• The Journal of Bigdata
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• v.8 no.2
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• pp.233-248
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• 2023

In the process of building the future of smart cities, innovation in mobility and road infrastructure is one of the most important topics. In particular, with the proliferation of autonomous vehicles and various types of mobility on the road, such as electric bicycles, electric kickboards, and electric wheels, roads have a variety of actors to accommodate, including traditional cars and pedestrians, and conflicts between them need to be resolved. Complete streets, a term coined in the United States in 2003, refers to the design and operation of roads that consider the equitable safety and convenience of all road users, including pedestrians, bicyclists, public transportation users, personal mobility (PM) users, and automobile drivers. Currently, many cities overseas are implementing complete streets, and research is being actively conducted to institutionalize them. However, there is a lack of research and discussion on complete streets in Korea. Therefore, this study aims to formalize the main factors to be considered in the design of complete streets by collecting and analyzing the opinions of academic and practitioner experts through the Delphi method. A total of three Delphi surveys were conducted, collecting free responses from experts through the first open-ended survey and organizing them into keywords to create the second and third closed-ended surveys. The second and third rounds of the survey consisted of a total of 52 questions, and 34 items out of 52 were selected as the final factors.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.65-82
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• 2015

Recently, there has been a surge of interest in finding core issues and analyzing emerging trends for the future. This represents efforts to devise national strategies and policies based on the selection of promising areas that can create economic and social added value. The existing studies, including those dedicated to the discovery of future promising fields, have mostly been dependent on qualitative research methods such as literature review and expert judgement. Deriving results from large amounts of information under this approach is both costly and time consuming. Efforts have been made to make up for the weaknesses of the conventional qualitative analysis approach designed to select key promising areas through discovery of future core issues and emerging trend analysis in various areas of academic research. There needs to be a paradigm shift in toward implementing qualitative research methods along with quantitative research methods like text mining in a mutually complementary manner. The change is to ensure objective and practical emerging trend analysis results based on large amounts of data. However, even such studies have had shortcoming related to their dependence on simple keywords for analysis, which makes it difficult to derive meaning from data. Besides, no study has been carried out so far to develop core issues and analyze emerging trends in special domains like the aviation industry. The change used to implement recent studies is being witnessed in various areas such as the steel industry, the information and communications technology industry, the construction industry in architectural engineering and so on. This study focused on retrieving aviation-related core issues and emerging trends from overall research papers pertaining to aviation through text mining, which is one of the big data analysis techniques. In this manner, the promising future areas for the air transport industry are selected based on objective data from aviation-related research papers. In order to compensate for the difficulties in grasping the meaning of single words in emerging trend analysis at keyword levels, this study will adopt topic analysis, which is a technique used to find out general themes latent in text document sets. The analysis will lead to the extraction of topics, which represent keyword sets, thereby discovering core issues and conducting emerging trend analysis. Based on the issues, it identified aviation-related research trends and selected the promising areas for the future. Research on core issue retrieval and emerging trend analysis for the aviation industry based on big data analysis is still in its incipient stages. So, the analysis targets for this study are restricted to data from aviation-related research papers. However, it has significance in that it prepared a quantitative analysis model for continuously monitoring the derived core issues and presenting directions regarding the areas with good prospects for the future. In the future, the scope is slated to expand to cover relevant domestic or international news articles and bidding information as well, thus increasing the reliability of analysis results. On the basis of the topic analysis results, core issues for the aviation industry will be determined. Then, emerging trend analysis for the issues will be implemented by year in order to identify the changes they undergo in time series. Through these procedures, this study aims to prepare a system for developing key promising areas for the future aviation industry as well as for ensuring rapid response. Additionally, the promising areas selected based on the aforementioned results and the analysis of pertinent policy research reports will be compared with the areas in which the actual government investments are made. The results from this comparative analysis are expected to make useful reference materials for future policy development and budget establishment.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.185-209
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• 2023

