• 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.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.31 no.4D
• /
• pp.489-496
• /
• 2011

Bike system is one of the green transportation systems and spotlighted recently. In the TOD (Transit-Oriented Development) based transportation and urban planning, bike system will be the major part as linkage modes. In this paper, bike demand estimation model was firstly established in Korea, with considering of personal and household characteristics of traveller, social and economic characteristics of city, weather conditions, and so on. The model reflects population density, the number of students except elementary school students, the number of vehicles, the length of bike roads, and monthly income. The adjusted $R^2$ was 0.738: the model is highly fitted. The results of this paper yield bike demand estimation in the urban planning area: further estimated results will be using to determine economic feasibility and size of bike facility. In other words, this paper is expected to provide the theoretical basis that supporting justification and investment efficiency of bike plans, which are actively progressed recently.

• 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.

• 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.

• International Journal of Highway Engineering
• /
• v.15 no.3
• /
• pp.177-189
• /
• 2013

PURPOSES: This study aims to suggest a proper left-turn treatment method for the bicycle traffic flow at four-legged intersections. METHODS: Four types of crossing methods are proposed and analyzed : (1) indirect left turn, (2) direct left turn, (3) direct left turn on a Bike Box, and (4) direct left turn on bike left turn lane. The VISSIM simulation tests were conducted based on forty-eight operation scenarios prepared by varying vehicle and bicycle traffic volumes. RESULTS : The results from the four-legged signalized intersections suggest that (1) the indirect left turn is appropriate when vehicle demand is high, (2) the direct left turn is efficient on most traffic situation but the safety is a concern, (3) the direct left turn on a Bike Box is appropriate when bicycle demand is high while vehicle demand is not, and (4) the direct left turn on a bike left turn lane is appropriate when both vehicle and bicycle demand are low. CONCLUSIONS : The direct left turn of bicycle provides more efficiency than the indirect left turn at the four-legged intersections but to apply the methods and to study more, advanced evaluation methods, related law, and insurance programs are needed.

• Journal of Korean Society of Transportation
• /
• v.28 no.3
• /
• pp.145-155
• /
• 2010

As the green transportation mode, revitalization of bike usage attracts remarkable public attention. For the acquirement of effective outcome, however, the concrete and close analysis about bike utilization characteristics should be arranged first. One result by MLTM(2009) is support this opinion; the bike mode share has been decreased whereas 9,170km of the bicycle path was improved(1995~2007). This study analyzed the bike mode share classified by trip types by using the 303,308 data of Household Travel Survey of Seoul Metropolitan Area, 2006. The highest mode share rate was induced by the institute attendee and Officetel resident as 3.75% and 3.13%, respectively. Also this study established the bike mode share estimation model of Seoul by logistic regression, and analyzed related factors and level of effectiveness related bike demand by calculation of odds ratio in terms of logistic regression coefficients. In conclusion, short trips, institutes district, parks, and Officetel residential area oriented policy should be effective on the revitalization of bike usage.

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

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.14 no.3
• /
• pp.1011-1015
• /
• 2013

The demand of bike increases eco-friendly as the mean of transportation but domestic production basis becomes sluggish. In this study, the design analysis of horizontal and vertical frame is performed on the model which is proposed as the bike of new concept in conjunction with public transport system. As the result, the structural analysis is conducted on the main frame of urban bike used with cast magnesium alloy. As the vertical load of 150 kg is applied, the design technology insures that maximum stress less than 70 MPa is obtained.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.34 no.5
• /
• pp.1517-1524
• /
• 2014

In recent years, the interest of environmental-friendly transportation modes has been growing. This is because of social and environmental problems such as increasing gas price and climate change. In Europe, bike-sharing service, one of the environmental-friendly transportation modes, has been already operated. Bike-sharing service named "Tashu" has been operated in Daejeon city since 2009. This study is a fundamental research to increase utilization efficiency of bike-sharing service and to decide optimal locations of bike stations. In addition this study examines characteristics of bike usage and analyzes factors affecting to demands using multiple regression model. Based on the result of examining of characteristics of bike usage, the rate of bike usage is higher compared with installation rate of public bike stations near parks in Daejeon. In addition demands of bike usage in weekend is higher than in weekday. It reveals that the main purpose of bike usage could be recreational activities. The return rate at the same location with rental station is comparatively high. Moreover, bike usage pattern is biased in specific areas (Dunsan and Yuseong) because bike-sharing stations are not equally located. As a result of multiple regression model, the factors affecting to demands are number of passengers in buses, length of bike lanes, parks, distance to waterfront, and rate of young people. A statistical significance of factors (r-square) is 0.748, which has strong relationship.

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