฒด์ด๋™์ˆ˜๋‹จ์€ ๋‹ค์Œ๊ณผ ๊ฐ™์ด ๊ตฌ์„ฑ๋˜์–ด์•ผ ํ•œ๋‹ค. ์ฒซ์งธ, ์—ฐ๊ณ„๊ฒฝ๋กœ๋ฅผ ์—ฐ์†์ ์œผ๋กœ ์ด์šฉํ•  ์ˆ˜ ์žˆ๊ณ , ๋‘˜์งธ, ๋™์Šน์ž์™€ ์ง์„ ์œ„ํ•œ ๊ณต๊ฐ„์ด ์ œ๊ณต๋˜๋ฉฐ, ์…‹์งธ, ์ฃผ๋ถ€๋“ค์˜ ์ฃผ๋œ ๋ชฉ์ ์ง€์™€ ์ด์šฉ์‹œ๊ฐ„๋Œ€๋ฅผ ๋ฐ˜์˜ํ•˜๋Š” ์ด๋™์ฒด๊ณ„๊ฐ€ ํ•„์š”ํ•˜๋‹ค.Why do full-time parents take their own car instead of walking or public transport? The use of full-time parents by own vehicles is in line with the growing demand for secondary cars in households and at the same time gives major implications for the study of mobility in the neighborhood. The car ownership has been steadily increasing, leading to the shortage of parking space, the increase of heavy traffic, the deterioration of street and residential environment. Although the city government of Seoul is making efforts through the improvement of transportation and urban environment, it is insufficient to consider this problem from the viewpoint of the people who demand, not the supplier. In other words, it is necessary to understand from the transportation users view for increasing the possibility of substitution of car use. In this sense, the group of full-time parents is a demanding family of car that has a great influence in the neighborhood. This study analyzed the patterns of their use of cars based on the mobility logbooks of 40 homemakers in four different residential areas in Seoul. Based on the use behavior of full-time parents, this research focused on own car problem which has not been lessened by the existing public transport system. In addition, I suggested revised transport system in order to substitute the parents use of cars. In particular, car sharing system, Na-noom car, in which had launched in Seoul since 2013, inspired many characteristics for suggestion of this research. The results of this study were as follows. Full-time parents comprehensively considered the linking path that connected the serial destinations. Therefore, while public transportation and taxi moved in a single route-from departure to destination-, personal vehicle had advantage of taking whole paths by one moving means. The use of private space was also a great advantage. It was found that most of the own-car routes were accompanied by luggage or passengers. For the purpose of transportation, parents often used cars for shopping and childcare was remarkable. Most of the cars were used during the week from 9:00 am to 18:00 pm. Therefore, in order to satisfy the demand of movement in the neighborhood area, the alternative transportation system should be composed as follows. First, it is necessary to consider a continuous route. Also, it needs to provide a room for passengers and baggage. The mobile system