• Journal of the Korea Convergence Society
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• v.9 no.3
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• pp.181-186
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• 2018

Car-sharing services are one of the successful model of sharing economies. The car-sharing service is a kind of car rental service that can pay the cost per minutes, also can makes it easy to book and return by smart phone at any time. Experts predict that the car-sharing services will likely dominate the auto market in the future by reducing the burden on consumers' purchasing cost of car and resolving the environmental issues caused by the vehicle. Therefore, a differentiated service strategy is needed to establish a competitive caching service among these companies. In this study, we surveyed differentiation cases by comparing and analyzing domestic caching companies, And customized service tailored to the situation and effective vehicle type infotainment. As a result of this study, the proposed service method change, customized service provision, and new platform application are expected to be detailed and in depth.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1993.04a
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• pp.483-492
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• 1993

미래의 차의 선택사항이 될 여행정보시스템을 설치하여 그 성능의 인간공학적 조사를 수행하였다. 이 시스템을 실제로 GM자동차 Oldsmobile Toronado형 100대에 설치하여 사용자가 얼마나 쉽게, 빨리 그리고 안락하게 갈 수 있고, 어떻게 하면 좀 더 이것을 인간공학적으로 향상시킬수 있는 가를 연구하였다. 100대중 23대는 대학교에서 연구를 하고 나머지는 AAA회사의 회원권을 가진 지역사용자들에게 AAA회사와 AVIS회사를 통하여 대여를 하여 컴퓨터차(TravTek시스템)의 성능과 효율성을 시험하였다. 그 내용을 살펴보면, 차에 부착된 컴퓨터의 컬러화면으로 사용자에게 도로를 보여주고, 컴퓨터가 최단거리를 제시하여 운전자에게 컴퓨터음성과 화면으로 길을 안내한다. 그 파급효과로 도로 체증현상을 막고, 기름의 낭비도 절약하고, 밤에도 안전하게 운행할 수 있게 할 뿐만아니라 처음보는 거리라고 하더라도 컴퓨터가 안내하면서 목적지까지 무사히 도착할 수 있게 하여 준다. 이러한 시스템을 설치한 차를 타고 여행할 때, 여행자가 과연 얼마나 안락하게 여행할 수 있으며, 도로의 체증현상을 줄이고, 사고를 예방하며, 차의 설계와 목적이 인간공학적으로 합당한 가를 알아보고자 하는 연구이다. 인간공학적 평가 인자들은 (1) 운전자의 수행도, (2) 사용자 선호도, (3) 사용자 인식, (4) 운행정보등이다. 그리고 컴퓨터음성을 사용하였을 때와 사용하지 않았을 때의 두가지 경우와 (1) 움직이는 컴퓨터지도를 사용하였을 때 (2) 단순화한 도로안내를 사용하였을 때, (3) 컴퓨터 지도를 사용하지 않았을 경우(종이지도사용)에 관해 위의 4가지 인간공학적 인자들을 평가하고자 한다. 이 연구는 아직도 진행중이라 발표하고자 하는 논문역시 현재까지의 연구결과를 토대로 발표하는 것이므로 완전한 결론을 내릴 수는 없고, 진행과 정의내용과 토의사항과 잠정적인 결론을 제시하고자 한다. 의거한 작업순서 결정을 위해 우선 BB의 상한을 구하는 연구를 행했다. 이를 위해 우선 단일작업장에서 야기될 수 있는 모든 상황을 고려한 최적 작업순서 결정규칙을 연구했으며, 이의 증명을 위해 이 규칙에 의거했을 때의 보완작업량이 최소가 된다는 것을 밝혔다. 보완작업 계산의 효율성을 제고하기 위해 과부하(violation)개념을 도입하였으며, 작업유형이 증가된 상황에서도 과부하 개념이 보완작업량을 충분히 반영할 수 있음을 밝혔다. 본 연구에서 제시한 최적 작업순서 규칙에 의거했을 때 야기될 수 있는 여러가지 경우의 과부하를 모두 계산했다. 앞에서 개발된 단일작업량의 최적 작업순서 결정규칙을 이용하여 다작업장의 문제를 실험했다. 이 문제는 규모가 매우 크므로 Branch & Bound를 이용하였으며, 각 가지에서 과부하량이 최적인 경우만을 고려하는 휴리스틱을 택하여 실험자료를 이용하여 여러 회 반복실험을 행했다. 그리고 본 연구의 성과를 측정하기 위해 휴리스틱 기법시 소요되는 평균 CPU time 범위에서, 랜덤 작업순서에 따른 작업할당을 반복실험하여 이중 가장 좋은 해와 비교했다. 그러나 앞으로 다작업장 문제를 다룰 때, 각 작업장 작업순서들의 상관관계를 고려하여 보다 개선된 해를 구하기 위한 연구가 요구된다. 또한, 준비작업비용을 발생시키는 작업장의 작업순서결정에 대해서도 연구를 행하여, 보완작업비용과 준비비용을 고려한 GMMAL 작업순서문제를 해결하기 위한 연구가 수행되어야 할 것이다.로 이루어 져야 할 것이다.태를 보다 효율적으로 증진시킬 수 있는 대안이 마련되어져야 한다고 사료된다.$\ulcorner$순응$\lrcorner$의 범위를 벗어나지 않는다. 그렇기 때문에도 $\ulcorner$순응$\lrcorner$과 $\ulcorner$표현$\lrcorner$

