• Information Systems Review
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• v.25 no.1
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• pp.145-164
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• 2023

The proliferation of online reviews on dining experiences has significantly affected consumers' choices of restaurants, especially overseas. Food quality, service, ambiance, and price have been identified as specific attributes for the choice of a restaurant in prior studies. In addition to these four representative attributes, cultural factors, which may also significantly impact the choice of a restaurant for tourists, in particular, have not received much attention in previous studies. This study employs the text mining technique to analyze over 10,000 online reviews of 76 Korean restaurants posted by Chinese tourists on dianping.com to explore the influence of cultural factors on the consumer's choice of restaurants in the overseas travel context. The findings reveal that "Hallyu (Korean Wave)" influences Chinese tourists' dining experiences in Korea and their satisfaction. Moreover, Korean food-related words, such as cold noodle, bibimbap, rice cake, pig trotters, and kimchi stew, appeared across all the review topics. Our findings contribute to the existing tourism and hospitality literature by identifying the critical role of cultural factors on consumers', especially tourists', satisfaction with the choice of a restaurant using text mining. The findings also provide practical guidance to restaurant owners in Korea to attract more Chinese tourists.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.3 no.1 s.4
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• pp.45-52
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• 2004

There are many cities installing ITS(Intelligent Transportation Systems) and running TMC(Trafnc Management Center) to improve mobility and safety of roadway transportation by providing roadway information to drivers. There are many devices in ITS which collect real-time traffic data. We can obtain many valuable traffic data from the devices. But it's impossible to avoid missing traffic data for many reasons such as roadway condition, adversary weather, communication shutdown and problems of the devices itself. We couldn't do any secondary process such as travel time forecasting and other transportation related research due to the missing data. If we use the traffic data to produce AADT and DHV, essential data in roadway planning and design, We might get skewed data that could make big loss. Therefore, He study have explored some imputation techniques such as heuristic methods, regression model, EM algorithm and time-series analysis for the missing traffic volume data using some evaluating indices such as MAPE, RMSE, and Inequality coefficient. We could get the best result from time-series model generating 5.0$\%$, 0.03 and 110 as MAPE, Inequality coefficient and RMSE, respectively. Other techniques produce a little different results, but the results were very encouraging.

• Ecology and Resilient Infrastructure
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• v.8 no.4
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• pp.223-232
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• 2021

In general, stormwater flows to the road surface, especially in urban areas, and it is discharged through the drainage grate inlets on roads. The appropriate evaluation of the road drainage capacity is essential not only in the design of roads and inlets but also in the design of sewer systems. However, the method of road surface flow analysis that reflects the topographical and hydraulic conditions might not be fully developed. Therefore, the enhanced method of road surface flow analysis should be presented by investigating the existing analysis method such as the flow analysis module (uniform; varied) and the flow travel time (critical; fixed). In this study, the algorithm based on varied and uniform flow analysis was developed to analyze the flow pattern of road surface. The numerical analysis applied the uniform and varied flow analysis module and travel time as parameters were conducted to estimate the characteristics of rainfall-runoff in various road conditions using the developed algorithm. The width of the road (two-lane (6 m)) and the slope of the road (longitudinal slope of road 1 - 10%, transverse slope of road 2%, and transverse slope of gutter 2 - 10%) was considered. In addition, the flow of the road surface is collected from the gutter along the road slope and drained through the gutter in the downstream part, and the width of the gutter was selected to be 0.5 m. The simulation results were revealed that the runoff characteristics were affected by the road slope conditions, and it was found that the varied flow analysis module adequately reflected the gutter flow which is changed along the downstream caused by collecting of road surface flow at the gutter. The varied flow analysis module simulated 11.80% longer flow travel time on average (max. 23.66%) and 4.73% larger total road surface discharge on average (max. 9.50%) than the uniform flow analysis module. In order to accurately estimate the amount of runoff from the road, it was appropriate to perform flow analysis by applying the critical duration and the varied flow analysis module. The developed algorithm was expected to be able to be used in the design of road drainage because it was accurately simulated the runoff characteristics on the road surface.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.3
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• pp.407-413
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• 2020

