• Journal of Korean Society of Transportation
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• v.36 no.3
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• pp.184-194
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• 2018

Recently, the need to introduce bus lines on a welfare basis is increasing as public intervention in bus routes is accelerated through semi-public bus system. The main objective of this study is to establish the concept of PSO (public service obligation) bus line on a welfare basis and propose a classification method of PSO bus line under semi-public bus system. We introduced 3 indexes such as "equity" in terms of bus line distribution, "publicity" in terms of age distribution, and "profitability" in terms of bus operating revenues. On the basis of 3 indexes, we introduced the PSO bus line, which is fair and public, even if it is not profitable. Based on the concept established in this study, we divided 130 ordinary city bus lines in Busan Metropolitan City into 8 PSO bus lines. Also, considering the positive impact of public buses, which are less profitable, but improve the mobility of residents and secure public community, we suggested the need to introduce public buses in bus service shortage areas.

• Korean Institute of Interior Design Journal
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• v.21 no.2
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• pp.23-34
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• 2012

After the modern age, the rapid urbanizationhad a big impact on the then architecture. R. Venturi and A. Rossi are two of the leading architects, developing architecture in cities in the US and Europe respectively. This study shed light on a tangible and intangible meaning and interpretation of urban landscapes through their architectural thoughts and architectures. The followings are the physical and intangible meaning and interpretation in architectural thoughts and works of those two architects. Venturi understood that iconological landscapes at the roadside in large citiesare the nature of physical landscapes. To Venturi, the façades of buildings at the roadside are a part of signage such as traffic lights and road signs, and those façades have the meaning of symbolic systems beyond simple physical landscapes. To A. Rossi, types of buildings as physical townscapes are a key role supporting raw data of classification in architecture. And also, those types have significance of the basic data shedding light on the principles and history of cities. For intangible factors in R. Venturi's architecture, daily routine, function and use, time, a use for a building and others form complex architecture. And also, those factors describe shared values of the same period as the façades of buildings and complex symbols and formative lexicons in metaphorical terms. For A. Rossi's intangible factors, 'collective memory' is buried in inhabitants of the city, and with that, the city is a place for memory to its inhabitants. What is more, cities' monuments have intangible landscapes like 'sustainability', 'permanence' and so on. With lots of events happening throughoutcities, those monuments are the whole images of cities giving the value to the urban buildings that reside in cities. Finally, R. Venturi's all-encompassing complex architecture concept was extended on a tangible and intangible point of townscapes. It was found that A. Rossi's tangible thought was formed from the whole landscape of historic cities in then Italy as the background of time and place. Also, With types of urban buildings and 'collective memory', A. Rossi drew architectural norms and formats of unchangeable types.

• Journal of Environmental Science International
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• v.19 no.3
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• pp.281-293
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• 2010

The present study explores time and spatial thermal environment for Daegu, which is a city built on a basin area, according to varying land cover conditions of the earth's surface by analyzing data derived from meteorological observation and satellite images. The study has classified land use by utilizing MODIS satellite images and analyzed land surface temperature. Also, by using data acquired from automatic weather system, the study has evaluated the effects of atmospheric heating caused by city pavements by analyzing the sensible heat flux between the city's land surface and the atmosphere. The results are as follows. 1) Classification of land use in the Daegu area shows 46.64% of urban and built-up area, 1.39% of watersides, 35.19% of forest, 11.43% of crops, and 5.37% grasslands. 2) During the weekdays throughout the year, the land surface temperature was high for Dalseogu, Bukgu, and Seogu regions where industrial complexes could be found. Comparatively, lower temperature could be observed in the woodlands. 3) While the land surface temperature displayed the effects of pushing air upwards during the weekdays in urban areas, the reverse was true for forest regions. During the night, the temperature did not exert any significant influence on air movement.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.26 no.4
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• pp.341-350
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• 2008

Change detection has been recognized as one of the most important steps to update city models. In this study, we thus propose a method to detect urban changes from two sets of LIDAR data acquired at different times. The main processes in the proposed method are (1) detecting change areas through subtraction between two DSMs generated from the LIDAR sets, (2) organizing the LIDAR points within the detected areas into surface patches, (3) classifying the class of each patch such as ground, vegetation, and building, and (4) determining the kinds of changes based on the properties and classes of the patches. The results which were obtained from the application of the proposed method to real data were verified as appropriate using the reference data manually acquired from the visual inspection of the orthoimages of the same area. The probability of success in change detection is assessed to 97% on an average. In conclusion, the proposed method is evaluated as a reliable, and efficient approach to change detection and thus the update of city model.

