• Journal of Korean Society of Transportation
• /
• v.28 no.3
• /
• pp.7-17
• /
• 2010

Bicycle is one of the most important eco-friendly transport mode which can cope with global warming. ICTs(information and communication technologies) on bicycles became a dominant factor for success to the spread of bicycles to public as we experienced Public Bike System. In this paper, conceptualization and classification of u-Bike services are fulfilled and its priority was examined using AHP method. Group capabilities technique of ECII, a computer fool of AHP was invited to minimize bias on appraisal. 12 u-Bike services were conceptualized. U-service with highest adoptability was 'bike and ride' service which can link bicycle to public transportation. 'Prevention system from abandonment and theft' and 'public bike system' similar to Velib system in Paris are also considered to be very important services in u-City.

• Journal of Korean Society of Transportation
• /
• v.34 no.5
• /
• pp.394-408
• /
• 2016

This study aims to study the impact of weather conditions and locational characteristics of bike stations on the usage of public bike sharing system for efficient deployment and operation of public bike systems. Linear regression analysis is used to estimate the usage of public bikes of Goyang city. The statistical analysis shows that the usage rate increases with average temperature and decreases under high wind (over 7m/s) or high temperature (over $29^{\circ}$) condition. The usage rate of public bike sharing system can be differentiated by locational characteristics of bike station such as residential area, commercial area, park, school, and metro station. The usage rate increases in park and commercial areas from 10 AM to 3 PM, while it increases in school areas from 3 PM to 5 PM. Public bikes are highly used near the metro station from 5 PM to 8 PM. The stations in parks are highly used in late night, and the usage rate in CBD area increases after the midnight.

• Transportation Technology and Policy
• /
• v.7 no.3
• /
• pp.49-58
• /
• 2010

• International Journal of Highway Engineering
• /
• v.16 no.1
• /
• pp.63-73
• /
• 2014

PURPOSES : The aim of this study is to analyze economic effect of bike-sharing after its introduction in Korea. METHODS : This study reviews current bike-sharing situations in Korea and other nations. We conduct surveys on bike-sharing system's bike usage patterns and economic benefits in Changwon and Goyang cities where public bikes are the most popular in the nation. Economic benefits are itemized after reviewing relevant previous studies. Then, the survey is implemented using the Contingent Valuation Method (CVM). Then estimated benefit is compared to the cost which is necessary for bike-sharing introduction and operation. RESULTS : Using the average WTP per household, the total economic benefit of bike-sharing is estimated as much as 1.75 billion KRW to 3.75 billion KRW in Goyang and Changwon city. Using estimated benefit, economic effect of bike-sharing are calculated as 0.69 and 1.00, respectively. CONCLUSIONS : The result of this study shows bike-sharing could be useful economic policy in Korea. However, economic effect of bike-sharing differs by city.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.41 no.1
• /
• pp.41-53
• /
• 2016

Static repositioning is a well-known and commonly used strategy to maximize customer satisfaction in public bike-sharing systems. Repositioning is performed by trucks at night when no customers are in the system. In models that represent the static repositioning problem, the decision variables are truck routes and the number of bikes to pick up and deliver at each rental station. To simplify the problem, the decision on the number of bikes to pick up and deliver is implicitly included in the truck routes. Two relocation-based local search algorithms (1-relocate and 2-relocate) with the best-accept strategy are incorporated into a variable neighborhood search (VNS) to obtain high-quality solutions for the problem. The performances of the VNS algorithm with the effect of local search algorithms and shaking strength are evaluated with data on Tashu public bike-sharing system operating in Daejeon, Korea. Experiments show that VNS based on the sequential execution of two local search algorithms generates good, reliable solutions.

• Science of Emotion and Sensibility
• /
• v.14 no.1
• /
• pp.7-16
• /
• 2011

This is a study on operation system's application method for leading-in public bike operation system through researching on case study at home and abroad, and situation research in Naju city. In the 1st research, studied about the main problems showed at application cases of public bike system at domestic and overseas, such as using time, danger for be stolen and damaged. 2nd research focuses on necessarily of leading-in operation system to be used easily by city residents and travelers of Naju City which is the scheduled city for leading-in public bike system. 3rd research is on the basis of the result showed in 1st and 2nd research, supplied some problems' solving method for France Veblib System, such as lending and rental process using mobile, system operation process, communication process. Also, supplied the application method for problems showed at 1st and 2nd research and civil service proposal. The leading-in method study on public bike operation system was done through above research, also case study at home and abroad, situation study, and rental program module development, and this operation system is worked as an model operation system in Naju City. The future study of leading-in operation system will be more effective by means of summing up test running result.

• The Journal of the Korea Contents Association
• /
• v.21 no.7
• /
• pp.259-270
• /
• 2021

Recently, interests in the use of Sharing Bike is increasing in consideration of eco-friendly transportation and safety from viruses. As the technology for collecting and storing data is improved with the development of ICTs, research on mobility using the Sharing Bike Data is also actively progressing. Therefore, this paper analyzes the properties of Sharing Bike Data and cases of researches on it through literature review, and based on the results of the review, data of Eoulling, the Sharing Bike System of Sejong City is analyzed as a way to utilize Sharing Bike Data. Most of the selected literature used structured data, and analyzed it through statistical methods or data mining. Through data analysis, it identified the current status, found out problems of the Sharing Bike System, proposed a solution to solve them, developed plans to activate the use of Sharing Bike. This provides basic data for efficient management and operation plans for Sharing Bike System. Ultimately, it will be possible to explore ways to improve mobility in urban spaces by utilizing Sharing Bike Data.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.14 no.3
• /
• pp.1011-1015
• /
• 2013

The demand of bike increases eco-friendly as the mean of transportation but domestic production basis becomes sluggish. In this study, the design analysis of horizontal and vertical frame is performed on the model which is proposed as the bike of new concept in conjunction with public transport system. As the result, the structural analysis is conducted on the main frame of urban bike used with cast magnesium alloy. As the vertical load of 150 kg is applied, the design technology insures that maximum stress less than 70 MPa is obtained.

• Transportation Technology and Policy
• /
• v.7 no.3
• /
• pp.59-70
• /
• 2010

• The Journal of the Convergence on Culture Technology
• /
• v.8 no.6
• /
• pp.861-866
• /
• 2022

Seoul introduced the shared bicycle system, "Seoul Public Bike" in 2015 to help reduce traffic volume and air pollution. Hence, to solve various problems according to the supply and demand of the shared bicycle system, "Seoul Public Bike," several studies are being conducted. Most of the research is a strategic "Bicycle Rearrangement" in regard to the imbalance between supply and demand. Moreover, most of these studies predict demand by grouping features such as weather or season. In previous studies, demand was predicted by time-series-analysis. However, recently, studies that predict demand using deep learning or machine learning are emerging. In this paper, we can show that demand prediction can be made a little better by discovering new features or ordering the importance of various features based on well-known feature-patterns. In this study, by ordering the selection of new features or the importance of the features, a better coefficient of determination can be obtained even if the well-known deep learning or machine learning or time-series-analysis is exploited as it is. Therefore, we could be a better one for demand prediction.