has to reflect the destinations and time zones which are mainly used by full-time parents.1. ๋ฌธ์ œ ์ œ๊ธฐ์™€ ์—ฐ๊ตฌ ๋ชฉ์  1 2. ์ด๋ก ๊ณผ ๊ด€์  8 2.1 ๊ทผ๋ฆฐ์ฃผ๊ตฌ ์ด๋ก  8 2.2 ๊ทผ๋ฆฐ ๋‚ด ๋Œ€์ค‘๊ตํ†ต๊ณผ ๋ณดํ–‰ ๊ทธ๋ฆฌ๊ณ  ์ž๊ฐ€์šฉ 10 2.3 ์นด์…ฐ์–ด๋ง ์„œ๋น„์Šค 11 2.4 ๊ฐœ๋… ์„ค๋ช… 14 3. ์กฐ์‚ฌ ๋ฐ ๋ถ„์„ ๋ฐฉ๋ฒ• 22 3.1 ์—ฐ๊ตฌ ๋ฐฉ๋ฒ• ๊ตฌ์ƒ 22 3.2 ๋‹จ๊ณ„๋ณ„ ์—ฐ๊ตฌ๊ณผ์ • 27 4. ์ฃผ๋ถ€๋“ค์˜ ์ž๊ฐ€์šฉ ์ด์šฉ ์–‘์ƒ 33 4.1 ํŠน์ง•์ ์ธ ๊ฐœ๋ณ„ ์‚ฌ๋ก€๋“ค 33 4.2 ์˜ํ–ฅ์š”์ธ๋ณ„ ์ž๊ฐ€์šฉ ์ด์šฉ ์†์„ฑ 43 4.3 ์ž๊ฐ€์šฉ๊ณผ ๊ธฐ์กด ์ด๋™์ˆ˜๋‹จ, ๊ทธ๋ฆฌ๊ณ  ์นด์…ฐ์–ด๋ง 54 4.4 ๋Œ€์•ˆ ์ œ์‹œ 61 5. ๊ฒฐ๋ก  ๋ฐ ์‹œ์‚ฌ์  64 ์ฐธ๊ณ ๋ฌธํ—Œ 66Maste

    ์ œํ’ˆ ์ถœ์‹œ์— ๋”ฐ๋ฅธ ๋งˆ์ผ€ํŒ… ๋ณ€ํ™”์— ๋Œ€ํ•œ ๋ถ„์„

    Get PDF
    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ํ˜‘๋™๊ณผ์ • ๊ธฐ์ˆ ๊ฒฝ์˜ยท๊ฒฝ์ œยท์ •์ฑ…์ „๊ณต, 2015. 2. ์ด์ข…์ˆ˜.Preserving the environment has been one of the key topics of the government and industrys goal in the 21st century. As part of the movement, developing an eco-friendly vehicle has become one of the most promising method in sustaining the environment. The key to successful transformation of the automobile market will require both the support from government policies and the research & development effort of the firms to produce the right kind of alternative fuel vehicles. Based on the Korean market, this research use both revealed preference (RP) and stated preference (SP) data through joint mixed logit estimation to analyze the effect of the increasing EV and HV vehicles in the automobile market through the changes in the customers preferences. The results of this research will determine which alternative fuel vehicle is most preferred by the consumers, and how the market will change when such vehicles are diffused.Contents Abstract iii List of figures vi List of tables vii 1. Introduction ๏ผ‘ 2. Previous research and theoretical background ๏ผ— 2.1. Environmental issues and the alternative fuel vehicle market ๏ผ— 2.2. Government policies ๏ผ™ 2.2.1. Korean government policies ๏ผ‘๏ผ‘ 2.2.2. Electric Vehicle in Korea ๏ผ‘๏ผ’ 2.2.3. Hybrid-vehicles in Korea ๏ผ‘๏ผ• 2.2.4. Policies regarding EV and HV ๏ผ‘๏ผ– 2.3. Firm Strategies ๏ผ’๏ผ‘ 2.3.1. Proprietary development: Hyundai/Kia, Volkswagen ๏ผ’๏ผ’ 2.4. Previous Researches/ Literature Review ๏ผ’๏ผ“ 2.4.1. Researches on automobile market share analysis ๏ผ’๏ผ“ 2.4.2. BLP methodology ๏ผ’๏ผ• 2.4.3. BLP interaction term methodology ๏ผ’๏ผ— 2.4.4. Joint mixed logit model ๏ผ’๏ผ˜ 3. Empirical model setup ๏ผ“๏ผ“ 3.1. Research Framework ๏ผ“๏ผ“ 3.2. Data ๏ผ“๏ผ“ 3.2.1. Stated Preference ๏ผ“๏ผ“ 3.2.2. Revealed Preference ๏ผ“๏ผ˜ 3.3. Methodology ๏ผ”๏ผ’ 3.3.1. Mixed logit framework ๏ผ”๏ผ’ 3.3.2. Simulations ๏ผ”๏ผ˜ 4. Empirical studies and