• The Journal of the Korea Contents Association
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• v.19 no.1
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• pp.132-140
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• 2019

Recently, many cities around the world introduced and operated shared bicycle system to reduce the traffic and air pollution. Seoul also provides shared bicycle service called as "Ddareungi" since 2015. As the use of shared bicycle increases, the demand for bicycle in each station is also increasing. In addition to the restriction on budget, however, there are managerial issues due to the different demands of each station. Currently, while bicycle rebalancing is used to resolve the huge imbalance of demands among many stations, forecasting uncertain demand at the future is more important problem in practice. In this paper, we develop forecasting model for demand for Seoul shared bicycle using statistical time series analysis and apply our model to the real data. In particular, we apply Holt-Winters method which was used to forecast electricity demand, and perform sensitivity analysis on the parameters that affect on real demand forecasting.

• Journal of Korean Society for Quality Management
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• v.46 no.3
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• pp.523-538
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• 2018

Purpose: The purpose of this study was to propose useful suggestions by analyzing causal effect relationship between sharing bicycle service quality, customer satisfaction, CSV, and continuos use intention in China. Methods: The collected data through the survey were analyzed using structure equation model analysis. The measurement tools used for this study were divided into four dimensions such as service quality, customer satisfaction, creating shared value, and continuous use intention. Results: The results of this study are as follows; regarding the influence of sharing bicycle service quality dimension on customer satisfaction(reliability and response), it was found that customer satisfaction was not significant on economic value. but it was significant on social value. It was found that social value made statistically significant influence on use intention. Conclusion: Sharing bicycle industry needs to activity of response to customer, and paying attention to view of social value for the sharing economy in the future.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.1
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• pp.45-56
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• 2022

Recently, bicycle sharing system is introduced and the usage of shared bicycles is increasing in Suwon city. Despite the need to expand the bicycle road infrastructure, this is not the case. Therefore, this research attempts to propose a method for bicycle lane installation in Suwon city. For this, this research conducted location analysis based on the shared bicycle usage data and trip inducing facility data. Using location analysis results, appropriate routes for bicycle lanes are selected. As a result, two routes are selected. These routes have advantages that it is easy to connect with the existing bicycle roads or traffic inducing facilities and to install using the existing bicycle roads. However, these routes also have disadvantage that traffic congestion may occur due to the occupancy of the existing road space. It is expected that this research may contribute to expansion and maintenance of bicycle lane infrastructure, the bicycle and PM sharing service usage, implementation of sustainable urban transportation systems in Suwon city.

• Journal of Information Processing Systems
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• v.19 no.2
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• pp.219-231
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• 2023

Intelligent applications are crucial for increasing the popularity of shared urban electric bicycles (EBs). Building an application platform architectural system that can satisfy independent user operations is critical for increasing the intelligent usage of shared EBs. Consequently, we collected online reviews of shared EB applications, conducted semantic processing and sentiment analysis, and refined the positive and negative review data for each function. The positive and negative review indices of each function were calculated using the formulae for positive and negative review indices of product functions, thereby determining the functions that need to be improved. Each function of the Shared EB application was improved according to its business process. The main contributions of this study are to build a user requirement architecture system for the Shared EB application with five dimensions and 22 functions using the Delphi method to design the user interface (UI) of this application based on user satisfaction evaluation results; to create a high-fidelity dynamic interaction prototype and compare user satisfaction before and after improving the Shared EB application functions. The testing results indicate that the changes in the UI significantly improve the user experience satisfaction of the urban Shared EB application, with the positive experience index increasing by 69.21% and the negative experience index decreasing by 75.85% overall. This information can be directly used by relevant companies to improve the functions of the Shared EB application.

• Journal of Advanced Navigation Technology
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• v.22 no.1
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• pp.43-47
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• 2018

In recent years, bicycle craze has been blowing in Korea, and bicycle has become a means of transportation near. In addition, there is a lot of interest in the Internet of things, and recently there is a lot of interest in the Internet of things. However, despite the development of the bicycle market, the bicycle cradle does not have such a development, and it still has a drawback in that it requires a separate lock for the lock. In this regard, I would like to find some problems and solve them. In this paper, we have implemented a system that can control the location and use of the bicycle cradle in real time by communicating with the app and Arduino so that it can help the collection and retrieval of the bicycle which is the problem of the current bicycle cradle. n addition, we propose a method to establish a shared economic system based on Internet of things by providing shared services with acquaintances and others, and to improve user convenience by providing real - time services.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.1
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• pp.10-21
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• 2021

This study analyzed how external variables, such as the supply policy of shared bicycles and the spread of infectious diseases, affect the demand for shared bicycle use in the COVID-19 era. In addition, this paper presents a methodology for more accurate predictions. The Seasonal Auto-Regulatory Integrated Moving Average with Exogenous stressors methodology was applied to capture the effects of exogenous variables on existing time series models. The exogenous variables that affected the future demand for shared bicycles, such as COVID-19 and the supply of public bicycles, were statistically significant. As a result, from the supply volume and COVID-19 outbreak according to the scenario, it was estimated that approximately 46,000 shared bicycles would be supplied by 2022, and the COVID-19 cases would continue to be at the current level. In addition, approximately 32 million and 45 million units per year will be needed in 2021 and 2024, respectively.