Participating in exhibitions is one of the major activities for tourists. When selecting their next travel destination after participating in an event, they use map services and social network services, such as blogs, to obtain information about tourist attractions. The map services are location-based recommendations, because they can easily retrieve information regarding nearby places. Blogs contain informative content about tourist attractions, thereby providing content-based recommendations. However, few services consider both location and content. In location-based recommendations, tourist attractions that are not related to the content of the event attended might be recommended. Content-based recommendation has a disadvantage in that events located at a distance might get recommended. We propose an algorithm that considers both location and content, based on information from the Korea Tourism Organization's Linked Open Data (LOD), Wikipedia, and a Korean dictionary. By extracting nouns from the description of a tourist attraction and then comparing them with nouns about other attractions, a content-based relationship is determined. The distance to the event is calculated based on the latitude and longitude of each tourist attraction. A weight selected by the user is used for linear combination with the content-based relationship to determine the preference order of the recommendations.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.2060-2077
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• 2019

Recently, mobile healthcare services have attracted significant attention because of the emerging development and supply of diverse wearable devices. Smartwatches and health bands are the most common type of mobile-based wearable devices and their market size is increasing considerably. However, simple value comparisons based on accumulated data have revealed certain problems, such as the standardized nature of health management and the lack of personalized health management service models. The convergence of information technology (IT) and biotechnology (BT) has shifted the medical paradigm from continuous health management and disease prevention to the development of a system that can be used to provide ground-based medical services regardless of the user's location. Moreover, the IT-BT convergence has necessitated the development of lifestyle improvement models and services that utilize big data analysis and machine learning to provide mobile healthcare-based personal health management and disease prevention information. Users' health data, which are specific as they change over time, are collected by different means according to the users' lifestyle and surrounding circumstances. In this paper, we propose a prediction model of user physical activity that uses data characteristics-based long short-term memory (DC-LSTM) recurrent neural networks (RNNs). To provide personalized services, the characteristics and surrounding circumstances of data collectable from mobile host devices were considered in the selection of variables for the model. The data characteristics considered were ease of collection, which represents whether or not variables are collectable, and frequency of occurrence, which represents whether or not changes made to input values constitute significant variables in terms of activity. The variables selected for providing personalized services were activity, weather, temperature, mean daily temperature, humidity, UV, fine dust, asthma and lung disease probability index, skin disease probability index, cadence, travel distance, mean heart rate, and sleep hours. The selected variables were classified according to the data characteristics. To predict activity, an LSTM RNN was built that uses the classified variables as input data and learns the dynamic characteristics of time series data. LSTM RNNs resolve the vanishing gradient problem that occurs in existing RNNs. They are classified into three different types according to data characteristics and constructed through connections among the LSTMs. The constructed neural network learns training data and predicts user activity. To evaluate the proposed model, the root mean square error (RMSE) was used in the performance evaluation of the user physical activity prediction method for which an autoregressive integrated moving average (ARIMA) model, a convolutional neural network (CNN), and an RNN were used. The results show that the proposed DC-LSTM RNN method yields an excellent mean RMSE value of 0.616. The proposed method is used for predicting significant activity considering the surrounding circumstances and user status utilizing the existing standardized activity prediction services. It can also be used to predict user physical activity and provide personalized healthcare based on the data collectable from mobile host devices.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.12
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• pp.87-96
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• 2020

To use a hydraulic structure effectively, the velocity of a river should be known in detail. In reality, velocity measurements are not conducted sufficiently because of their high cost. The formulae to yield the flux and velocity of the river are commonly called the Manning and Chezy formulae, which are empirical equations applied to uniform flow. This study is based on Chiu (1987)'s paper using entropy theory to solve the limits of the existing velocity formula and distribution and suggests the velocity and distance formula derived from information entropy. The data of a channel having records of a spot's velocity was used to verify the derived formula's utility and showed R2 values of distance and velocity of 0.9993 and 0.8051~0.9483, respectively. The travel distance and velocity of a moving spot following the streamflow were calculated using some flow information, which solves the difficulty in frequent flood measurements when it is needed. This can be used to make a longitudinal section of a river composed of a horizontal distance and elevation. Moreover, GIS makes it possible to obtain accurate information, such as the characteristics of a river. The connection with flow information and GIS model can be used as alarming and expecting flood systems.