results ๏ผ”๏ผ™ 4.1. Separate estimations ๏ผ”๏ผ™ 4.1.1. Stated preference data estimation ๏ผ”๏ผ™ 4.1.2. Revealed preference data estimation ๏ผ•๏ผ” 4.2. Joint estimation ๏ผ•๏ผ– 4.2.1. SUV and Sedan estimation ๏ผ•๏ผ™ 4.3. Market Simulation ๏ผ–๏ผ‘ 4.3.1. Lump Sum ๏ผ–๏ผ” 4.3.2. Annual subsidy ๏ผ—๏ผ‘ 4.3.3. Availability ๏ผ—๏ผ• 5. Discussion and conclusion ๏ผ—๏ผ— 6. Reference ๏ผ˜๏ผ Appendix: Survey on smart-device usage in 2010 ๏ผ˜๏ผ” ์ดˆ ๋ก ๏ผ˜๏ผ™Maste

    Preformulation and oral absorption improvement of Revaprazan, a new acid pump antagonist

    No full text
    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์•ฝํ•™๊ณผ ๋ฌผ๋ฆฌ์•ฝํ•™์ „๊ณต,2004.Docto

    ๋ฒ ๋„ค์‹œ์•ˆ ๋ธ”๋ผ์ธ๋“œ ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ์ œ์–ด ์‹œ๊ฐ„ ๊ฐ„๊ฒฉ ๋ฐ ์˜ˆ์ธก ์ œ์–ด์— ๊ด€ํ•œ ์—ฐ๊ตฌ

    No full text
    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) --์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๊ฑด์ถ•ํ•™๊ณผ,2008.2.Maste

    ์Šค๋งˆํŠธํฐ ์‹ ์ฒดํ™œ๋™ ๋ฐ์ดํ„ฐ ๊ธฐ๋ฐ˜ ์„œ์šธ์‹œ ์„œ์ดˆ๊ตฌ ์‚ฌ๋ก€์—ฐ๊ตฌ

    No full text
    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ๊ฑด์ถ•ํ•™๊ณผ, 2022. 8. ๋ฐ•์†Œํ˜„.As physical inactivity has emerged as a global problem, walking activities that are easily accessible to many people have been raised as one of the primary strategies for promoting physical activities in cities. To achieve positive effects on health, people require a sufficient level of physical activity; at least 10-minutes duration and moderate intensity, according to the global recommendation of the World Health Organization. However, most daily walking activities fall short of the recommended level of physical activity. Parks are one of the most active urban places that can bring such a sufficient level of walking activities. Urban planning and public health sectors have focused on making urban parks walkable, such as enhancing accessibility to the parks, improving park environments, and activating park programs. For these efforts with parks to contribute to health benefits, it requires an evidence-based understanding of how urban parks promote a 'healthy walking,' that meets public health recommendations, with more than 10-minutes duration and moderate intensity. Thus, empirical studies are needed to measure the duration and intensity of park walking. However, there were many difficulties in measuring the walking activities in parks due to methodological limitations. It was difficult for traditional methods using questionnaires to quantify the level of walking activities because the self-reported data were too subjective. Although quantitative analysis for walking has been possible through gait-measuring devices, there was a