With the advent of the 4th industrial revolution, various technologies have received much attention. Technologies related to the 4th industry include the Internet of Things (IoT), big data, artificial intelligence, virtual reality (VR), 3D printers, and robotics, and these technologies are often converged. In particular, the robotics field is combined with technologies such as big data, artificial intelligence, VR, and digital twins. Accordingly, much research using robotics is being conducted, which is applied to distribution, airports, hotels, restaurants, and transportation fields. In the given situation, research on human-robot interaction is attracting attention, but it has not yet reached the level of user satisfaction. However, research on robots capable of perfect communication is steadily being conducted, and it is expected that it will be able to replace human emotional labor. Therefore, it is necessary to discuss whether the current human-robot interaction technology can be applied to business. To this end, this study first examines the trend of human-robot interaction technology. Second, we compare LDA (Latent Dirichlet Allocation) topic modeling and BERTopic topic modeling methods. As a result, we found that the concept of human-robot interaction and basic interaction was discussed in the studies from 1992 to 2002. From 2003 to 2012, many studies on social expression were conducted, and studies related to judgment such as face detection and recognition were conducted. In the studies from 2013 to 2022, service topics such as elderly nursing, education, and autism treatment appeared, and research on social expression continued. However, it seems that it has not yet reached the level that can be applied to business. As a result of comparing LDA (Latent Dirichlet Allocation) topic modeling and the BERTopic topic modeling method, it was confirmed that BERTopic is a superior method to LDA.

• Journal of The Korean Association of Information Education
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• v.24 no.1
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• pp.21-28
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• 2020

Technological advancements such as artificial intelligence, robots, and big data are revolutionizing the entire society. In this paper, we analyzed preliminary teachers' daily internet/gaming/smartphone habit and the difference between preference factors in gender and diagnosis group in the situation of ethical dilemma in driverless cars. The result shows most of the male students are in high risk group of daily internet/gaming usage, and male students tend to be more immersed in games compared to female students, which negatively affects their daily lives. Students who have at least one of the daily internet/gaming/smartphone habits are more likely to be classified as high-risk group in all three of daily internet/gaming/smartphone habit. Fortunately, the students perceived themselves addicted and wanted change their habits. An analysis by a moral machine of these students tells that there is no significant difference in preference between male and female students and among diagnosis groups. However, specifically in the ethical dilemma of driverless cars, all the groups of male, female, normal, high-risk showed they have priority in pedestrians over drivers, a large number of people over small, and people who obey traffic rules over who do not. The tendency was pronounced in female group and high-risk students prioritized people who are older and in lower social status.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.11
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• pp.469-478
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• 2019

Car-sharing services have been settled on as a new type of public transportation owing to their enhanced convenience, expanded awareness of practical consumption patterns, the inspiration for environmental conscientiousness, and the diffusion of smart phones following the economic crisis. With development of the market, many people have started using such services. However, security is still an issue. Damage is expected since IDs and passwords are required for log-in when renting and controlling the vehicles. The protocol suggested in this study uses bio-information, providing an optimized service, and convenient (but strong) authentication with various service-provider clouds registering car big data about users through brokers. If using the techniques suggested here, it is feasible to reduce the exposure of the bio-information, and to receive service from multiple service-provider clouds through one particular broker. In addition, the proposed protocol reduces public key operations and session key storage by 20% on mobile devices, compared to existing car-sharing platforms, and because it provides convenient, but strong, authentication (and therefore constitutes a secure channel), it is possible to proceed with secure communications. It is anticipated that the techniques suggested in this study will enhance secure communications and user convenience in the future car-sharing-service cloud environment.

• The Journal of the Convergence on Culture Technology
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• v.6 no.2
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• pp.437-447
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• 2020

The purpose of this study is to research the countermeasures and expected effects through the use of drones in the field of disaster prevention as a drone-based smart quarantine performance method. The environmental, market, and technological approaches to the review of the current quarantine performance task and its countermeasures are as follows. First, in terms of the environment, the effectiveness of the quarantine performance business using drone-based control is to broaden the utilization of forest, bird flu, livestock, facility areas, mosquito larvae, pests, and to simplify and provide various effective prevention systems such as AI and cholera. Second, in terms of market, the standardization of livestock and livestock quarantine laws and regulations according to the use of disinfection and quarantine missions using domestic standardized drones through the introduction of new technologies in the quarantine method, shared growth of related industries and discovery of new markets, and animal disease prevention It brings about the effect of annual budget savings. Third, the technical aspects are (1) on-site application of disinfection and prevention using multi-drone, a new form of animal disease prevention, (2) innovation in the drone industry software field, and (3) diversification of the industry with an integrated drone control / control system applicable to various markets. (4) Big data drone moving path 3D spatial information analysis precise drone traffic information ensures high flight safety, (5) Multiple drones can simultaneously auto-operate and fly, enabling low-cost, high-efficiency system deployment, (6) High precision that this was considered due to the increase in drone users by sector due to the necessity of airplane technology. This study was prepared based on literature surveys and expert opinions, and the future research field needs to prove its effectiveness based on empirical data on drone-based services. The expected effect of this study is to contribute to the active use of drones for disaster prevention work and to establish policies related to them.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.14 no.6
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• pp.69-76
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• 2015

