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

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

• ETRI Journal
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• v.44 no.2
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• pp.244-259
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• 2022

Bicycle- or bike-sharing systems (BSSs) have received increasing attention as a secondary transportation mode due to their advantages, for example, accessibility, prevention of air pollution, and health promotion. However, in BSSs, due to bias in bike demands, the bike rebalancing problem should be solved. Various methods have been proposed to solve this problem; however, it is difficult to apply such methods to small cities because bike demand is sparse, and there are many practical issues to solve. Thus, we propose a demand prediction model using multiple classifiers, time grouping, categorization, weather analysis, and station correlation information. In addition, we analyze real-world relocation data by relocation managers and propose a relocation algorithm based on the analytical results to solve the bike rebalancing problem. The proposed system is compared experimentally with the results obtained by the real relocation managers.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.3
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• pp.353-362
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• 2023

In this research, the usage status and characteristics of user big data of Mobike, a dockless bike sharing service introduced in Suwon city, were analyzed, and multiple regression analysis was performed to identify factors influencing the demand for dockless bike sharing service. For analysis, usage data of bike sharing system in Suwon city in 2019 were obtained, and they were organized by areas. As a result of analyzing the characteristics of the influencing factors selected for each area, it was found that the extension of bicycle roads shows high in areas with high demand for bicycles or adjacent areas. Also, the population of 10-30's shows high in areas with high demand for bicycles or adjacent areas. In addition, it was analyzed that the use of bike sharing system is high in areas with high maintenance rate of bicycle roads and large-scale residential and commercial facilities near residential districts and adjacent areas. As a result of the multiple regression analysis, it is analyzed that length of bicycle·pedestrian roads (non-separated), population of 10-30's, number of railway stations, number of schools, number of commercial facilities, number of industrial facilities factors were significant. It is expected that it may be possible to create an environment in which citizens want to use dockless bike sharing service by identifying factors affecting the number of stationless shared bicycles. Also, the results of data analysis are considered to be contributing to policy data to promote the use of dockless bike sharing.

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

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

• Journal of Internet Computing and Services
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• v.24 no.5
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• pp.107-120
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• 2023

The public bicycle of Seoul, Ttareungyi, was launched at October 2015 to reduce traffic and carbon emissions in downtown Seoul and now, 2023 Oct, the cumulative number of user is upto 4 million and the number of bike is about 43,000 with about 2700 stations. However, super growth of Ttareungyi has caused the several problems, especially demand/supply mismatch, and thus the Seoul citizen has been complained about out of stock. In this point, this study conducted a real time demand forecasting model to prevent stock out bike at stations. To develop the model, the research team gathered the rental·return transaction data of 20,000 bikes in whole 1600 stations for 2019 year and then analyzed bike usage, user behavior, bike stations, and so on. The forecasting model using machine learning is developed to predict the amount of rental/return on each bike station every hour through daily learning with the recent 90 days data with the weather information. The model is validated with MAE and RMSE of bike stations, and tested as a prototype service on the Seoul Bike Management System(Mobile App) for the relocation team of Seoul City.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.40 no.2
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• pp.157-166
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• 2020

The city of Suwon has introduced a dockless bike for the first time in Korea. In this study, a questionnaire was conducted for citizens to suggest improvement through satisfaction analysis. As a result of the survey, 88 % of respondents awarded the presence of a bike, and 90 % answered positively in terms of bike policy; whereas mature civic ethics showed an low satisfaction. For the purpose of usage, approximately 75 % of users mainly used for commuting, business, shopping, and the connection with mass transit. In the result of the primary means of a trip, a private car was most preferred before operation; However, it was found that mode transition has been actively carried out. This suggests significant implications for implementing sustainable urbanism. Meanwhile, The level of satisfaction significantly differed in the field of payment method, charge, usage guidance and publicity by an occupation and age groups commonly. Satisfaction in bike maintenance was statistically differed by an occupation. Satisfaction in Rental-return and registration procedures showed differences by an age groups. The results of this study will contribute to establishment related policies and to more activate dockless shared bike.

• Journal of Service Research and Studies
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• v.8 no.4
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• pp.53-75
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• 2018

Bicycle-sharing as a product service system in the sharing economy, its characteristic is that users can go to nearby places by the public bike at any time however they don't need to have their own bicycle. However, the service quality of bicycle-sharing and satisfaction of users continuously decrease with the rapid growth number of bicycle-sharing users in China. Based on the double diamond model and from the perspective of service design, this research studies the service experience of bicycle-sharing of Chinese users, focuses on the potential pain points of Chinese users when using the public bike, and designs the optimum proposal to promote users' satisfaction. According to the research result, the research builds a new concept which can satisfy users' demand for communication, enjoyment and option during the cycling. The experience of new service system of bicycle-sharing makes humanized and subconscious interaction possible.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.5
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• pp.169-178
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• 2021

Recently, a bike-sharing system (BSS) has become popular as a convenient "last mile" transportation. Rebalancing of bikes is a critical issue to manage BSS because the rents and returns of bikes are not balanced by stations and periods. For efficient and effective rebalancing, accurate traffic prediction is important. Recently, cluster-based traffic prediction has been utilized to enhance the accuracy of prediction at the station-level and the clustering step is very important in this approach. In this paper, we propose a k-means based clustering algorithm that overcomes the drawbacks of the existing clustering methods for BSS; indeterministic and hardly converged. By employing the centroid initialization and using the temporal proportion of the rents and returns of stations as an input for clustering, the proposed algorithm can be deterministic and fast.