• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.3
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• pp.129-141
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• 2019

In recent years, fast growing cities in Korea are showing signs of being vulnerable to more disasters as their population and facilities increase and intensify. In particular, fire is one of the most common disasters in Korea's cities, along with traffic accidents. Therefore, in this study, we analyze what type of factors affect the fire that threatens urban people. Fire data were acquired for 10 years, from 2007 to 2017, in Jinju, Korea. Spatial distribution pattern of fire occurrence in Jinju was assessed through the spatial autocorrelation analysis. First, spatial autocorrelation analysis was carried out to grasp the spatial distribution pattern of fire occurrence in Jinju city. In addition, correlation and multiple regression analysis were used to confirm spatial dependency and abnormality among factors. Based on this, OLS (Ordinary Least Square) regression analysis was performed using space weighting considering fire location and spatial location of each facility. As a result, First, LISA (Local Indicator of Spatial Association) analysis of the occurrence of fire in Jinju shows that the most central commercial area are fire department, industrial area, and residential area. Second, the OLS regression model was analyzed by applying spatial weighting, focusing on the most derived factors of multiple regression analysis, by integrating population and social variables and physical variables. As a result, the second kind of neighborhood living facility showed the highest correlation with the fire occurrence, followed by the following in the order of single house, sales facility, first type of neighborhood living facility, and number of households. The results of this study are expected to be useful for analyzing the fire occurrence factors of each facility in urban areas and establishing fire safety measures.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.6
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• pp.25-39
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• 2018

The purpose of this study is to develop an efficient incident detection algorithm by applying machine learning, which is being widely used in the transport sector. As a first step, network of the target site was constructed with micro-simulation model. Secondly, data has been collected under various incident scenarios produced with combination of variables that are expected to affect the incident situation. And, detection results from both McMaster algorithm, a well known incident detection algorithm, and the Naive Bayes algorithm, developed in this study, were compared. As a result of comparison, Naive Bayes algorithm showed less negative effect and better detect rate (DR) than the McMaster algorithm. However, as DR increases, so did false alarm rate (FAR). Also, while McMaster algorithm detected in four cycles, Naive Bayes algorithm determine the situation with just one cycle, which increases DR but also seems to have increased FAR. Consequently it has been identified that the Naive Bayes algorithm has a great potential in traffic incident detection.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.9
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• pp.188-197
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• 2019

This study analyzed empirically the effects of the perception of outbound passengers, who flew from Korea to overseas tourist destinations, on the cabin service quality, tourist experience, and tourist satisfaction. For empirical analysis, a survey was given to Korean passengers using a national carrier of South Korea. Based on empirical analysis, the researchers judged that the analysis results and implications could be applied to inbound foreign passengers who visited Korea for the tourism purposes if researchers generated significant results from empirical analysis. This paper designed a research model that represented the meaningful relationship among, airline cabin-service quality, tourist experience, and tourist satisfaction following preceding research. According to the analysis, that factors that consisted of the cabin service quality had a significant effect on the tourist satisfaction with a mediating effect of both the cognitive and emotional tourist experience. Therefore, the perception on airline's cabin service quality of the national carrier would be a starting point that can help improve the tourist satisfaction.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.5
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• pp.64-78
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• 2019

This paper explores key factors affecting taxi travel using global positioning system(GPS) data in Seoul, Korea, considering spatial dependence. We first analyzed the travel characteristics of taxis such as average travel time, average travel distance, and spatial distribution of taxi trips according to the time of the day and the day of the week. As a result, it is found that the most taxi trips were generated during the morning peak time (8 a.m. to 9 a.m.) and after the midnight (until 1 a.m.) on weekdays. The average travel distance and travel time for taxi trips were 5.9 km and 13 minutes, respectively. This implies that taxis are mainly used for short-distance travel and as an alternative to public transit after midnight in a large city. In addition, we identified that taxi trips were spatially correlated at the traffic analysis zone(TAZ) level through the Moran's I test. Thus, spatial regression models (spatial-lagged and spatial-error models) for taxi trips were developed, accounting for socio-demographics (such as the number of households, the number of elderly people, female ratio to the total population, and the number of vehicles), transportation services (such as the number of subway stations and bus stops), and land-use characteristics (such as population density, employment density, and residential areas) as explanatory variables. The model results indicate that these variables are significantly associated with taxi trips.

• Journal of Digital Convergence
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• v.19 no.3
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• pp.55-64
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• 2021

Public libraries are a space where residents learn a wide range of knowledge and ideologies, and as they are directly connected to life, various related studies have been conducted. In most previous studies, variables such as population, traffic accessibility, and environment were found to be highly relevant to library use. In this study, it can be said that the difference from previous studies is that the book borrow demand and relevance were analyzed by reflecting the variables of cultural characteristics based on the book borrow history (1,820,407 cases) and member information (297,222 persons). As a result of the analysis, it was analyzed that as the increase in borrows for social science and literature books compared to technical science books, the demand for book borrows increased. In addition, various descriptive statistical analyzes were used to analyze the characteristics of library book borrow demand, and policy implications and limitations of the study were also presented based on the analysis results. and considering that cultural characteristics change depending on the location and time of day, it is believed that related research should be continued in the future.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.50 no.1
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• pp.59-66
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• 2022

In order to solve the problem of urban traffic congestion, Urban Air Mobility (UAM) concept using Electric Vertical Take-off and Landing (eVTOL) aircraft has been gaining popularity, and many domestic and international studies are underway. However, since these aircraft inevitably fly over densely populated areas, it is essential to ensure safety, which starts with accurately analyzing the crash risk. In this paper, the locations and impact speeds of crash are computed using six degree-of-freedom simulations of an eVTOL aircraft in a fixed-wing mode. System malfunction was modeled by a sudden loss of thrust with control surfaces being stuck during cruise. Because most of these eVTOL aircraft are still under development, a methodology of constructing a six degree-of-freedom dynamics model from generic specification is also developed. The results show that the crash locations are highly concentrated right under the aircraft within a square that has an edge length similar to the cruise altitude. Speed distribution is more complicated because almost identical crash locations can be achieved by two very different paths resulting in a large variation in the speeds.