limit to increasing the number of subjects; parks and pedestrians. With the recent technological development of smartphone-based human activity recognition, it has expected as a new approach that can assess a large amount of data and expand the range of research areas. This study uses smartphone-based human activity data to figure out quantitative and spatial characteristics of walking activities in urban parks in terms of healthy walking. The goal of this study is; first to examine the differences between healthy walking in parks and healthy walking in other areas; second, to classify the type of parks in terms of healthy walking; and third, to derive the spatial distribution characteristics of healthy walking in parks. This study covers the urban park facilities (n=130) using smartphone-assessed walking activity data (n=550,234) that occurred in Seocho-gu, Seoul for three months from September to November 2019. Smartphone-assessed data were provided by Swallaby, Inc., the operator of the smartphone walking application, WalkOn. In this study, a methodology was developed to analyze healthy walking in each park. This method consists of three parts; to identify which walking activities pass through a certain park area; to classify healthy walking in terms of duration and intensity of walking; to visualize the spatial distribution of healthy walking in each park. This paper includes a series of studies on the following three topics: 1) Differences between walking in the parks and walking in other urban areas 2) Classification of parks which brings more/less healthy walking 3) Spatial visualization of service area and density in terms of healthy walking The results of this study present a better understanding of urban parks and healthy walking. First, it examines the empirical differences between healthy walking in parks and other urban areas. By grading the duration and the intensity of each walking activity, it could distinguish whether a walking meets the public health recommendation or not. The result has shown that only 4.48% of total walking activities were classified as healthy walking. Comparing walking activities in the parks and the others elsewhere, the parks have a higher rate of healthy walking (10.89%) than non-park areas have (3.80%). It suggests a consistent finding with previous studies that parks would bring more active walking than other urban areas. The second part of the study classifies the groups of parks depending on the level of healthy walking in each park. The level of healthy walking should be explained by two indicators, the frequency and the volume of healthy walking. When we call a park โ€˜healthier,โ€™ it might mean two