Hanyang University · 工学
Professor Cheol Oh's research lab specializes in intelligent transportation systems, with a focus on enhancing traffic safety and efficiency through advanced data-driven methodologies. The lab investigates real-time risk assessment, vehicle behavior in mixed traffic environments (including automated and manual vehicles), and the development of innovative warning and surveillance systems using trajectory data and machine learning. Key research directions include real-time safety monitoring, signalized intersection performance evaluation, and the application of probabilistic models such as Bayesian networks and probabilistic neural networks to improve transportation decision-making.
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The Global Forum on Reinventing Government has made government reform and new forms of comparative public administration and politics global issues. Since the forum was initiated in the United States in 1999, it has been held in locations around the world with broad representation. Yet the proceedings of these forums have not been fully reported to the international public administration community. This paper reports on the ideas on reinventing governance that emerged from the Sixth Global Forum
This study presents a warning information system based on an innovate methodology to estimate accident likelihood in real time. Bayesian modeling approach implemented by the probabilistic neural network (PNN) is conducted to identify hazardous traffic conditions leading to potential accident occurrence. The proposed system displays warning signs to call drivers' attention for safer and careful driving once hazardous traffic conditions are observed by evaluating accident likelihood. It is believe
Vehicle platooning, a beneficial feature of automated driving environments, is likely to affect the lane change behaviour of manually driven vehicles (MVs) because identifying proper gaps in vehicle platoons in the target lane will be more difficult than in the current non‐platooning environment. These interactions between MVs and automated vehicles (AVs) could lead to a higher potential for unstable traffic flow, which is closely associated with traffic safety issues. The objective of this stud
The purpose of this study is to explore aspects of learning that are perceived as helping foster successful eLearning for all participants separated by time and distance. The author argues that learner-centered practice is necessary to improve the quality of learning on the Internet. To this end, this study deals with the issues of potential or actual students as they relate to eLearning. In two different surveys, most respondents expected information communication technology to play a key role
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