• Journal of Digital Convergence
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• v.20 no.4
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• pp.469-482
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• 2022

The purpose of this study is to analyze regional variation factors of hypertension treatment rate in COVID-19 based on the analysis results based on ecological methodology. To this end, data suitable for ecological analysis were collected from the Korea Centers for Disease Control and Prevention's regional health statistics, local government COVID-19 confirmed cases, National Health Insurance Corporation, Health Insurance Review and Assessment Service's welfare statistics, and Korea Transport Institute's traffic access index. Descriptive statistics and correlation analysis were conducted using SPSS Statistics 23 for regional variation and related factors in hypertension treatment rate, and geographical weighted regression analysis was conducted using Arc GIS for regional variation factors. As a result of the study, the overall explanatory power of the calculated geo-weighted regression model was 27.6%, distributed from 23.1% to 33.4% by region. As factors affecting the treatment rate of hypertension, the higher the rate of basic living security medical benefits, diabetes treatment rate, and health institutions per 100,000 population, the higher the rate of hypertension treatment, the lower the number of COVID-19 confirmed patients, the lower the rate of physical activity, and the alcohol consumption. Percentage of alcohol consumption decreased due to COVID-19 pandemic. It was analyzed that the lower the ratio, the higher the treatment rate for hypertension. Based on these results, the analysis of regional variables in the treatment rate of hypertension in COVID-19 can be expected to be effective in managing the treatment rate of hypertension, and furthermore, it is expected to be used to establish community-centered health promotion policies.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.3
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• pp.379-389
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• 2022

The number of public transportation users has dropped drastically due to COVID-19. In this work, my survey was conducted to uncover the factors that influence citizens' travel patterns. Data were collected and logistic regression analysis on the shifts in transportation was undertaken. Additionally, an importance-performance analysis was carried out to investigate how to effectively operate public transportation systems and improve facilities. The main research findings were as follows: First, the more individuals were concerned about COVID-19 (+) and being infected when using public transportation (+), the greater the tendency to switch to private transportation modes. Secondly, when it came to personal traits, respondents who could drive a car (+) or owned a car (+)or did more online shopping (+) or used public transportation for trips (+) tended to switch over, compared with respondents who could not drive or did not own a caror used public transportation to commute. In addition, respondents who were vaccinated (-) or had more household members tended not to switch transportation modes, compared with those who were not vaccinated or had fewer household members. Third, it is important to continue the following efforts to safeguardhygiene linked to public transportation: wearing masks, disinfecting hands, controlling diseases, and general cleaning. The conclusion was that it is important to put traffic congestion and ventilation issues first, especially in regards public transportation, which was not rated as satisfactory enough compared to its importance. The research findings can provide useful basic data when establishing countermeasures to the current COVID-19 circumstances in the areas of public transportation operation and management and in the event of an infectious disease outbreak in the future.

• Smart Media Journal
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• v.11 no.11
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• pp.49-62
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• 2022

Recently, with the development of Smart City Solutions such as Big data, AI, IoT, Autonomous driving, and Digital twins around the world, the proliferation of various smart devices and social media, and the record of the deeds that people have left everywhere, the construction of Smart Cities using the "Big Data" environment in which so much information and data is produced that it is impossible to gauge the scale is actively underway. The Purpose of this study is to construct an objective and systematic analysis Model based on Big Data to improve the transportation convenience of citizens and formulate efficient policies in Urban Information and Public Transportation accessibility in sustainable Smart Cities following the 4th Industrial Revolution. It is also to derive the methodology of developing a Big Data-Based public transport accessibility and policy management Platform using a sustainable Urban Public DB and a Private DB. To this end, Detailed Living Areas made a division and the accessibility of basic living amenities of Gwangju Metropolitan City, and the Public Transportation system based on Big Data were analyzed. As a result, it was Proposed to construct a Big Data-based Urban Information and Public Transportation accessibility Platform, such as 1) Using Big Data for public transportation network evaluation, 2) Supporting Transportation means/service decision-making based on Big Data, 3) Providing urban traffic network monitoring services, and 4) Analyzing parking demand sources and providing improvement measures.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.1D
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• pp.1-10
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• 2008

Recently, the Seoul Metropolitan Government (SMG) has initiated several urban redevelopment projects to revitalize the downtown well equipped for transit oriented development (TOD). Since, TOD should incur higher density development in our context, it has negative impacts on travel patterns, congestion, and urban environmental quality. The purpose of this study is to develop new transportation service index which can facilitate higher density TOD. This study includes relevant foreign case studies, the development of multimodal transportation index, and the impact analysis of TOD when it is applied in the downtown Seoul. In chapter III, it developed a so-called ITLOS, new multimodal transportation service index which shows the possibility of accommodating further development by integrating roadway service index with public transportation service index. The study sets ten policy scenarios by varying densities, and run the Seoul Congestion Management Model (SECOMM) to estimate the sustainable transportation impacts of TOD in the downtown. Travel speed index that only represents the availability of road capacity for development reveal that higher density development in the downtown can deteriorate traffic congestion while improving region-wide transportation level of service in Seoul. Also, it is proved that higher density development is more feasible when using ITLOS as the index because it considers not only available road capacity but subway capacity in the analysis area.