• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.3
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• pp.42-52
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• 2022

Bus Information System (BIS) services are expanding nationwide to small and medium-sized cities, including large cities, and user satisfaction is continuously improving. In addition, technology development related to improving reliability of bus arrival time and improvement research to minimize errors continue, and above all, the importance of information accuracy is emerging. In this study, accuracy performance was evaluated using LSTM, a machine learning method, and compared with existing methodologies such as Kalman filter and neural network. As a result of analyzing the standard error for the actual travel time and predicted values, it was analyzed that the LSTM machine learning method has about 1% higher accuracy and the standard error is about 10 seconds lower than the existing algorithm. On the other hand, 109 out of 162 sections (67.3%) were analyzed to be excellent, indicating that the LSTM method was not entirely excellent. It is judged that further improved accuracy prediction will be possible when algorithms are fused through section characteristic analysis.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.3
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• pp.173-185
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• 2022

For the smooth implementation and success of smart city projects, it is necessary to recognize that there is a difference in the perception of value judgments or strategic goals among major stakeholders in the planning process. And it is necessary to aim the values and goals of smart cities through reconciliation of these differences. The two major stakeholders in the smart city development project are citizens group and government officials group. Government officials are in charge of establishing and implementing policies for smart city projects, and their value judgments and perceptions influence the policy direction. In these respects, government officials can be an important stakeholder group. Citizens are a group that includes ordinary residents and business owners who live in smart cities and are the ultimate users of infrastructure and facilities. This study investigated the importance perceptions of citizens and government officials, who are the major stakeholders, about the core values and strategic goals that the smart city project aims. Responses were collected using a structured questionnaire to which the AHP methodology was applied. And the priority of perceptions for constituent items was compared for each stakeholder group. Through the comparative analysis results, it was empirically confirmed that there is a difference in the values and goals pursued by the smart city project between stakeholder groups. As an empirical study on the stakeholders of the smart city project, this study is meaningful in contributing to the theoretical development in that it suggests that the conceptual structural model of the smart city strategy system presented in previous studies can be applied in practice.

• Journal of Information Technology Services
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• v.21 no.3
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• pp.63-72
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• 2022

The extinction crisis of local cities, caused by a population density increase phenomenon in capital regions, directly causes the increase of vacant houses in local cities. According to population and housing census, Gunsan-si has continuously shown increasing trend of vacant houses during 2015 to 2019. In particular, since Gunsan-si is the city which suffers from doughnut effect and industrial decline, problems regrading to vacant house seems to exacerbate. This study aims to provide a foundation of a system which can predict and deal with the building that has high risk of becoming vacant house through implementing a data driven vacant house prediction machine learning model. Methodologically, this study analyzes three types of machine learning model by differing the data components. First model is trained based on building register, individual declared land value, house price and socioeconomic data and second model is trained with the same data as first model but with additional POI(Point of Interest) data. Finally, third model is trained with same data as the second model but with excluding water usage and electricity usage data. As a result, second model shows the best performance based on F1-score. Random Forest, Gradient Boosting Machine, XGBoost and LightGBM which are tree ensemble series, show the best performance as a whole. Additionally, the complexity of the model can be reduced through eliminating independent variables that have correlation coefficient between the variables and vacant house status lower than the 0.1 based on absolute value. Finally, this study suggests XGBoost and LightGBM based machine learning model, which can handle missing values, as final vacant house prediction model.

• Journal of Industrial Convergence
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• v.20 no.8
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• pp.25-31
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• 2022

This study examines the impact of residential environmental satisfaction on AIP(Aging in Place), and analyzes how their relationship differs depending on housing characteristic variables (ownership type, housing type, and residential area). For this purpose, as of November to December 2020, a questionnaire analysis was conducted on 373 adult males and females residing in Busan and Gyeongnam. The results of this study are summarized in two ways as follows. First, the higher the satisfaction with the residential environment, the higher the AIP. Second, it was analyzed that the positive relationship between satisfaction with the residential environment and AIP was higher in ownership than in rental cases, and further decreased in detached houses compared to apartment houses. In addition, compared to other regions, metropolitan cities or small and medium-sized cities had a higher positive (+) relationship between satisfaction with the residential environment and AIP. This study will provide important implications for policymaking related to population and urban planning.

• The Journal of the Convergence on Culture Technology
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• v.8 no.5
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• pp.525-534
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• 2022

The supply of housing can be understood through a close relationship with the urbanization phenomenon. Through industrialization and urbanization, many cities have implemented systems and policies for housing supply due to population concentration in cities, poor housing quality, and lack of housing. In the case of the Netherlands, the Housing Act was enacted in 1901 to improve the residential environment during the period when the population was concentrated in the city, and various efforts were made to expand the housing supply. Through this process, we understand the background of collective housing types in the Netherlands and analyze the application stages of contemporary residential housing planning in Rotterdam and Amsterdam. On the other hand, through the experience of Western society, we examine how to view the problem of multi-family housing types in our society, where the proportion of apartments is continuously increasing.