• Journal of the Korean Institute of Landscape Architecture International Edition
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• no.2
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• pp.102-108
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• 2004

In the city of Asia like Seoul and Tokyo, aggregate power is poured into the city making which is suitable in new age. Large-scale re-development is active in various places as the present condition, As for the various East Asian cities, high-level utilization conversion of the land and high conversion of the building are advanced. As for these realities, the re-development is advanced in the situation that has not been clarified enough though it is true that green of the city has decreased. This research was aimed at the clarifying the allocation of greenery when proceeding with town planning projects by quantifying the possibility of allocating greenery in the form of rooftop landscaping in redeveloped areas. Moreover, the distribution of the green in the re-development region was calculated and the coexistence level of green space was calculated, The realities of a desirable green were clarified in the spectacle by understanding a sight and spatial continuousness in the green. In order to clarify the potentiality of roof greenery revaluating, the research was conducted by analyzing from aerial photographs, in Tokyo and Seoul, And the flow of the research was conducted by selection of research object area, taking pictures, acquisition of the aerial photography, rearrangement the data and the analysis. As a result, I was able to prepare a land vegetation coverage classification chart and obtain data pertaining to the level of urban and green tract of land for the researched cities of Tokyo and Seoul. By analyzing this data, we were able to indicate patterns of greenery unable to be obtained through data from ratio of vegetation coverage and ascertain the effectiveness of rooftop landscaping.

• Korean Journal of Agricultural Science
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• v.47 no.4
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• pp.933-940
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• 2020

Land-use change matrix data is important for calculating the LULUCF (land use, land use change and forestry) sector of the national greenhouse gas inventory. In this study, land cover changes in 2004 and 2019 were compared using the Wall-to-Wall technique with a land cover map of Sejong City from the Ministry of Environment. Sejong City was classified into six land use classes according to the Intergovernmental Panel on Climate Change (IPCC) guidelines: Forest land, crop land, grassland, wetland, settlement and other land. The coordinate system of the land cover maps of 2004 and 2019 were harmonized and the land use was reclassified. The results indicate that during the 15 years from 2004 to 2019 forestlands and croplands decreased from 50.4% (234.2 ㎢) and 34.6% (161.0 ㎢) to 43.4% (201.7 ㎢) and 20.7% (96.2 ㎢), respectively, while Settlement and Other land area increased significantly from 8.9% (41.1 ㎢) and 1.4% (6.9 ㎢) to 35.6% (119.0 ㎢) and 6.5% (30.3 ㎢). 79.㎢ of cropland area (96.2 ㎢) in 2019 was maintained as cropland, and 8.8 ㎢, 1.7 ㎢, 0.5 ㎢, 5.4 ㎢, and 0.4 ㎢ were converted from forestland, grassland, wetland, and settlement, respectively. This research, however, is subject to several limitations. The uncertainty of the land use change matrix when using the wall-to-wall technique depends on the accuracy of the utilized land cover map. Also, the land cover maps have different resolutions and different classification criteria for each production period. Despite these limitations, creating a land use change matrix using the Wall-to-Wall technique with a Land cover map has great advantages of saving time and money.

• Journal of Digital Convergence
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• v.20 no.5
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• pp.39-46
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• 2022

As the link between traffic accidents and social and economic losses has been confirmed, there is a growing interest in developing safety policies based on crash data and a need for countermeasures to reduce severe crash outcomes such as severe injuries and fatalities. In this study, we select Daejeon city where the relative proportion of fatal crashes is high, as a case study region and focus on the severity of pedestrian crashes. After a series of data manipulation process, we run machine learning algorithms for the optimal model selection and variable identification. Of nine algorithms applied, AdaBoost and Random Forest (ensemble based ones) outperform others in terms of performance metrics. Based on the results, we identify major influential factors (i.e., the age of pedestrian as 70s or 20s, pedestrian crossing) on pedestrian crashes in Daejeon, and suggest them as measures for reducing severe outcomes.

