• Journal of Industrial Convergence
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• v.15 no.1
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• pp.33-42
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• 2017

The purpose of this study is to identify the current situation of bicycle rental center in Seoul through big data analysis and to find ways to improve it. For this purpose, we analyzed the open data set provided by the Seoul Metropolitan Government and the typical data which is the citizen opinion of the customer center of the Seoul City bicycle. As the result, it was found that it is better to install a bicycle rental shop in Gangdong-gu, Seoul.

• KIISE Transactions on Computing Practices
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• v.22 no.6
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• pp.253-267
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• 2016

The world's major cities operate public rental bicycle systems to complement the existing problems of public transport in the city. Disclosing the rental history data in Daejeon has opened new analytical possibilities. In this paper, we proposed a method to analyze the data using the visualization. We found a positional feature of the station according to the bicycle usage. In addition, we examined the bicycle usage patterns according to the time/day/month. On the other hand, the usage patterns between each of the bicycle stations were identified through a path analysis. The specific objectives were identified through each stop destination ratio analysis. Based on these data, we suggest a direction of Daejeon public bicycle rental system development.

• 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.

• Journal of the Korea Society of Computer and Information
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• v.17 no.8
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• pp.41-50
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• 2012

Most of the developed countries and Korea have continued to invest much money in developing low-carbon vehicles such as electric car, methanol car and hydrogen car to replace the conventional fossil fuel vehicles. Government and local governments of each country, however, grope to construct roads for bicycle and public bicycle rental systems because bicycle is the only ultimate and feasible non-pollution transportation. Most of the current bicycle monitoring systems have achieved automation of rental process in bicycle stations, but they can not monitor bicycles in use. This paper develops GPS-CDMA modem combined terminals and implements a LBS-based bicycle monitoring system using them for public bicycle rental system. The monitoring system collects location information from GPS-CDMA modem combined terminals attached on bicycles and presents the moving tracks of bicycles on a GIS map for easy return and redistribution of bicycles. Also, the system helps to prevent from theft and vandalism of bicycles and to recommend the nearest bicycle station.

• Journal of Internet of Things and Convergence
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• v.9 no.6
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• pp.111-117
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• 2023

There is a lot of research on using a combination of graph neural networks and recurrent neural networks as a way to account for both temporal and spatial dependencies. In particular, graph neural networks are an emerging area of research. Seoul's bicycle rental service (aka Daereungi) has rental stations all over the city of Seoul, and the rental information at each station is a time series that is faithfully recorded. The rental information of each rental station has temporal characteristics that show periodicity over time, and regional characteristics are also thought to have important effects on the rental status. Regional correlations can be well understood using graph neural networks. In this study, we reconstructed the time series data of Seoul's bicycle rental service into a graph and developed a rental prediction model that combines a graph neural network and a recurrent neural network. We considered temporal characteristics such as periodicity over time, regional characteristics, and the degree importance of each rental station.

• Journal of Korea Multimedia Society
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• v.25 no.2
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• pp.287-296
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• 2022

Today, air pollution is becoming a severe issue worldwide and various policies are being implemented to solve environmental pollution. In major cities, public bicycles are installed and operated to reduce pollution and solve transportation problems, and operational information is collected in real time. However, research using public bicycle operation information data has not been processed. This study uses the daily weather data of Korea Meteorological Agency and real-time air pollution data of Korea Environment Corporation to predict the amount of daily rental bicycles. Cross- validation, principal component analysis and multiple regression analysis were used to determine the independent variables of the predictive model. Then, the study selected the elements that satisfy the significance level, constructed a model, predicted the amount of daily rental bicycles, and measured the accuracy.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.3
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• pp.28-37
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• 2020

This study developed a deep learning model that predicts rental demand for public bicycles. For this, public bicycle rental data, weather data, and subway usage data were collected. After building an exponential smoothing model, ARIMA model and LSTM-based deep learning model, forecasting errors were compared and evaluated using MSE and MAE evaluation indicators. Based on the analysis results, MSE 348.74 and MAE 14.15 were calculated using the exponential smoothing model. The ARIMA model produced MSE 170.10 and MAE 9.30 values. In addition, MSE 120.22 and MAE 6.76 values were calculated using the deep learning model. Compared to the value of the exponential smoothing model, the MSE of the ARIMA model decreased by 51% and the MAE by 34%. In addition, the MSE of the deep learning model decreased by 66% and the MAE by 52%, which was found to have the least error in the deep learning model. These results show that the prediction error in public bicycle rental demand forecasting can be greatly reduced by applying the deep learning model.

• Journal of Korea Multimedia Society
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• v.18 no.11
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• pp.1351-1357
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• 2015

This paper proposes and implements a mobile service and web service integration for utilizing public bicycle sharing system NUBIJA. The NUBIJA system is operated by Changwon City, GyeongNam since 2008 and maintains 247 unattended bicycle sharing terminal on the city. Average users per day of the NUBIJA are more than 8,000 and many users want to get more services from their mobile phones. We implemented realtime mobile bicycle rental and return information service. The informations are acquired from each terminal kiosks and we integrated the information and location based services(LBS) on iOS and Android platform. In this paper, we described an integration services between web and mobile application and shows graphical user interface for highly customized on mobile platforms.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.17 no.2
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• pp.83-92
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• 2024

Currently, various countries are enhancing accessibility by providing bicycle rental services for convenient usage within daily life. This paper introduces the Nubija public bicycle service in Changwon, South Korea, aiming to address the imbalance between demand and supply of Nubija bicycles. We propose a Highest Priority Reallocation Scheme to prevent this disparity. Comparing this scheme with others that randomly visit terminals for redistribution and those that prioritize terminals closest to current locations, we illustrate its superior efficiency. Our proposed Highest Priority Reallocation Scheme prioritizes terminals with the highest demand and shortest distances nearby. Through experiments, our proposed scheme demonstrates superior performance, with the lowest average of 817.44km distance and an average of 6437.45 times, i.e., 88.14% successful rental occurrences. This highlights its superiority over the other two algorithms.

• Journal of Digital Convergence
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• v.13 no.4
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• pp.247-252
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• 2015

The earth has been suffering from global warming and experiencing unusual weather caused by increased fossil fuel usage in the industrialization period. In spite of the continuous effort to resolve the problem, the amount of fossil fuel usage is increasing constantly. Public bicycle system has been introduced as a solution to the fundamental problem of the existing public transportation systems. Also public bicycle system has another advantage that riding bicycle can keep the users in good health. In this paper, a new ICT convergence public bicycle system is presented which resolves the problems of existing public bicycle systems. The presented system has strong points in low installation fee and low maintenance expenses. The effectiveness of the presented system will be proven by analyzing case studies.