• Proceedings of the Korea Information Processing Society Conference
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• 2017.11a
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• pp.1061-1064
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• 2017

자전거 이용자 수가 천만 명을 넘으면서, 자전거 사업은 우리의 삶에 중요 요소로 자리 잡게 되었다. 본 연구에서는 자전거 사용자들에게 편의를 제공하기 위해 GPS정보를 기반으로 한 자전거 전용 네비게이션과 사용자들을 자전거 사고로부터 보호할 수 있는 사고 대처 시스템을 개발하였다. 이를 위해 우리는 아두이노기반의 시스템을 토대로 개발되었으며 스마트폰과 블루투스 송신기술을 사용하여 다양한 정보를 주고 받을 수 있도록 설계하였다. 우리의 사고 대처시스템은 실시간으로 자전거 운행 정보를 공유하도록 하여 보다 효율적으로 자전거 사고를 예방 할 수 있도록 하는데 그 목적이 있다. 이를 위해 우리의 시스템은 아두이노를 통해 측정한 센서 값들을 스마트폰을 통해 자전거 사용자들에게 실시간으로 공유되도록 하고 이를 통해 다른 자전거 사용자들이 자전거 도로의 상태, 사고 내용들을 파악하도록 하여 안전하고 편리한 자전거 운행이 가능하도록 하여 향후 자전거 사업이 발전시킬 수 있는 계기를 마련할 것으로 기대한다.

• Journal of Service Research and Studies
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• v.9 no.4
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• pp.19-40
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• 2019

Mobility as a Service is a service conception to achieve the intelligent transportation system. Its aims to improve the travel experience. In one platform, it connects various transportation modes to offer service and users only need to pay in one time for the whole travel. But the single MaaS platform easily faces the problems such as low user traffic, low retention rate and the access to markets of the this service is still unfound. From the perspective of bicycle-sharing, this research makes the MaaS concept into bicycle-sharing to build a better service model. The bicycle-sharing combine with the MaaS concept is innovative in business model, travel model and service architecture. In addition, this research also tests users' expectations for service model. The research shows that MaaS-based bicycle-sharing could offer flexible and convenient travel experience in the aspects of combined transportation, travel, transfer and payment, and it also makes it possible to make travel services according to need, and sustainable transport.

• 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.

• 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 the Korea Institute of Information Security & Cryptology
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• v.29 no.4
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• pp.719-728
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• 2019

Recently, there has been a growing market for shared mobility businesses that share 'transport' such as cars and bikes, and many operators offer a variety of services. However, if the fare can not be charged normally because of security vulnerability, the operator can not continue the business. So there should be no security loopholes. However, there is a lack of awareness and research on shared mobility security. In this paper, we analyzed security vulnerabilities exposed in application log of shared bike service in Korea. We could easily obtain the password of the bike lock and the encryption key of the AES-128 algorithm through the log, and confirmed the data generation process for unlocking using software reverse engineering. It is shown that the service can be used without charge with a success rate of 100%. This implies that the importance of security in shared mobility business and new security measures are needed.

• 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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.8
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• pp.1123-1132
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• 2018

In this paper, we design and implement a public bicycle sharing system based on smart phone application capable of distributing access codes via internet connection. When smartphone user uses the application to request a bicycle unlock code, server receives the request and sends an encrypted code, which is used to unlock the bicycle at the station and the same code is used to return the bicycle. The station's hardware prototypes were built on top of Internet devices such as raspberry pi, arduino, keypad, and motor driver, and smartphone application basically includes shared bike rental and return functionality. It also includes an additional feature of reservation for a certain time period. We tested the implemented system, and found that it is efficient because it shows the average of 3-4 seconds delay. The system can be implemented to manage multiple bikes with a single control box, and as the user can use a smartphone application, this makes the system more cost effective.

• 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.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.6
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• pp.14-25
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• 2021

With the spread of COVID-19 and the government policy of social distancing, the demand for subways and buses is decreasing, whereas the demand for public bicycles and personal transportation is increasing. Hence, research is needed to understand the characteristics of this phenomenon and to prove the statistical reliability of the correlation between the subway and shared bicycle demands. In this study, the correlation between the number of confirmed COVID-19 cases and the replacement rate of subway and public bicycle demands was examined, but the statistical significance was not significant. However, during the period of September to December 2020, in which the number of confirmed COVID-19 cases in Seoul started to increase rapidly, there was a correlation between the number of confirmed COVID-19 cases and the replacement ratio. If the number of confirmed COVID-19 cases increases by more than a certain number, public bicycles are expected to play a significant role as alternates to the subways. It is expected that the role of public bicycles will increase, and that it is possible to suggest the direction of transportation operation and policy establishment for the continuation of COVID-19 countermeasures in field demonstration after elementary technology development. It is also expected that this study will suggest a direction for future development and policymaking.

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