different views: One is a park that had a higher tendency of healthy walking, and the other is a park that brought more bouts of healthy walking. Based on the frequency and total amount of healthy walking in each park, four groups of parks were derived by dividing the higher/lower parks by the level of healthy walking. For example, the result found thirty-five parks in Seocho-gu classified as the group that had a higher-frequency-larger-amount of healthy walking. This finding is meaningful in that most parks in the research area could be evaluated based on their quantifiable level of healthy walking. Third, it reveals the spatial distribution of healthy walking and the parks. The notion of โ€˜park service areaโ€™ is one of the major indicators in determining the accessibility of parks in the urban planning of Seoul city. However, it was different from the actual park service because it did not reflect the actual walking behavior, but was derived only from built environmental features. This study develops the service area by splitting each group which is categorized based on empirical walking activities. It unveils certain areas that cannot actually bring healthy walking well, even though they were previously considered park service areas. Another spatial analysis is possible to visualize the spatial density of walking around the park. Selecting four cases for each park group, the spatial kernel density of the walking routes shows the different patterns of healthy walking in the parks. It is possible to clarify in which areas healthy walking routes are mainly clustered. This finding would be useful for designing and planning parks healthier. Empirical research on healthy walking in urban parks is necessary for the activity-supportive built environments, in which urban planners, designers, and public health practitioners are interested. This study is meaningful in enhancing a better understanding of walking activities in urban parks in terms of health benefits. With a new method for park walking analysis using smartphone-based walking data, it is expected that further studies will continue the discussion of urban places that promote more physical activities.ํ˜„๋Œ€์ธ์˜ ์‹ ์ฒดํ™œ๋™ ๋ถ€์กฑ์ด ์„ธ๊ณ„์ ์ธ ๋ฌธ์ œ๋กœ ๋Œ€๋‘๋˜๋ฉด์„œ ๋งŽ์€ ์‚ฌ๋žŒ๋“ค์ด ์‰ฝ๊ฒŒ ์ ‘ํ•  ์ˆ˜ ์žˆ๋Š” ๊ฑท๊ธฐ๋ฅผ ํ™œ์šฉํ•œ ์‹ ์ฒดํ™œ๋™ ์ฆ์ง„ ์ „๋žต์ด ํ•ต์‹ฌ์ ์ธ ๋Œ€์•ˆ ์ค‘ ํ•˜๋‚˜๋กœ ์ œ๊ธฐ๋˜์–ด ์™”๋‹ค. ํ•˜์ง€๋งŒ ๊ฑท๊ธฐ๋ฅผ ํ†ตํ•ด ์‹ ์ฒด ๊ฑด๊ฐ•์— ๊ธ์ •์  ํšจ๊ณผ๋ฅผ ๊ฐ€์ ธ์˜ฌ ๋งŒํผ์˜ ์‹ ์ฒดํ™œ๋™ ์ˆ˜์ค€์„ ๋‹ฌ์„ฑํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ์ƒ๋‹นํ•œ ์‹œ๊ฐ„๊ณผ ๊ฐ•๋„(์„ธ๊ณ„๋ณด๊ฑด๊ธฐ๊ตฌ ๊ถŒ์žฅ ๊ธฐ์ค€์— ๋”ฐ๋ฅด๋ฉด, 1ํšŒ 10๋ถ„ ์ด์ƒ ์ค‘๊ฐ•๋„ ์ด์ƒ์˜ ์‹ ์ฒดํ™œ๋™)๊ฐ€ ์š”๊ตฌ๋˜๋Š”๋ฐ, ์ผ์ƒ์ ์ธ ๋ณดํ–‰ ๊ฐ€์šด๋ฐ ์ด ๊ธฐ์ค€์„ ์ถฉ์กฑํ•˜๋Š” โ€˜๊ฑด๊ฐ•๋ณดํ–‰โ€™์€ ๋งŽ์ง€ ์•Š๋‹ค. ํ•œํŽธ, ๊ณต์›์€ ๋„์‹œ ๋‚ด์—์„œ ์ด๋Ÿฌํ•œ ์–‘์งˆ์˜ ๋ณดํ–‰์ด ๊ฐ€๋Šฅํ•œ ๋„์‹œ ์žฅ์†Œ ์ค‘ ํ•˜๋‚˜๋กœ ์ฃผ๋ชฉ๋ฐ›๊ณ  ์žˆ๋‹ค. ๋„์‹œ๊ณต์›์„ ๋„์‹œ๊ณ„ํš์‹œ์„ค๋กœ ์ง€์ • ๋ฐ ๊ด€๋ฆฌํ•˜๊ณ  ์žˆ๋Š” ๊ณต๊ณต๋ถ€๋ฌธ์—์„œ๋Š” ๋„์‹œ ๋‚ด ๊ณต์› ๊ณต๊ธ‰์„ ํ†ตํ•œ ์ ‘๊ทผ์„ฑ ํ–ฅ์ƒ, ๊ณต์›์‹œ์„ค ๊ด€๋ฆฌ ๋ฐ ๊ฐœ์„ ์„ ํ†ตํ•œ ๋ณดํ–‰ํ™˜๊ฒฝ ์กฐ์„ฑ, ๊ณต์›์—์„œ์˜ ๊ฑท๊ธฐ ์บ ํŽ˜์ธ๊ณผ ๊ฐ™์€ ์ง€์—ญ์‚ฌํšŒ ๊ฑด๊ฐ•์ •์ฑ… ์šด์˜ ๋“ฑ ๋„์‹œ๊ฐ€ ๊ฐ–์ถ”๊ณ  ์žˆ๋Š” ๊ณต์› ์ž์›์„ ํ™œ์šฉํ•˜๋ ค๋Š” ๋…ธ๋ ฅ์ด ์ด์–ด์ ธ์™”๊ณ , ์ด๋Ÿฌํ•œ ๋…ธ๋ ฅ์ด ์‹œ๋ฏผ ๊ฑด๊ฐ•์— ๋ณด๋‹ค ํšจ๊ณผ์ ์œผ๋กœ ๊ธฐ์—ฌํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ๊ณต์›์—์„œ ์ผ์–ด๋‚˜๋Š” ์‹ ์ฒดํ™œ๋™์— ๋Œ€ํ•œ ์ •๋Ÿ‰์  ์ž๋ฃŒ์™€ ๊ทผ๊ฑฐ๊ฐ€ ํ•„์š”ํ•˜๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๋„์‹œ์ง€์—ญ์—์„œ ๊ด‘๋ฒ”์œ„ํ•˜๊ฒŒ ์ผ์–ด๋‚˜๋Š” ๋‹ค์ˆ˜์˜ ๋ณดํ–‰์„ ์ง‘๊ณ„ํ•˜๊ณ  ๊ณต์›๊ณผ ๊ฐ™์€ ํŠน์ • ์žฅ์†Œ์—์„œ ์ผ์–ด๋‚œ ๋ณดํ–‰์„ ๋”ฐ๋กœ ๊ตฌ๋ถ„ํ•˜๋Š”๋ฐ ๋ฐฉ๋ฒ•๋ก ์  ํ•œ๊ณ„๊ฐ€ ์กด์žฌํ•˜์—ฌ, ์ง€์—ญ ๋‚ด ๊ณต์›๋“ค์˜ ๋ณดํ–‰ ์–‘์ƒ์„ ์‹ค์ฆ์ ์œผ๋กœ ํŒŒ์•…ํ•˜๋Š”๋ฐ ์–ด๋ ค์›€์ด ์žˆ์—ˆ๋‹ค. ์šฐ์„ , ์„ค๋ฌธ ๋ฐฉ์‹์œผ๋กœ ์ด๋ฃจ์–ด์ง„ ์ „ํ†ต์ ์ธ ๋ณดํ–‰ ์กฐ์‚ฌ ๋ฐฉ๋ฒ•์€ ๋ณดํ–‰์‹œ๊ฐ„๊ณผ ๋ณดํ–‰๊ฐ•๋„๋ฅผ ์ •ํ™•ํžˆ ์‚ฐ์ถœํ•˜๊ธฐ ์–ด๋ ค์› ๋‹ค. ์ดํ›„ ๋ณดํ–‰ ์ธก์ •๊ธฐ๊ธฐ๋“ค์ด ๋“ฑ์žฅํ•˜๋ฉด์„œ ๊ณต์›์—์„œ์˜ ๋ณดํ–‰์— ๊ด€ํ•œ ์ •๋Ÿ‰์  ๋ถ„์„์ด ๊ฐ€๋Šฅํ•ด์กŒ์ง€๋งŒ ์ธก์ •์— ๋“œ๋Š” ๋น„์šฉ์ด ํฌ๊ณ  ์—ฐ๊ตฌ์˜ ๋Œ€์ƒ์ž์™€ ๋Œ€์ƒ ๋ฒ”์œ„๋ฅผ ํ™•์žฅํ•˜๋Š”๋ฐ ํ•œ๊ณ„๊ฐ€ ์žˆ์—ˆ๋‹ค. ํ•˜์ง€๋งŒ ์ตœ๊ทผ ์Šค๋งˆํŠธํฐ ๊ธฐ๋ฐ˜์˜ ์‹ ์ฒดํ™œ๋™ ์ธก์ • ๊ธฐ์ˆ ์ด ๋ฐœ์ „ํ•˜๋ฉด์„œ ๊ธฐ์กด ๋ณดํ–‰ ์ธก์ • ๊ณผ์ •์˜ ์‹œ๊ฐ„๊ณผ ๋น„์šฉ์„ ์ ˆ๊ฐํ•˜๊ณ  ๋ฐ์ดํ„ฐ์˜ ์–‘์  ํ•œ๊ณ„๋ฅผ ๊ทน๋ณตํ•  ์ˆ˜ ์žˆ๋Š” ๋Œ€์•ˆ์œผ๋กœ ๊ธฐ๋Œ€๋ฅผ ๋ชจ์œผ๊ณ  ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ๋ฐฐ๊ฒฝ์—์„œ ๋ณธ ์—ฐ๊ตฌ๋Š” ์Šค๋งˆํŠธํฐ ์‹ ์ฒดํ™œ๋™ ๋ฐ์ดํ„ฐ๋ฅผ ํ™œ์šฉํ•˜์—ฌ 10๋ถ„ ์ด์ƒ ์ค‘๊ฐ•๋„ ์ด์ƒ์˜ ๋ณดํ–‰, ์ด๋ฅธ๋ฐ” โ€˜๊ฑด๊ฐ•๋ณดํ–‰โ€™ ์ธก๋ฉด์—์„œ ๊ณต์›์—์„œ์˜ ๋ณดํ–‰ ํ™œ๋™๊ณผ ๋ณดํ–‰ ๊ณต๊ฐ„์„ ์žฌ์กฐ๋ช…ํ•œ๋‹ค. ์ด ์—ฐ๊ตฌ์˜ ๋ชฉํ‘œ๋Š” ์ฒซ์งธ, ์ „์ฒด ๋ณดํ–‰ ๊ฐ€์šด๋ฐ ๊ณต์›์—์„œ์˜ ๋ณดํ–‰๊ณผ ๊ทธ ์™ธ ์ง€์—ญ์—์„œ์˜ ๋ณดํ–‰์ด ๊ฑด๊ฐ•๋ณดํ–‰์—์„œ ์–ด๋– ํ•œ ์ฐจ์ด๋ฅผ ๋‚˜ํƒ€๋‚ด๋Š”์ง€ ์‚ดํŽด๋ณด๊ณ  ๋‘˜์งธ, ์ด๋กœ๋ถ€ํ„ฐ ์ง€์—ญ ๋‚ด ๊ณต์›๋“ค์„ ๊ฑด๊ฐ•๋ณดํ–‰ ์ธก๋ฉด์—์„œ ์œ ํ˜•ํ™”ํ•˜์—ฌ ๋ถ„๋ฅ˜ํ•˜๋ฉฐ ์…‹์งธ, ์ง€์—ญ ๋‚ด ๊ณต์› ์œ ํ˜•์˜ ๋ถ„ํฌ์™€ ๊ฑด๊ฐ•๋ณดํ–‰์˜ ๊ณต๊ฐ„์  ๋ถ„ํฌ ํŠน์„ฑ์„ ๋„์ถœํ•˜๋Š” ๋ฐ ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋ฅผ ์œ„ํ•ด 2019๋…„ 9์›”๋ถ€ํ„ฐ ๋™๋…„ 11์›”๊นŒ์ง€ 3๊ฐœ์›”๊ฐ„ ์„œ์šธ์‹œ ์„œ์ดˆ๊ตฌ์—์„œ ์ง‘๊ณ„๋œ ์Šค๋งˆํŠธํฐ ์‹ ์ฒดํ™œ๋™ ๋ฐ์ดํ„ฐ(n = 550,234)๋ฅผ ์Šค๋งˆํŠธํฐ ๊ฑท๊ธฐ ์–ดํ”Œ๋ฆฌ์ผ€์ด์…˜ ์›Œํฌ์˜จ์˜ ์šด์˜์‚ฌ ์Šค์™ˆ๋ผ๋น„(์ฃผ)๋กœ๋ถ€ํ„ฐ ์ œ๊ณต๋ฐ›์•„ ์„œ์šธ์‹œ ์„œ์ดˆ๊ตฌ ๊ด€ํ• ์˜ ๊ณต์›์‹œ์„ค(n = 130)์„ ๋Œ€์ƒ์ง€๋กœ ๋ถ„์„ํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ ๊ณผ์ •์—์„œ ์Šค๋งˆํŠธํฐ ์‹ ์ฒดํ™œ๋™ ๋ฐ์ดํ„ฐ๋กœ๋ถ€ํ„ฐ ๊ฐ ๋ณดํ–‰ ํ™œ๋™์˜ ์–‘์  ์ง€ํ‘œ๋ฅผ ์‚ฐ์ถœํ•˜๊ณ , ํŠน์ • ๊ณต์› ์˜์—ญ์„ ๊ฒฝ์œ ํ•œ ๋ณดํ–‰์„ ์‹๋ณ„ํ•˜์—ฌ, ๊ฐ ๊ณต์›์˜ ๊ฑด๊ฐ•๋ณดํ–‰์˜ ๋น„์œจ ๋ฐ ์ด๋Ÿ‰, ๊ณต์› ์„œ๋น„์Šค ์ง€์—ญ๊ณผ ๋ณดํ–‰๋ฐ€์ง‘๋„ ๋ถ„ํฌ๋ฅผ ๋„์ถœํ•  ์ˆ˜ ์žˆ๋Š” ๋ฐฉ๋ฒ•๋ก ์„ ๊ฐœ๋ฐœํ•˜์˜€๋‹ค. ์ด๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ์—ฐ๊ตฌ๋Š” ๋‹ค์Œ์˜ ์„ธ ๊ฐ€์ง€ ์งˆ๋ฌธ์„ ์ˆœ์ฐจ์ ์œผ๋กœ ํƒ๊ตฌํ•œ๋‹ค. 1) ๊ณต์›์—์„œ ์ผ์–ด๋‚œ ๋ณดํ–‰๊ณผ ๊ณต์› ์ด์™ธ ์ง€์—ญ์—์„œ ์ผ์–ด๋‚œ ๋ณดํ–‰์„ ๋น„๊ตํ•  ๋•Œ ๊ฑด๊ฐ•๋ณดํ–‰์˜ ์ธก๋ฉด์—์„œ ์ฐจ์ด๊ฐ€ ์กด์žฌํ•˜๋Š”๊ฐ€? 2) ๊ฐ ๊ณต์›๋ณ„ ๋ณดํ–‰์ง€ํ‘œ๋ฅผ ํ†ตํ•ด ๊ณต์›๋“ค์˜ ๊ฑด๊ฐ•๋ณดํ–‰ ์ˆ˜์ค€์„ ๋น„๊ตํ•˜์—ฌ ์œ ํ˜•์„ ๋ถ„๋ฅ˜ํ•  ์ˆ˜ ์žˆ๋Š”๊ฐ€? 3) ๊ฑด๊ฐ•๋ณดํ–‰์˜ ๊ณต๊ฐ„์  ๋ถ„ํฌ๋ฅผ ํ†ตํ•ด ๋ณธ ๊ถŒ์—ญ๋ณ„ ๊ณต์› ์„œ๋น„์Šค ์ง€์—ญ๊ณผ ๊ฐ ๊ณต์›๋ณ„ ๋ณดํ–‰๋ฐ€์ง‘์˜์—ญ์€ ์–ด๋– ํ•œ๊ฐ€? ๋ณธ ์—ฐ๊ตฌ์˜ ๊ฒฐ๊ณผ๋Š” ๊ณต์›์—์„œ ์ผ์–ด๋‚˜๋Š” ๋ณดํ–‰์˜ ์ง€ํ‘œ ํŠน์ง•, ๊ฑด๊ฐ•๋ณดํ–‰์„ ๊ธฐ์ค€์œผ๋กœ ํ•œ ๊ณต์› ์œ ํ˜•, ๊ทธ๋ฆฌ๊ณ  ๊ณต์›๋ณดํ–‰์˜ ๊ณต๊ฐ„์  ๋ถ„ํฌ์— ๋Œ€ํ•ด ์ƒˆ๋กœ์šด ์‹œ๊ฐ์„ ์ œ์‹œํ•œ๋‹ค. ๋„์‹œ์—์„œ ์ผ์–ด๋‚œ ๋ณดํ–‰ ๊ฐ€์šด๋ฐ ๋ณดํ–‰์‹œ๊ฐ„๊ณผ ๋ณดํ–‰๊ฐ•๋„์˜ ์กฐ๊ฑด์„ ์ถฉ์กฑ์‹œํ‚ค๋Š” ๊ฑด๊ฐ•๋ณดํ–‰์˜ ์–‘๊ณผ ๋ถ„ํฌ์— ๋Œ€ํ•ด ์ •๋Ÿ‰์ ์œผ๋กœ ๋ถ„์„ํ•˜๋ฉฐ, ๊ณต์›์—์„œ ๊ฑด๊ฐ•๋ณดํ–‰์˜ ์ˆ˜์ค€์„ ๊ฐ€๋Š ํ•  ์ˆ˜ ์žˆ๋„๋ก ๊ณต์›์˜ ์œ ํ˜•์„ ๋‚˜๋ˆ„๊ณ , ์ƒํ™œ๊ถŒ์—ญ๋ณ„ ๊ณต์›์†Œ์™ธ์ง€์—ญ์„ ๋„์ถœํ•˜๊ณ  ๊ฐœ๋ณ„ ๊ณต์›์—์„œ์˜ ๋ณดํ–‰๋ฐ€์ง‘์˜์—ญ์„ ํƒ๊ตฌํ•˜๋Š” ์ผ๋ จ์˜ ๊ณผ์ •์€ ๊ณต์›๋ณดํ–‰ ์—ฐ๊ตฌ์˜ ์ƒˆ๋กœ์šด ๋ฐฉ๋ฒ•๋ก ๊ณผ ์ ์šฉ์‚ฌ๋ก€๋ฅผ ์ œ๊ณตํ•œ๋‹ค. ์ฃผ์š” ์—ฐ๊ตฌ๊ฒฐ๊ณผ๋ฅผ ์„ธ ๋‹จ๊ณ„๋กœ ์š”์•ฝํ•˜๋ฉด ์ฒซ์งธ, ์ „์ฒด ๋ณดํ–‰ ๊ฐ€์šด๋ฐ ๊ฑด๊ฐ•๋ณดํ–‰์˜ ๋น„์œจ์€ ์•ฝ 4.48%๋กœ ๋‚˜ํƒ€๋‚ฌ๋Š”๋ฐ, ๊ณต์›์˜ ๋ณดํ–‰ ๊ฐ€์šด๋ฐ ๊ฑด๊ฐ•๋ณดํ–‰์€ 10.89%, ๊ณต์›์ด ์•„๋‹Œ ์ง€์—ญ์˜ ๋ณดํ–‰ ๊ฐ€์šด๋ฐ ๊ฑด๊ฐ•๋ณดํ–‰์€ ์•ฝ 3.80%๋กœ ๋ณดํ–‰์˜ ๊ฒฝ๋กœ๊ฐ€ ๊ณต์›์„ ๊ฒฝ์œ ํ•˜๋Š”์ง€ ์•„๋‹Œ์ง€์— ๋”ฐ๋ผ ๊ฑด๊ฐ•๋ณดํ–‰ ๋น„์œจ์˜ ์ฐจ์ด๊ฐ€ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ด๋Š” ๋„์‹œ๊ณต๊ฐ„์—์„œ ๊ณต์›์ด 10๋ถ„ ์ด์ƒ์˜ ์ค‘์žฅ์‹œ๊ฐ„, ๋น ๋ฅธ ์†๋„๋กœ ๊ฑธ์„ ์ˆ˜ ์žˆ๋Š” ์žฅ์†Œ๋ฅผ ์ œ๊ณตํ•œ๋‹ค๋Š” ๊ธฐ์กด ์—ฐ๊ตฌ๋“ค๊ณผ ์ผ์น˜ํ•˜๋Š” ๋ถ€๋ถ„์ด๋‹ค. ๋‘˜์งธ, ๊ฐ ๊ณต์›์—์„œ ๋‚˜ํƒ€๋‚˜๋Š” ๊ฑด๊ฐ•๋ณดํ–‰์˜ ๋นˆ๋„์™€ ์ด๋Ÿ‰์„ ๊ธฐ์ค€์œผ๋กœ ์ƒ์œ„๊ถŒ/ํ•˜์œ„๊ถŒ ๊ณต์›์„ ๊ตฌ๋ถ„ํ•˜์—ฌ ๋„ค ๊ฐ€์ง€ ๊ณต์› ์œ ํ˜•์„ ๋„์ถœํ•˜์˜€๋‹ค. ๊ณต์›๋ณดํ–‰ ๊ฐ€์šด๋ฐ ๊ฑด๊ฐ•๋ณดํ–‰์ด ์ฐจ์ง€ํ•˜๋Š” ๋น„์œจ๊ณผ ๊ฑด๊ฐ•๋ณดํ–‰์„ ํ†ตํ•ด ์–ป์„ ์ˆ˜ ์žˆ์—ˆ๋˜ ์ด ์—๋„ˆ์ง€ ์†Œ๋น„๋Ÿ‰์ด ๊ธฐ์ค€์ด ๋˜์—ˆ๋‹ค. ๊ณ ๋นˆ๋„-๊ณ ์ด๋Ÿ‰, ์ €๋นˆ๋„-๊ณ ์ด๋Ÿ‰, ๊ณ ๋นˆ๋„-์ €์ด๋Ÿ‰, ์ €๋นˆ๋„-์ €์ด๋Ÿ‰ ์œ ํ˜•์œผ๋กœ ๊ณต์›์„ ๋‚˜๋ˆ„๊ณ , ๊ฐ ๊ณต์›์˜ ๋ณดํ–‰์ง€ํ‘œ๋ฅผ ์‚ฐ์ถœํ•˜์—ฌ ์„œ์ดˆ๊ตฌ์˜ ๊ณต์›๋ณ„ ๋ณดํ–‰์ˆ˜์ค€์„ ๋น„๊ตํ•˜์˜€๋‹ค. ์ด๋Š” ๊ธฐ์กด ์—ฐ๊ตฌ์— ๋น„ํ•ด ์„œ์ดˆ๊ตฌ ๋‚ด ๊ณต์› ๋‹ค์ˆ˜๋ฅผ ๋Œ€์ƒ์œผ๋กœ ํ‘œ๋ณธ์˜ ํฌ๊ธฐ๋ฅผ ํ™•๋Œ€ํ•˜์˜€๊ณ , ๊ฑด๊ฐ•๋ณดํ–‰์„ ์ •๋Ÿ‰์ ์œผ๋กœ ์‚ฐ์ถœํ•˜์—ฌ ์ƒ์œ„๊ถŒ๊ณผ ํ•˜์œ„๊ถŒ ๊ณต์›์„ ๊ตฌ๋ถ„ํ•˜์˜€๋‹ค๋Š”๋ฐ ์˜๋ฏธ๊ฐ€ ์žˆ๋‹ค. ์…‹์งธ, ๋Œ€์ƒ์ง€์—์„œ ์œ ํ˜•๋ณ„ ๊ณต์›์˜ ๋„์‹œ๊ณต๊ฐ„์  ๋ถ„ํฌ๋ฅผ ํ†ตํ•ด ๊ณต์› ์„œ๋น„์Šค ์†Œ์™ธ์ง€์—ญ์„ ๋„์ถœํ•˜๊ณ , ์œ ํ˜•๋ณ„ ๋Œ€ํ‘œ ๊ณต์› ์‚ฌ๋ก€์ง€๋ฅผ ์„ ์ •ํ•˜์—ฌ ๊ฐœ๋ณ„ ๊ณต์›์—์„œ ๋ณดํ–‰๊ฒฝ๋กœ์˜ ๋ถ„ํฌ๋ฅผ ์‹œ๊ฐํ™”ํ•˜์˜€๋‹ค. ๊ธฐ์กด ๊ณต์›๋…น์ง€ ๊ธฐ๋ณธ๊ณ„ํš์—์„œ ํ™œ์šฉ๋˜๋Š” ๊ณต์› ์„œ๋น„์Šค ์†Œ์™ธ์ง€์—ญ ๊ฐœ๋…์„ ๋ฐ”ํƒ•์œผ๋กœ ๊ฑด๊ฐ•๋ณดํ–‰ ๊ด€์ ์˜ ๊ณต์›์†Œ์™ธ์ง€์—ญ์„ ์„œ์ดˆ๊ตฌ ์ƒํ™œ๊ถŒ์—ญ๋ณ„๋กœ ์ƒˆ๋กญ๊ฒŒ ๋„์ถœํ•œ ์—ฐ๊ตฌ๊ฒฐ๊ณผ๋Š”, ์„ ํ–‰์—ฐ๊ตฌ๋“ค์—์„œ ๊ณต์› ์„œ๋น„์Šค๊ฐ€ ์–‘ํ˜ธํ•˜๋‹ค๊ณ  ์—ฌ๊ฒจ์กŒ๋˜ ์ง€์—ญ์ผ์ง€๋ผ๋„ ๊ฑด๊ฐ•๋ณดํ–‰์ด ํ™œ๋ฐœํžˆ ์ผ์–ด๋‚˜๋Š” ๊ณต์›์„ ์ด์šฉํ•˜๊ธฐ๋Š” ์–ด๋ ค์šธ ์ˆ˜ ์žˆ๋‹ค๋Š” ์‚ฌ์‹ค์„ ๋“œ๋Ÿฌ๋‚ด๊ณ  ์žˆ๋‹ค. ๋‚˜์•„๊ฐ€ ๋„ค ๊ฐ€์ง€ ๊ณต์› ์œ ํ˜•๋ณ„๋กœ ๋Œ€ํ‘œ์ ์ธ ์‚ฌ๋ก€์ง€๋ฅผ ์„ ์ •ํ•˜์—ฌ ๋ณดํ–‰ ๊ฒฝ๋กœ์˜ ๊ณต๊ฐ„์  ๋ฐ€์ง‘ ๋ถ„ํฌ๋ฅผ ๋„์ถœํ•˜์—ฌ ๊ณต์› ๋‚ด์™ธ์˜ ๋ณดํ–‰๋ฐ€์ง‘์˜์—ญ์„ ์‹œ๊ฐํ™”ํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์ด๋ฅผ ํ†ตํ•ด ๊ณต์›์—์„œ ๋ณดํ–‰์ด ํ™œ๋ฐœํžˆ ์ผ์–ด๋‚˜๋Š” ๊ณต๊ฐ„์„ ์ฐพ๊ณ  ๊ฑด๊ฐ•๋ณดํ–‰์˜ ๊ณต๊ฐ„์  ๋ฐ€์ง‘ ์–‘์ƒ์„ ์‚ดํŽด๋ณผ ์ˆ˜ ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ๊ฒฐ๊ณผ๋Š” ์‹ ์ฒดํ™œ๋™ ์ฆ์ง„์˜ ์ธก๋ฉด์—์„œ ๊ณต์›๋ณดํ–‰์— ๋Œ€ํ•œ ์‹ค์ฆ์  ์ดํ•ด๋ฅผ ๋†’์ด๊ณ , ์Šค๋งˆํŠธํฐ ๋ณดํ–‰ ๋ฐ์ดํ„ฐ๋ฅผ ํ™œ์šฉํ•œ ๊ณต์›๋ณดํ–‰ ๋ถ„์„ ๋ฐฉ๋ฒ•๋ก ์„ ์ˆ˜๋ฆฝํ•˜๋ฉฐ, ์ด๋ฅผ ์‹ค์ œ ๋Œ€์ƒ์ง€์— ์ ์šฉํ•œ ๊ฒฐ๊ณผ ์‚ฌ๋ก€๋ฅผ ํ†ตํ•ด ๊ธฐ์กด ๊ณต์› ๊ณ„ํš ๋ฐ ์„ค๊ณ„ ์ธก๋ฉด์—์„œ ๊ฑด๊ฐ•ํ•œ ๋ณดํ–‰์„ ์ฆ์ง„ํ•˜๋Š” ๊ณต์›์— ๋Œ€ํ•œ ๋…ผ์˜๋ฅผ ์ด์–ด๊ฐ€๋Š”๋ฐ ์˜์˜๊ฐ€ ์žˆ๋‹ค. ์Šค๋งˆํŠธํฐ ๋ณดํ–‰ ๋ฐ์ดํ„ฐ๋ฅผ ์ด์šฉํ•˜์—ฌ ๋„์‹œ๋ณดํ–‰์˜ ์ƒˆ๋กœ์šด ์—ฐ๊ตฌ๋ฐฉ๋ฒ•๋ก ์„ ๋ชจ์ƒ‰ํ•˜๊ณ , ์ด๋ฅผ ํ†ตํ•ด ์ฐพ์•„๋‚ธ ๊ณต์› ๋ณดํ–‰์— ๊ด€ํ•œ ๊ฒฐ๊ณผ๋“ค์ด ์‹ ์ฒดํ™œ๋™ ์ฆ์ง„์„ ์œ„ํ•œ ๋„์‹œ ํ™˜๊ฒฝ์„ ๊ตฌ์ถ•ํ•˜๋Š”๋ฐ ๋ณดํƒฌ์ด ๋˜๊ธฐ๋ฅผ ๊ธฐ๋Œ€ํ•œ๋‹ค.์ œ 1 ์žฅ ์„œ ๋ก  1 1.1. ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ 1 1.2. ์—ฐ๊ตฌ์˜ ๋ชฉ์  7 1.3. ์—ฐ๊ตฌ์˜ ๋Œ€์ƒ ๋ฐ ๋ฒ”์œ„ 9 1.4. ๋…ผ๋ฌธ์˜ ๊ตฌ์„ฑ 12 ์ œ 2 ์žฅ ์ด๋ก ์  ๋ฐฐ๊ฒฝ ๋ฐ ์„ ํ–‰์—ฐ๊ตฌ ๊ณ ์ฐฐ 16 2.1. ๊ฑด๊ฐ•ํ•œ ๊ฑท๊ธฐ ํ™œ๋™ 16 2.2. ์‹ ์ฒดํ™œ๋™ ์ฆ์ง„๊ณผ ๊ณต์› ๋ณดํ–‰ ์—ฐ๊ตฌ 23 2.3. ๋ณดํ–‰ ํ™œ๋™ ์ธก์ • ๊ธฐ์ˆ ์˜ ๋ฐœ์ „ 29 ์ œ 3 ์žฅ ๋ถ„์„์˜ ํ‹€ 34 3.1. ์ž๋ฃŒ์˜ ์ค€๋น„ 34 3.2. ๋Œ€์ƒ์ง€ ์„ค๋ช… 35 3.3. ์ž๋ฃŒ ์ˆ˜์ง‘ ๋ฐ ์ „์ฒ˜๋ฆฌ 45 3.4. ๊ณต์›๋ณดํ–‰ ๋ถ„์„์„ ์œ„ํ•œ ํ‹€ 51 3.5. ์šฉ์–ด ์ •๋ฆฌ ๋ฐ ์กฐ์ž‘์  ์ •์˜ 58 ์ œ 4 ์žฅ ์—ฐ๊ตฌ ๊ฒฐ๊ณผ 60 4.1. ์ „์ฒด ๋ณดํ–‰, ๊ฑด๊ฐ•๋ณดํ–‰, ๊ณต์›๋ณดํ–‰ 60 4.2. ๊ฑด๊ฐ•๋ณดํ–‰ ์–‘์ƒ์œผ๋กœ ๋ณธ ๊ณต์› ์œ ํ˜•ํ™” 68 4.3. ๊ณต์› ์œ ํ˜•๊ณผ ๊ฑด๊ฐ•๋ณดํ–‰์˜ ๊ณต๊ฐ„์  ๋ถ„ํฌ 82 ์ œ 5 ์žฅ ํ•ด์„ ๋ฐ ๋…ผ์˜ 110 5.1. ๊ฑด๊ฐ•๋ณดํ–‰: ์‹ ์ฒด๊ฑด๊ฐ•์— ์œ ์˜๋ฏธํ•œ ๋ณดํ–‰ ์„ธ๋ถ„ํ™” 110 5.2. ๊ณต์› ์œ ํ˜•: ๊ณต์›๋งˆ๋‹ค ๋‹ค๋ฅด๊ฒŒ ๋‚˜ํƒ€๋‚˜๋Š” ๊ฑด๊ฐ•๋ณดํ–‰ 113 5.3. ๊ณต๊ฐ„์  ๋ถ„ํฌ: ๊ณต์› ์„œ๋น„์Šค ์ง€์—ญ๊ณผ ๋ณดํ–‰๋ฐ€์ง‘์˜์—ญ 116 5.4. ์Šค๋งˆํŠธํฐ ๋ณดํ–‰ ๋ฐ์ดํ„ฐ์™€ ๋„์‹œ์žฅ์†Œ-๋ณดํ–‰ ์—ฐ๊ตฌ ๋ฐฉ๋ฒ•๋ก  120 ์ œ 6 ์žฅ ๊ฒฐ ๋ก  123๋ฐ•
    • โ€ฆ
    corecore