Although traffic information has been actively analyzed using big data, it has not been used as much with the consideration of driver characteristics. Among the various types of information, road signs can directly affect the driver. Road signs must provide the optimal information that enables drivers to reach their destinations with ease as well as information suitable for navigation systems. However, present road sign rules provide standardized information, regardless of the road type or size. This study suggests a method for providing road information that will help drivers determine their behavior. First, the minimum character size that can be used on a road sign for each design speed was obtained with respect to the visibility and decipherability of a road sign. Instead of conventional diagram-based direction guidance, a scenario using split-based direction guidance was created. To verify the effectiveness of the provided information, a three-dimensional simulated road environment was constructed, and a driving simulator was used for the test. At a simple plane intersection, the driver was not greatly influenced by directional guidance, but at a complex, three-dimensional intersection, the driver preferred summary-based directional guidance, which is instinctive guidance, over diagram-based guidance. On the basis of the test results, a secondary verification test that applied split-based guidance at a three-dimensional intersection confirmed that the driver had no problems in making decisions.

• Journal of Digital Convergence
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• v.19 no.10
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• pp.241-246
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• 2021

Although there have been many studies on laws and systems for the proliferation of autonomous vehicles, studies on the activation of autonomous vehicles from a strategic perspective are insufficient. This study examines the issues and problem solving methods of autonomous vehicles. Based on this, plans to activate autonomous vehicles from a strategic point of view are proposed. In order to solve the issues and problems of autonomous vehicles, it is necessary to clearly establish legal and institutional standards based on the reinforcement of the safety of autonomous vehicles. In the event of a traffic accident, who is responsible for the accident and responsibility for compensation should be prioritized. Diffusion strategies are established according to the level of autonomous driving for the activation of autonomous vehicles in strategic perspective. In addition, governmental support policies should be used as triggers for initial activation, and marketing mix strategies should be implemented based on segmentation, targeting, and positioning strategies.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.2
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• pp.24-35
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• 2019

In this study, a hotspot analysis was conducted to suggest a new method for interpreting soil heavy-metal contamination data of abandoned metal mines according to statistical significance level. The spatial autocorrelation of the data was analyzed using the Getis-Ord $Gi{\ast}$ statistic in order to check whether soil heavy metal contamination data showing abnormal values appeared concentrated or dispersed in a specific space. As a result, the statistically significant data showing abnormal values in the mine area could be classified as follows: (1) the contamination degree and the hotspot value (z-score) were both high, (2) the contamination degree was high but the z-score was low, (3) the contamination degree was low but the z-score was high and (4) the contamination degree and the z-score were both low. The proposed method can be used to interpret the soil heavy metal contamination data according to the statistical significance level and to support a rational decision for soil contamination management in abandoned mines.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.1
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• pp.1-18
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• 2019

This research measured and visualized the spatial dependency and the spatial heterogeneity of the small business in Cheonan-si, Asan-si with $100m{\times}100m$ grids based on global and local spatial autocorrelation. First, we confirmed positive spatial autocorrelation of small business in the research area using Moran's I Index, which is ESDA(Exploratory Spatial Data Analysis). And then, through Getis-Ord $GI{\ast}$, one kind of LISA(Local Indicators of Spatial Association), local patterns of spatial autocorrelation were visualized. These verified that Spatial Regression Model is valid for the location factor analysis on small business commercial buildings. Next, GWR(Geographically Weighted Regression) was used to analyze the spatial relations between the distribution of small business, hourly mobile traffic-based floating population, land use attributes index, residence, commercial building, road networks, and the node of traffic networks. Final six variables were applied and the accessibility to bus stops, afternoon time floating population, and evening time floating population were excluded due to multicollinearity. By this, we demonstrated that GWR is statistically improved compared to OLS. We visualized the spatial influence of the individual variables using the regression coefficients and local coefficients of determinant of the six variables. This research applied the measured population information in a practical way. Reflecting the dynamic information of the urban people using the commercial area. It is different from other studies that performed commercial analysis. Finally, this research has a differentiated advantage over the existing commercial area analysis in that it employed hourly changing commercial service population data and it applied spatial statistical models to micro spatial units. This research proposed new framework for the commercial analysis area analysis.