• Journal of Korea Multimedia Society
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• v.25 no.8
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• pp.1167-1174
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• 2022

With the sustainable development and transformation of the city, public art as a business card of the famous city of culture has become a hot topic of research. The intervention of public art in public space not only brings users a sense of space experience, but also becomes a unique carrier of urban and rural image making. Although there is much research on the classification, aesthetics and function of public art, there is few quantitative research on user satisfaction. This paper takes the basic features of airport public art as a research object and the basic features of airport public art as the theoretical basis to study the impact of the basic characteristics of airport public art on user satisfaction. Research methods were based on questionnaire data of 247 people, in which models and hypotheses were tested using SPSS 21.0 software, based on the induction and extraction of nine influential factors in the basic characteristics of public art. The study found that public interpretation, media patterns, color perception, modeling form, place perception, city image and memory have significant positive effects on user satisfaction. The sharedness of public art, cognition and communication in public culture and spatial relations do not affect satisfaction. Conclusion, inspiration and prospect provide suggestions for designers and reference data and theoretical support for public art evaluation.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.95-100
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• 2023

Due to Traffic accidents people faces health and economical casualties around the world. As the population increases vehicles on road increase which leads to congestion in cities. Congestion can lead to increasing accident risks due to the expansion in transportation systems. Modern cities are adopting various technologies to minimize traffic accidents by predicting mathematically. Traffic accidents cause economical casualties and potential death. Therefore, to ensure people's safety, the concept of the smart city makes sense. In a smart city, traffic accident factors like road condition, light condition, weather condition etcetera are important to consider to predict traffic accident severity. Several machine learning models can significantly be employed to determine and predict traffic accident severity. This research paper illustrated the performance of a hybridized neural network and compared it with other machine learning models in order to measure the accuracy of predicting traffic accident severity. Dataset of city Leeds, UK is being used to train and test the model. Then the results are being compared with each other. Particle Swarm optimization with artificial neural network (PSO-ANN) gave promising results compared to other machine learning models like Random Forest, Naïve Bayes, Nearest Centroid, K Nearest Neighbor Classification. PSO- ANN model can be adopted in the transportation system to counter traffic accident issues. The nearest centroid model gave the lowest accuracy score whereas PSO-ANN gave the highest accuracy score. All the test results and findings obtained in our study can provide valuable information on reducing traffic accidents.

• Journal of Korea Water Resources Association
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• v.56 no.4
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• pp.261-272
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• 2023

In recent years, natural disasters such as heavy rainfall and typhoons have occurred more frequently, and their severity has increased due to climate change. The Korea Meteorological Administration (KMA) currently uses the same criteria for all regions in Korea for watch and warning based on the maximum cumulative rainfall with durations of 3-hour and 12-hour to reduce damage. However, KMA's criteria do not consider the regional characteristics of damages caused by heavy rainfall and typhoon events. In this regard, it is necessary to develop new criteria considering regional characteristics of damage and cumulative rainfalls in durations, establishing four stages: blue, yellow, orange, and red. A classification model, called DSCM (Disaster Severity Classification Model), for the four-stage disaster severity was developed using four machine learning models (Decision Tree, Support Vector Machine, Random Forest, and XGBoost). This study applied DSCM to local governments of Seoul, Incheon, and Gyeonggi Province province. To develop DSCM, we used data on rainfall, cumulative rainfall, maximum rainfalls for durations of 3-hour and 12-hour, and antecedent rainfall as independent variables, and a 4-class damage scale for heavy rain damage and typhoon damage for each local government as dependent variables. As a result, the Decision Tree model had the highest accuracy with an F1-Score of 0.56. We believe that this developed DSCM can help identify disaster risk at each stage and contribute to reducing damage through efficient disaster management for local governments based on specific events.