• Human Ecology Research
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• v.61 no.2
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• pp.233-245
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• 2023

The aim of this study was to identify community-level childcare infrastructure for infants and toddlers and to use the data to categorize community types using K-Means cluster analysis with spatial constraints. Seven indicators of childcare resource supply were used for the purpose of categorization and the results revealed six types of community cluster. Communities in the Type 1 cluster provided sufficient parks, libraries, and kindergartens, but lacked pediatric facilities and private education institutions. This cluster comprised small cities and rural areas in Gangwon-do, Gyeongsangbuk-do, Chungcheongbuk-do, and Jeollabuk-do. The Type 2 cluster had numerous pediatric facilities and childcare centers, but lacked other childcare infrastructure. This comprised small and medium-sized cities in Gyeonggi-do, some areas in Chungcheongnam-do, Chungcheongbuk-do, and Gangwon-do bordering Gyeonggi-do. The Type 3 cluster comprised Busan, Daegu, and Gyeongsangnam-do, but had insufficient childcare infrastructure as a whole. Type 4 had the largest number of childcare centers, libraries, and private education institutions and comprised Jeollabuk-do, areas near Gwangju, and Jeju-do. Type 5, consisting of Seoul, Incheon and the southern part of Gyeonggi-do had many pediatric facilities and certified childcare centers, but lacked other childcare infrastructure. Type 6, being the rural areas and islands in Jeollanam-do, had sufficient kindergartens, but other infrastructure was insufficient. These results are expected to provide local government with policy implications in terms of relieving the childcare burden on residents with infants and toddlers.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.164-164
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• 2023

This study investigates the importance of flood-influencing factors on the accuracy of flood risk mapping using the integration of remote sensing-based and machine learning techniques. Here, the Extreme Gradient Boosting (XGBoost) and Random Forest (RF) algorithms integrated with GIS-based techniques were considered to develop and generate flood risk maps. For the study area of NAPA County in the United States, rainfall data from the 12 stations, Sentinel-1 SAR, and Sentinel-2 optical images were applied to extract 13 flood-influencing factors including altitude, aspect, slope, topographic wetness index, normalized difference vegetation index, stream power index, sediment transport index, land use/land cover, terrain roughness index, distance from the river, soil, rainfall, and geology. These 13 raster maps were used as input data for the XGBoost and RF algorithms for modeling flood-prone areas using ArcGIS, Python, and R. As results, it indicates that XGBoost showed better performance than RF in modeling flood-prone areas with an ROC of 97.45%, Kappa of 93.65%, and accuracy score of 96.83% compared to RF's 82.21%, 70.54%, and 88%, respectively. In conclusion, XGBoost is more efficient than RF for flood risk mapping and can be potentially utilized for flood mitigation strategies. It should be noted that all flood influencing factors had a positive effect, but altitude, slope, and rainfall were the most influential features in modeling flood risk maps using XGBoost.

• 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.

• Economic and Environmental Geology
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• v.55 no.6
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• pp.701-716
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• 2022

This study investigated the distribution behaviors of PM2.5 and PM10 at two air quality monitoring sites, Go-eup (GO) and Backseokeup (BS), located in Yangju City, South Korea. The amounts of emissions sources of pollutants were analyzed based on the Clean Air Policy Support System (CAPSS), and the contribution rates of neighboring cities were enumerated in Yangju. Yangju has a geological basin structure, and it is a city with mixed urban and rural characteristics. The emission concentration of particulate matter was affected by geological and seasonal factors for all sites observed in this study. Therefore, these factors should be considered when establishing policies related to particulate matter. Because the official GO and BS station sites in Yangju are both situated in the southern part of the city, the representativeness of both stations was checked using correlation analysis for the measurement of PM2.5 and PM10 by considering two more sites-those of Bongyang-dong (BY) and the Gumjun (GJ) industrial complex. The data included discharge amounts for business types 4 and 5, which were not sufficiently considered in the CAPSS estimates. Because the 4 and 5 types of businesses represent over 92.6% of businesses in this city, they are workplaces in Yangju that have a significant effect on the total air pollutant emission. These types of businesses should be re-inspected as the main discharge sources in industry, and basic data accumulation should be carried out. Moreover, to manage the emission of particulate matter, attainable countermeasures for the main sources of these emissions should be prepared in a prioritized fashion; such countermeasures include prohibition of backyard burning, supervision of charcoal kilns, and management of livestock excretions and fugitive dust in construction sites and on roads. The contribution rates by neighboring cities was enumerated between 6.3% and 10.9% for PM2.5. Cooperation policies are thought to be required with neighboring cites to reduce particulate matter.

• Journal of the Economic Geographical Society of Korea
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• v.25 no.4
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• pp.547-567
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• 2022

In this study, I focus on makerspaces, which have rapidly spread since the late 2000s in the world's major cities. Makerspaces, born amid great social change, reflect the core characteristics of industrial space. I analyze the makerspace based on the theoretical perspectives of urbanism in the macroscopic aspect and placeness in the microscopic aspect. The urbanism of makerspaces is manifested through entry into the inner cities and their connections with urban capabilities. This means that convergence with innovation factors is becoming more important than optimization of factor costs and agglomeration economies in the locational determinants of industrial space. The placeness of makerspaces is being re-formed through an emphasis on taste, the expansion of autonomy, and the strengthening of connections. This reveals how the value creation process within the industrial space is changing, from forming-placelessness through standardization, uniformity, and compartmentalization to forming-placeness through restoration of individual humanity and interaction. The results of the urbanism and placenesss analysis carry implications for the present moment, when it is necessary to diversify the spatial planning of industrial spaces.