• Journal of Digital Convergence
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• v.15 no.3
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• pp.77-86
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• 2017

This study selected car-related mobile apps for app developers suffering from low revenue and classified car apps assisting users in driving or managing a car. A total of 697 car apps were classified into eight categories. Most apps are in four categories: car news & information (28%), locating service (23%), car rental service (15%), safe/efficient driving service (12%). The remaining categories are buying & selling, driver's communication, maintenance management, and expenses monitoring. Many apps are simple and too similar in their main functions. Only a few apps are designed to be more comprehensive and have functions in two or more categories. For the practicality of the categorization scheme, this study checked the inter-rater reliability in two tests and got 0.886 and 0.828. The result from this study suggests functions that are not implemented yet or need to be combined. Future research will focus on identifying promising car apps or designing multi-functional car apps.

• Journal of Broadcast Engineering
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• v.27 no.2
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• pp.228-239
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• 2022

In this paper, we propose an unmanned vehicle scratch detection deep learning model for car sharing services. Conventional scratch detection models consist of two steps: 1) a deep learning module for scratch detection of images before and after rental, 2) a manual matching process for finding newly generated scratches. In order to build a fully automatic scratch detection model, we propose a one-step unmanned scratch detection deep learning model. The proposed model is implemented by applying transfer learning and fine-tuning to the deep learning model that detects changes in satellite images. In the proposed car sharing service, specular reflection greatly affects the scratch detection performance since the brightness of the gloss-treated automobile surface is anisotropic and a non-expert user takes a picture with a general camera. In order to reduce detection errors caused by specular reflected light, we propose a preprocessing process for removing specular reflection components. For data taken by mobile phone cameras, the proposed system can provide high matching performance subjectively and objectively. The scores for change detection metrics such as precision, recall, F1, and kappa are 67.90%, 74.56%, 71.08%, and 70.18%, respectively.

• Information Systems Review
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• v.22 no.1
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• pp.147-165
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• 2020

The sharing economy service is now spreading in various fields such as accommodation, cars and bicycles. In particular, bicycle-sharing service have become very popular around the world, and since September 2015, Seoul has been providing a bicycle-sharing service called 'Ttareungi'. However, the number of bicycles is unbalanced among rental stations continuously according to the user's bicycle use. In order to solve these problems, we employed social network analysis using Ttareungi data in Seoul, Korea. We analyzed degree centrality, closeness centrality, betweenness centrality and k-core. As a result, the degree centrality was found to be closely linked with bus or subway transfer center. Closeness centrality was found to be in an unbalanced departure and arrival frequency or poor public transport proximity. Betweenness centrality means where the frequency of departure and arrival occurs frequently. Finally, the k-core analysis showed that Mapo-gu was the most important group by time zone. Therefore, the results of this study may contribute to the planning of relocation and additional installation of bike rental station in Seoul.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.19 no.1
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• pp.93-105
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• 2024

Car-sharing is a representative model of the sharing economy, and it is a service that rents or uses a car for the necessary time without owning a car. This industry is growing due to various factors such as technological advances, increasing awareness of environmental protection, and increasing demand for solving traffic congestion problems in cities. Accordingly, there is a need for a strategic approach for companies providing car-sharing services to respond quickly to market changes in order to expand market share and differentiate services. Accordingly, this study conducted a case study on open innovation activities between Gongcar and existing rental car companies, focusing on the research question "What effects do open innovation activities between car-sharing companies and existing rental car companies cause?" As a result of the study, it was confirmed that Gongcar have (1) the ability to actively respond to market fluctuations by establishing a flexible vehicle supply chain based on demand, (2) have significantly reduced growth capital expenditure (Growth Capex), and both cafe and rental car companies have (3) performed successful open innovation by improving key KPI indicators and recording financial performance. This study reveals how open innovation acts as a key business growth engine in the car-sharing industry, and its significance is found in that it empirically confirmed the successful implementation conditions of open innovation based on resource dependence theory.