• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.1
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• pp.223-233
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• 2014

This study empirically analyzes the relationships among the road and traffic experts' personal characteristics, the driving behavior and factors being expected to have an effect on the satisfaction about roundabout operation. The factors are drawn and grouped through the principle component analysis to clarify driving environment satisfaction on roundabout operation. Each group is named as personal attribute, driving behavior attribute, and satisfaction. After the variables are refined by confirmatory factor analysis, satisfaction model is developed with personal attribute and driving behavior attributes as exogenous variables and roundabout driving awareness and emotion attributes as endogenous variables. As a result, driving satisfaction of roundabout operation is directly influenced by delay reduction, safety improvement, capacity increase, sight improvement, severity accident reduction, and bicycle convenience and indirectly gender, age, driving time, and driving experience. Law obeyance, driving concession, traffic sign obeyance, and interposition do not statistically shows significant on satisfaction. As a result of Analytical Hierarchy Process (AHP), the turning radius of geometry and the driving behavior are important elements for roundabout safety.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.4
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• pp.44-57
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• 2019

Various studies have been conducted to solve traffic congestions in many metropolitan cities through accurate traffic flow prediction. Most studies are based on the assumption that past traffic patterns repeat in the future. Models based on such an assumption fall short in case irregular traffic patterns abruptly occur. Instead, the approaches such as predicting traffic pattern through big data analytics and artificial intelligence have emerged. Specifically, deep learning algorithms such as RNN have been prevalent for tackling the problems of predicting temporal traffic flow as a time series. However, these algorithms do not perform well in terms of long-term prediction. In this paper, we take into account various external factors that may affect the traffic flows. We model the correlation between the multi-dimensional context information with temporal traffic speed pattern using deep neural networks. Our model trained with the traffic data from TOPIS system by Seoul, Korea can predict traffic speed on a specific date with the accuracy reaching nearly 90%. We expect that the accuracy can be improved further by taking into account additional factors such as accidents and constructions for the prediction.

• 한국노년학
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• v.38 no.3
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• pp.593-605
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• 2018

This study examines factors that influence older drivers to consider stopping driving in advance and whose advice plays an important role in the driving cessation decision. Data came from a nationally representative cross-sectional survey of community-dwelling older adults (N=2,076). About two thirds reported that they have considered stopping driving (62.8%) and they preferred to decide by themselves (66.0%). Logistic regression results showed that women and residents in urban areas were twice as likely to consider voluntary driving cessation compared to their counterparts. Each one year increase in age contributed to the probability that respondents would consider driving cessation by five percent. Those with greater difficulties in ADLs due to chronic illnesses were also more likely to consider driving cessation. No sociodemographic and health factors were related to whether older drivers preferred to decide by themselves or listen to others. These results imply that policies to promote self-motivation to stop driving among older drivers would be effective. Furthermore, they suggest it could be important to help older drivers plan their driving cessation in advance while still at a young-old age, and that more attention needs to be paid to men and those living in rural areas among older drivers.

• Journal of Wetlands Research
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• v.26 no.1
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• pp.41-50
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• 2024

Plant Microbial Fuel Cells (P-MFCs) are biomass-based energy technologies that generate electricity from plant and root microbial communities and are suitable for natural fundamental solutions considering sustainable environments. In order to develop P-MFC technology suitable for domestic waterfront space, it is necessary to analyze international research trends first. Therefore, in this study, 700 P-MFC-related research papers were investigated in Web of Science, and the core keywords were derived using VOSviewer, a word analysis program, and the research trends were analyzed. First, P-MFC-related research has been on the rise since 1998, especially since the mid to late 2010s. The number of papers submitted by each country was "China," "U.S." and "India." Since the 2010s, interest in P-MFCs has increased, and the number of publications in the Philippines, Ukraine, and Mexico, which have abundant waterfront space and wetland environments, is increasing. Secondly, from the perspective of research trends in different periods, 1998-2015 mainly carried out microbial fuel cell performance verification research in different environments. The 2016-2020 period focuses on the specific conditions of microbial fuel cell use, the structure of P-MFC and how it develops. From 2021 to 2023, specific research on constraints and efficiency improvement in the development of P-MFC was carried out. The P-MFC-related international research trends identified through this study can be used as useful data for developing technologies suitable for domestic waterfront space in the future. In addition to this study, further research is needed on research trends and levels in subsectors, and in order to develop and revitalize P-MFC technologies in Korea, research on field applicability should be expanded and policies and systems improved.