• 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.

• 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.

• 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.

• 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.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06d
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• pp.158-160
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• 2012

건강에 대한 관심의 증가와 자전거의 이용자의 증가로 다방면에서 자전거 이용 활성화에 관한 법률이 지정되고 있다. 이런 흐름을 반영하듯 자전거 관련 어플리케이션도 다양하게 개발되어있다. 또한 소설네트워크의 발전으로 서로간의 정보교류가 활발해진 만큼 그에 따른 서비스도 필요로 하게 되었고, 이전과는 다른 자전거의 가치로 인해 자전거의 절도에 대한 방지 서비스도 필요하게 되었다. 이에 따라서 기존의 어플리케이션에서 제공했던 경로, 운동량, 속도의 정보 외에도 사용자간의 경로와 자전거정보 공유, 분실된 자전거를 찾을 수 있도록 도움이 되는 기능을 추가하여 기존의 어플리케이션보다 나은 어플리케이션을 제안한다. 사용자가 공유하려는 경로데이터를 데이터베이스에 저장하여 다른 사용자에게 전달하여주고 모든 사용자의 데이터를 정리하여 QR코드를 부여하여 분실시 QR코드를 사용하여 사용자간에 분실자전거를 찾게 해주는 방법을 사용하였다. 본 논문에서는 구현 하려는 어플리케이션의 설계와 구현하였다.

• 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.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.01a
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• pp.91-93
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• 2021

퍼스널 모빌리티를 비롯한 공유 모빌리티 시장이 국내에서 급격한 성장을 거두고 있다. 서울시에서는 2015년부터 공공자전거 서비스 '따릉이' 사업을 시작해 서울시민에게 주목받는 정책 중 하나로 자리매김했다. 그에 따라 매해 늘어나는 따릉이 수요를 맞추기 위해 서울시에서는 따릉이 대여소를 매해 증설하고 있으나, 자전거 부족, 거치대 부족으로 많은 불만이 나오고 있다. 본 논문에서는 따릉이 대여소의 이용률을 분석하여 사용이 집중되는 대여소와 그 시간대를 분석하고, 이를 통해 특정 대여소에 자전거가 필요 이상으로 반납되거나 부족해지는 현상을 해결할 방법을 제시한다.

• The Journal of Bigdata
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• v.8 no.2
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• pp.45-54
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• 2023

Seoul's public bicycle system, 'Ttareungyi,' introduced in 2015, has achieved an annual ridership of 40 million in 2022. Similarly, electric scooters, a type of personal mobility device, surpassed one million riders in 2020 due to various sharing platforms. However, the major roadways for these new transportation, bicycle lanes, are notably insufficient compared to other forms of transport. Hence, this study proposes an optimal location selection method for bicycle lanes in Seoul to prevent accidents and enhance bicycle ride safety. The location selection process prioritizes road safety concerning bicycle accident risk. Using regression models, high-risk areas for bicycle accidents are identified. Cluster analysis categorizes these areas into six clusters, each suggesting suitable types of bicycle lanes based on cluster-specific characteristics. We hope that this study will contribute to the improvement of Seoul's transportation environment, including the expansion of dedicated bicycle lanes and lanes for personal mobility devices.

• 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.

• 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.