PURPOSES : This study aims to perform a quantitative analysis of Forward Collision Warning and crash frequency using heavy vehicle driving data collected in expressway driving environments, and to classify the driving environments where Forward Collision Warnings of heavy vehicles occur for accident-prone areas and analyze their occurrence characteristics. METHODS : A bivariate Gaussian mixture model based on inter-vehicle distance gap and speed-acceleration parameters is used to classify the environment in which Forward Collision Warning occurs for heavy vehicles driving on expressways. For this analysis, Probe Vehicle Data of 80 large trucks collected by C-ITS devices of Korea Expressway Corporation from May to June 2022. Combined with accident information from the past five years, a detailed analysis of the classified driving environments is conducted. RESULTS : The results of the clustering analysis categorizes Forward Collision Warning environments into three groups: Group I (highdensity, high-speed), Group II (high-density, low-speed), and Group III (low-density, high-speed). It reveals a positive correlation between Forward Collision Warning frequency and accident rates at these points, with Group I prevailing. Road characteristics at sites with different accident incidences showed that on-ramps and toll gates had high occurrences of both accidents and warnings. Furthermore, acceleration deviation at high-accident sites was significant across all groups, with variable speed deviations noted for each warning group. CONCLUSIONS : The Forward Collision Warning of heavy vehicles on expressways is classified into three types depending on the driving environment, and the results of these environmental classifications can be used as a basis for building a road environment that reduces the risk of crashes for heavy vehicles.

The purpose of this paper is to analyze factors affecting the net income of regional and industrial fisheries cooperatives in South Korea using panel data. This paper utilizes linear or GLS regression models such as pooled OLS model, fixed effects model, and random effects model to estimate affecting factors of the net income of regional and industrial fisheries cooperatives. After reviewing various tests, we eventually select random effects model. The results, based on panel data between 2013 and 2018 year and 64 fisheries cooperatives, indicate that capital and area dummy variables have positive effects and employment has negative effect on the net income of regional and industrial fisheries cooperatives as predicted. However, debt are opposite with our predictions. Specifically, it turns out that debt has positive effect on the net income of regional and industrial fisheries cooperatives although it has been increased. Additionally, this paper shows that the member of confreres does not show any significant effect on the net income of regional and industrial fisheries cooperatives in South Korea. This study is significant in that it analyzes the major factors influencing changes in the net income that have not been conducted recently for the fisheries cooperatives by region and industry.

Many measurement methods for understanding consumers’ acceptable price range have been developed. Among these, Price Sensitivity Meter (PSM) is one of the most popular. It has been regarded as a convenient research method because of the ease of data collection and data processing. In particular, PSM requires only four questions to determine the price range. Nevertheless, it also has some problems from a theoretical viewpoint. The purpose of the present research is to develop a new price research method for measuring consumers’ acceptable price range. In particular, applying survival analysis to data prepared for PSM, Japanese consumers’ price acceptance ranges for several categories were estimated.

본 연구에서는 지진해일에 의한 원자력발전소의 확률론적 안전성 평가를 위하여 필수적으로 도출해야 하는 지진해일 재해도 곡선을 도출하기 위한 연구를 수행하였다. 1900년도 이후에 기록된 동해안에서의 지진해일 기록과 1900년도 이전에 역사지진기록에서 찾을 수 있는 지진해일 기록을 이용하여 지진해일에 의한 최대파고에 대한 재현주기를 산정하고자 하였다. Power law, upper-truncated power law 그리고 지수함수에 의해서 추세선을 작성하였으며 그 결과를 비교하였다. 동해안에서 발생한 지진해일의 기록이 10건 내외에 불과하므로 기록에 의한 지진해일 재해도 곡선추정 연구에 제한이 있으나 국내에는 지진해일의 재해도곡선 추정에 관한 연구가 전무한 현실이므로 지진해일 확률론적 안전성 평가를 위한 초석을 놓은 것으로 판단된다.

Based on the data of Ctrip. com, this study analyzes the satisfaction of China tourists after booking hotels online when they travel to South Korea. Extract the online word-of-mouth data of Ctrip, analyze the comment data based on grounded theory, extract the keywords in the comments and sort them out to form the influencing factors of tourists’ hotel booking, split the comment sentences with python, analyze the keywords in the sentences, conduct emotional analysis with Python, score the emotional analysis of each influencing factor corresponding to each sentence, and finally get the satisfaction score of each factor. According to the score, it is proposed that Korean hotels should use more platforms for China tourists and China tourists to carry out targeted marketing activities, pay attention to language communication in service, and pay attention to the improvement of hotel facilities decoration with the change of tourists in China

최근 기후변화로 인한 자연재해가 증가하면서 강수 및 기온자료의 시계열에 대한 변동성과 추세를 분석하여 그 변화를 예측하는 연구의 필요성 이 점점 커지고 있다. 하지만 강수나 기온의 경우 복합적인 요소에 의해 변동이 일어나 자료의 변동성이 매우 심하고 너무 많은 요소를 포함하게 되 어 그 특성을 정확히 판단하기가 쉽지 않다. 따라서 자료의 시계열을 분해하게 되면 각 특성을 가진 요소를 추출할 수 있으므로, 정확한 변동 특성을 파악할 수 있다. 본 연구에서는 우리나라 강수 및 기온자료를 경험적 모드분해법(Empirical Mode Decomposition, EMD)을 통해 주기별로 분해 하여 각각의 내재모드함수(Intrinsic Mode Function, IMF)를 추출하였다. 또한, 추출된 내재모드함수의 에너지 밀도를 이용한 유의성 검정을 통 해 원자료로부터 유의미한 자료를 포함하고 있는 내재모드함수를 선별하고, 이들의 주기성, 경향성을 분석하였다.

In this study, we examine the relationship between climate change and food productivity using empirical econometric methods. The existing literature shows that natural hazard caused by climate change has a negative impact on food productivity since the natural disaster devastates farmers and food supply. The conventional study however considered only the correlation between food productivity change and climate condition such as optimum air temperature rather than the association between food productivity and climate change. Agricultural area, crop per unit area and crop productivity are known as the most important factors in food productivity. Thus, we explore the relationship between the three factors and climate change. We analyze the carbon dioxide concentration level in the atmosphere as a proxy for the climate change since the level of carbon dioxide in the atmosphere affects global temperature. We found that agricultural area, crop per unit area and crop productivity are negatively associated with climate change.