• Korean Journal of Artificial Intelligence
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• v.10 no.1
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• pp.9-14
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• 2022

The demand for public bicycles operated by the Seoul Metropolitan Government is increasing every year. The size of the Seoul public bicycle project, which first started with about 5,600 units, increased to 3,7500 units as of September 2021, and the number of members is also increasing every year. However, as the size of the project grows, excessive budget spending and deficit problems are emerging for public bicycle projects, and new bicycles, rental office costs, and bicycle maintenance costs are blamed for the deficit. In this paper, the Azure Machine Learning Studio program and the Boosted Decision Tree Regression technique are used to predict the number of public bicycle rental over environmental factors and time. Predicted results it was confirmed that the demand for public bicycles was high in the season except for winter, and the demand for public bicycles was the highest at 6 p.m. In addition, in this paper compare four additional regression algorithms in addition to the Boosted Decision Tree Regression algorithm to measure algorithm performance. The results showed high accuracy in the order of the First Boosted Decision Tree Regression Algorithm (0.878802), second Decision Forest Regression (0.838232), third Poison Regression (0.62699), and fourth Linear Regression (0.618773). Based on these predictions, it is expected that more public bicycles will be placed at rental stations near public transportation to meet the growing demand for commuting hours and that more bicycles will be placed in rental stations in summer than winter and the life of bicycles can be extended in winter.

• The Journal of the Convergence on Culture Technology
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• v.7 no.2
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• pp.377-388
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• 2021

With the growth of the global sharing economy market, various sharing services have recently appeared in South Korea. Seoul city unmanned public bicycle rental service, "Ttareuni" is one of the services representing the shared mobility industry in South Korea. Although many users use this service, the usability of the mobile application, which is the essential medium of utilizing the service, is not improving. In this regard, this study conducted usability testing and in-depth interviews for Ttareungi's main users in their 20s and 30s to improve the user experience of the mobile application of Ttareungi. As a result, the problems of Ttareungi mobile application were identified, and based on this, design considerations for shared mobility services having publicity like Ttareungi were proposed. This is expected to contribute to improving the user experience of shared mobility services.

• Journal of the Korean housing association
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• v.11 no.1
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• pp.115-124
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• 2000

The purpose of this research is to examine residents' needs for shared community spaces and to explore the relationships between these needs and characteristics of consumers such as age and employment status of the homemakers, family life cycle stage, occupation of the primary income provider, housing size, and homeownership. Statistical data were compiled to determine frequencies and percentage distributions, and subjected to General Linear Model and Duncan-test analysis.Most residents wanted to utilize the basements of their complexes for storage space. Further interest was shown for shared community spaces that would be run by residents themselves, such as vegetable gardens, indoor playgrounds for children, senior citizen's activity rooms, walking paths, study rooms, and lounges. Female residents under 45 years old were likely to express needs for storage spaces for bicycle, and multipurpose rooms for meetings and family events. Female residents with jobs were more likely to desire shared community spaces than full-time females residents were. These findings imply the need for consideration of diverse plans rather than uniform ones. This results can be usefully applied to develop new apartment housing for different social class residents.

• Korean Journal of Artificial Intelligence
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• v.10 no.1
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• pp.27-32
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• 2022

As the need for eco-friendly transportation increases due to the deepening climate crisis, many local governments in Korea are introducing shared bicycles. Due to anxiety about public transportation after COVID-19, bicycles have firmly established themselves as the axis of daily transportation. The use of shared bicycles is spread, and the demand for bicycles is increasing by rental offices, but there are operational and management difficulties because the demand is managed under a limited budget. And unfortunately, user behavior results in a spatial imbalance of the bike inventory over time. So, in order to easily operate the maintenance of shared bicycles in Seoul, bicycles should be prepared in large quantities at a time of high demand and withdrawn at a low time. Therefore, in this study, by using machine learning, the linear regression algorithm and MS Azure ML are used to predict and analyze when demand is high. As a result of the analysis, the demand for bicycles in 2018 is on the rise compared to 2017, and the demand is lower in winter than in spring, summer, and fall. It can be judged that this linear regression-based prediction can reduce maintenance and management costs in a shared society and increase user convenience. In a further study, we will focus on shared bike routes by using GPS tracking systems. Through the data found, the route used by most people will be analyzed to derive the optimal route when installing a bicycle-only road.