• Asia pacific journal of information systems
• /
• v.29 no.3
• /
• pp.414-427
• /
• 2019

The sharing economy has significantly changed the way of living for years. The emergence and expansion of sharing economy empowered by the mobile information technologies and intellectual algorithms reconfigure how people use transportation means. In this paper, the bike sharing phenomenon is highlighted. Combining a big data set provided by the Seoul government about user logs and air quality data set, the empirical findings reveal that temperature change is tightly associated bike sharing activities. Also, the concentration of particulate matter is weakly related to bike sharing, but the trend should be carefully examined. By considering external environmental factors to bike sharing businesses, this work is differentiated. To further understand empirical data, data mining methods and econometric approaches were adopted.

• 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.

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

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

• Journal of Digital Convergence
• /
• v.18 no.7
• /
• pp.167-175
• /
• 2020

With the recent revitalization of the shared economy, bike-sharing services are gaining huge popularity in the bicycle sector. Bike-sharing services are characterized by reducing environmental pollution and borrowing bicycles at low prices. This study investigated the mechanisms for the formation of customer's continuance intention toward bike-sharing services. The theoretical framework clarified the role of perceived value and trust in enhancing customer's continuance intention. Perceived usefulness, perceived ease of use and perceived enjoyment are considered as the vital factors of enhancing perceived value and trust in a service provider. The research model was validated by data from 217 bike-sharing users in China. Both perceived value and trust in a service provider had a significant impact on user's continuance intention. However, the analysis results showed that perceived usefulness does not have a significant impact on both perceived value and trust in a service provider. Perceived ease of use and perceived enjoyment played a significant role in enhancing both perceived value and trust in a service provider. Our results are expected to provide academic and practical implications for bike-sharing services.

• 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.

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

• Knowledge Management Research
• /
• v.22 no.4
• /
• pp.135-155
• /
• 2021

With the emergence of various types of social media and the diversification of their roles, it has become essential for marketers to understand how different types of social media influence consumers' purchase decisions differently and derive more detailed strategies by social media types. This study classifies social media into two types-expression-focused social media and relationship-focused social media-and investigates the relationship between consumer purchases and social media mentions by type. Using the Seoul bike-sharing data and time-series data for social media mentions, we apply the VAR model with Exogenous Variables (VARX). We find that the increase of product mentions in expression-focused social media positively affects both the number of new customers (customer acquisition) and the number of shared bike rentals, while that in relationship-focused social media negatively affects the number of new customers only. In addition, as new customers increase, the product mentions in both types of social media increase. On the other hand, the number of bike rentals has no significant effect in increasing social media mentions regardless of type. This study contributes to the social media and sharing economy literature and provides managerial implications for establishing sophisticated social media marketing in bike-sharing businesses.

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

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