• Journal of Internet Computing and Services
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• v.25 no.3
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• pp.71-81
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• 2024

In response to the COVID-19 pandemic, the global airport industry is actively introducing 4th Industrial Revolution technology-based systems for quarantine and passenger safety, and test bed construction and prior verification using airport infrastructure and resources are actively being conducted. Analysis of recent cases shows that despite the changing travel patterns of airport users and the diversification of airport service demands, most testbeds construction studies are still focused on suppliers, and task prioritization is also determined by decision makers. There is a tendency to rely on subjective judgment. In order to find practical ways to become a first mover that leads innovation in the aviation industry, this study selected tasks and derived priorities to build testbeds from a service perspective that reflects various customer service needs and changes. Research results using the AHP analysis method resulted in priorities in the order of access transportation and parking services (29.2%), security screening services (23.4%), and departure services (21.8%), and these analysis results were tested in the airport industry. It shows that innovation in testbeds construction is an important factor. In particular, the establishment of smart parking and UAM transportation testbeds not only helps strengthen airports as centers of technological innovation, but also promotes cooperation with companies, research institutes, and governments, and provides an environment for testing and developing new technologies and services. It can be a foundation for what can be done. The results and implications produced through this study can serve as useful guidelines for domestic and foreign airport practitioners to build testbeds and establish strategies.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.10 no.6
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• pp.74-83
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• 2011

The high-pass transportation information system directly collects section information by using probe cars and therefore can offer more reliable information to drivers. However, because the running condition and features of probe cars and statistical processing methods affect the reliability of the information and particularly because the section travel time is greatly influenced by whether there has been delay by signals on urban roads or not, there can be much deviation among the collected individual probe data. Accordingly, researches in multilateral directions are necessary in order to enhance the credibility of the section information. Yet, the precedent studies related to high-pass information provision have been conducted on the highway sections with the feature of continuous flow, which has a limit to be applied to the urban roads with the transportational feature of an interrupted flow. Therefore, this research aims at analyzing the features of high-pass transportation data on urban roads and finding a proper processing method. When the characteristics of the high-pass data on urban roads collected from RSE were analyzed by using a time-space diagram, the collected data was proved to have a certain pattern according to the arriving cars' waiting for signals with the period of the signaling cycle of the finish node. Moreover, the number of waiting for signals and the time of waiting caused the deviation in the collected data, and it was bigger in traffic jam. The analysis result showed that it was because the increased number of waiting for signals in traffic jam caused the deviation to be offset partially. The analysis result shows that it is appropriate to use the mean of this collected data of high-pass on urban roads as its representative value to reflect the transportational features by waiting for signals, and the standard of judgment of delay and congestion needs to be changed depending on the features of signals and roads. The results of this research are expected to be the foundation stone to improve the reliability of high-pass information on urban roads.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.15 no.3
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• pp.45-51
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• 2016

Existing real-time signal control system was brought up typical problems which are supersaturated condition, point detection system and loop detection system. For that reason, the next generation signal control system of advanced form is required. Following thesis aimed at calculating queue length for the next generation signal control system to utilize basic parameter of signal control in crossing queue instead of the volume of real-time through traffic. Overflow saturated condition which was appeared as limit of existing system was focused to set-up range. Real-time location information of individual vehicle which is collected by GPS data. It converted into the coordinate to apply shock wave model with an linear equation that is extracted by regression model applied by a least square. Through the calculated queue length and link length by contrast, If queue length exceed the link, queue of downstream intersection is included as queue length that upstream queue vehicle is judeged as affecting downstream intersection. In result of operating correlation analysis among link travel time to judge confidence of extracted queue length, Both of links were shown over 0.9 values. It is appeared that both of links are highly correlated. Following research is significant using real-time data to calculate queue length and contributing to signal control system.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.6 no.3
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• pp.1-11
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• 2007

Seoul's transportation system was changed drastically starting the first of June in two thousand. This policy includes integrated distance-based fare system and public transportation card system called smart card. Especially, as public transportation card data contains individual travel, transfer and using modes information it is possible to catch the characteristics of path-based individuals and mass transit. Thus, public transportation card data can contribute to evaluate the mass transit service in integrated public transportation networks. In addition, public transportation card data are able to help to convert previous researches and analyses with link-based trip assignment models to path-based mass transit service analysis. In this study, an algorithm being suitable for path-based trip assignment models is suggested and proposed algorithm can also contribute to make full use of public transportation card data. For this, column generation algorithm hewn to draw the stable solution is adopted. This paper uses the methodology that is to take local approximate equilibrium from partial network and expand local approximate equilibrium to global equilibrium.