• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.3D
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• pp.363-373
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• 2011

With rapid economic development, general demand and interest in surroundings that consist of our lives have grown gradually. In addition, as there has been increased social interest in creating landscape of roads, which considers all important factors from the user's view including accessibility, safety, and psychological stability, efforts to improve quality of roads are required. Therefore it is needed to establish standards on safe and comfort road design based on sensibility satisfaction of road users rather than based on standardized road design guidance from the engineering perspective. To this end, research was carried out to analyze sensibility satisfaction of users about road landscape focused on elements of road cross section in a city. It identified relation between sensibility satisfaction and the elements by using principal and cluster analysis, and the multiple regression models. It also found that user's satisfaction about roads and a road landscape is high with road width (3~5 meters), clear zone (2.2~3.9 meters), road central garden (1.05~1.9 meters), shoulder (0.55~1.43 meters), median (0.65~1.625 meters), the number of travel lanes (2~5), height of trees at the central garden (6.4~15 meters) and height of buildings surrounding roads (18~44 meters or 6~15 floors).

• KIPS Transactions on Software and Data Engineering
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• v.12 no.2
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• pp.59-76
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• 2023

Since the COVID-19 era, the rise in apartment prices has been unconventional. In this uncertain real estate market, price prediction research is very important. In this paper, a model is created to predict the actual transaction price of future apartments after building a vast data set of 870,000 from 2015 to 2020 through data collection and crawling on various real estate sites and collecting as many variables as possible. This study first solved the multicollinearity problem by removing and combining variables. After that, a total of five variable selection algorithms were used to extract meaningful independent variables, such as Forward Selection, Backward Elimination, Stepwise Selection, L1 Regulation, and Principal Component Analysis(PCA). In addition, a total of four machine learning and deep learning algorithms were used for deep neural network(DNN), XGBoost, CatBoost, and Linear Regression to learn the model after hyperparameter optimization and compare predictive power between models. In the additional experiment, the experiment was conducted while changing the number of nodes and layers of the DNN to find the most appropriate number of nodes and layers. In conclusion, as a model with the best performance, the actual transaction price of apartments in 2021 was predicted and compared with the actual data in 2021. Through this, I am confident that machine learning and deep learning will help investors make the right decisions when purchasing homes in various economic situations.