• Journal of Distribution Science
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• v.16 no.7
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• pp.5-16
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• 2018

Purpose - This study posits that sustainable mobility of the sharing economy plays a key role to consider environment benefits. The purpose of this study is to investigate the bicycle-sharing economy as an emerging and alternative mode of transportation service and provide managerial and policy implications. The bicycle-sharing economy is still at an early stage of introduction as a transportation mode, while the governmental sector is promoting public bicycle-sharing to encourage bicycle as a substitute for private cars. Research design, data, and methodology - This study analyzed the current status of bicycle sharing programs through a survey that was distributed randomly to users and non-users across the country. Using factor analysis, satisfaction and loyalty for the existing users and intention to use and expected satisfaction for the potential users were examined in relation to utility factors. Results - The results show that economic utility affects satisfaction for user, while storage, mobility, and economic utility affects intention to use for potential users. The findings of this study indicate that in order to promote a bicycle-sharing scheme, it would be better to focus on the scheme's economic advantage to be truly effective. Conclusions - The findings of the study could be applicable to future directions of the sharing economy as a means to achieve the sustainable development of society.

• Journal of Korea Planning Association
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• v.53 no.7
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• pp.49-66
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• 2018

The purpose of this study is to analyze neighborhood environmental factors affecting bicycle accidents and accidental severity in Seoul, Korea. The use of bicycles has increased rapidly as daily transportation means in recent years. As a result, bicycle accidents are also steadily increasing. Using Traffic Accident Analysis System (TAAS) data from 2015 to 2017, this study uses negative binomial regression analysis to identify neighborhood environmental factors affecting bicycle accidents and accidential severity. The main results are as follows. First, bicycle accidents are more likely to occur in commercial and mixed land use areas where pedestrians, bicycle and vehicles are moving together. Second, bicycle accidents are positively associated with road structures such as four-way intersection. In contrast, three-way intersection is negatively associated with serious bicycle accidents. The density of speed hump or street tree is negatively associated with bicycle accidents and accidential severity. This finding indicates the effect of speed limit or street trees on bicycle safety. Fourth, bicycle infrastructures are also important factors affecting bicycle accidents and accidential severity. Bicycle-exclusive roads or bicycle-pedestrian mixed roads are positively associated with bicycle accidents and accidential severity. Finally, this study suggests policy implications to improve bicycle safety.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.9
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• pp.89-96
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• 2016

According to the promoting policy for bicycle riding, the bicycle road infrastructure in Korea has been widely established. As the number of bicycle rider increases, bicycle traffic accidents also increase year after year. In this paper, we analyze bicycle traffic accident data from 2007 to 2014 which is provided by Road Traffic Authority and present statistical results of bicycle traffic accidents. And also regression analysis is applied to predict the number of daily traffic accidents in Seoul using ASOS(Automated Synoptic Observing System) climate data observed in the Seoul sector which are provided by Korea Meteorological Administration. In addition, decision tree analysis techniques are used to forecast the level of traffic accidents severity. In the analytic results of this research, we expect that it will be helpful to establish the collective policy of bicycle accident data and protective strategy in order to reduce the number of bicycle accidents.

• Proceedings of the KSR Conference
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• 2010.06a
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• pp.1320-1327
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• 2010

This study is designed to provide a facilitation program of the railroad tourist business that the bicycle is connected with the railroad. The sustainable growth will be achieved thorough the green growth being able to coexist with environment. To deal with the transportation problems -the environmental pollution caused by a large number of cars and the energy depletion, the traffic congestion- in our society is the prerequisites for the green growth. There is the need focusing on the expansion of the green network and the policy implementation of utilizing the bicycle, the formulation of the social consensus for environmental preservation. This provides the opportunity that we create the customer values and reinforce the firm's competitiveness. This study is to propose the plan for accelerating the tourism business connected to the bicycle, and to contribute to the government policy, to boost the green image of the railroad. Approaching in the strategic way to increase the customer's value and environmental preservation, this study will contribute to providing high quality customer service for both of the business performance and the customer satisfaction.

• Transportation Technology and Policy
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• v.7 no.3
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• pp.87-92
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• 2010

• Transportation Technology and Policy
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• v.7 no.3
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• pp.19-29
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• 2010

• Journal of the Korea Society of Computer and Information
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• v.23 no.10
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• pp.23-31
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• 2018

Recently, there have been many studies on machine learning. Among them, studies on reinforcement learning are actively worked. In this study, we propose a controller to control bicycle using DDPG (Deep Deterministic Policy Gradient) algorithm which is the latest deep reinforcement learning method. In this paper, we redefine the compensation function of bicycle dynamics and neural network to learn agents. When using the proposed method for data learning and control, it is possible to perform the function of not allowing the bicycle to fall over and reach the further given destination unlike the existing method. For the performance evaluation, we have experimented that the proposed algorithm works in various environments such as fixed speed, random, target point, and not determined. Finally, as a result, it is confirmed that the proposed algorithm shows better performance than the conventional neural network algorithms NAF and PPO.

• 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.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.41 no.3
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• pp.257-265
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• 2021

In this study, an evaluation index was developed and applied to derive indicators related to the environment for bicycle use and to evaluate the environment for bicycle use in the city of Suwon. Analysis showed that the relative importance between the assessment factors was highest in bicycle safety and that the relative importance among the assessment indicators was highest in terms of priority of items directly affecting bike riding, and items with indirect influence were low in importance. As a result of applying the evaluation model to bike paths in Suwon, it was confirmed that they can be described in a relatively realistic manner. The findings are expected to contribute to the development of local government directives for improving the environment of cycle paths.

• Convergence Security Journal
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• v.18 no.3
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• pp.3-9
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• 2018

The Deep Deterministic Policy Gradient (DDPG) algorithm is an algorithm that learns by using artificial neural network s and reinforcement learning. Among the studies related to reinforcement learning, which has been recently studied, the D DPG algorithm has an advantage of preventing the cases where the wrong actions are accumulated and affecting the learn ing because it is learned by the off-policy. In this study, we experimented to control the bicycle autonomously by applyin g the DDPG algorithm. Simulation was carried out by setting various environments and it was shown that the method us ed in the experiment works stably on the simulation.