• Informatization Policy
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• v.21 no.2
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• pp.89-110
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• 2014

As a travel demand management and environmental policy, the government actively promotes alternative work arrangements such as telecommuting. Against this backdrop, several empirical studies, which aim to verify the benefits of telecommuting, have been recently conducted. Little consensus, however, exists with respect to the defining, measuring, and forecasting telecommuting, although these are fundamental basis of policy evaluation and academic research. As a fundamental research for analyzing telecommuting impacts, this paper reviews various definitions regarding telecommuting, examines telecommuting penetration and level of telecommuting through review of available survey data in Korea, and forecasts future penetration. The result suggests that current home-based telecommuting penetration and level of telecommuting is approximately 0.5 to 1.1% and 0.2 to 0.5%, respectively, and is approximately 0.2% and 0.1%, respectively, for the center-based telecommuting. In addition, shift-share analysis shows that home-based telecommuting penetration in the Seoul Metropolitan Area in 2020 will be 1.3%, not much different with the current value. Consequently, current telecommuting penetration is much lower than the value that is fed to us by the media (10~20%), and the future prospect is also much lower than the goal of government (30~45%); thus, we can conclude that government's goal of telecommuting promotion is difficult to meet if active encouragement policy will not be introduced.

• Journal of Korean Society of Transportation
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• v.26 no.4
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• pp.185-194
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• 2008

When forecasting demand for a new road, a select link analysis is usually used to understand the OD pairs that send trips along paths that use the selected link (i.e., the new road). These OD pairs and their associated volumes are listed in a select link analysis. However, there is no research about other methods to obtain these results, so experts are almost always dependent on select link analysis results to obtain these results. The purpose of this study is to propose a model with a different approach from select link analysis to obtain the previously mentioned results. Time and spatial characteristics of networks are used in this new approach. Select link analysis results are used as a comparison index for the results by the proposed model. Also, two case studies (interzonal trips and intracity trips) are performed to validate the significance of the model. Consequently, a correlation coefficient between the results by the proposed model and the comparison index shows high significance: 0.82.

• The Journal of Bigdata
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• v.6 no.1
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• pp.91-113
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• 2021

Despite the influence of real estate in the Korean asset market, it is not easy to predict market trends, and among them, apartments are not easy to predict because they are both residential spaces and contain investment properties. Factors affecting apartment prices vary and regional characteristics should also be considered. This study was conducted to compare the factors and characteristics that affect apartment prices in Seoul as a whole, 3 Gangnam districts, Nowon, Dobong, Gangbuk, Geumcheon, Gwanak and Guro districts and to understand the possibility of price prediction based on this. The analysis used machine learning algorithms such as neural networks, CHAID, linear regression, and random forests. The most important factor affecting the average selling price of all apartments in Seoul was the government's policy element, and easing policies such as easing transaction regulations and easing financial regulations were highly influential. In the case of the three Gangnam districts, the policy influence was low, and in the case of Gangnam-gu District, housing supply was the most important factor. On the other hand, 6 mid-lower-level districts saw government policies act as important variables and were commonly influenced by financial regulatory policies.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.2
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• pp.273-282
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• 2021

This study investigates for skills mismatching of the IT service sector in the flows of fast technology changes. It was conducted through an in-depth interview method for professional groups. There were differences in demand for skilled labor by business organizations and educators as providers of skilled labor. A five-point Likert scale was used. The degree of importance of 3.7 average point and the degree of satisfaction of 3.4 average point were responded for the set items in case of matching. In addition, the degree of importance of 3.79 average point and the satisfaction of 3.12 were responded in case of non-majored education students for IT. The skills desired from business organizations included multi-dimensional competencies and soft-skill items. For the reason of skills mismatching, business organizations presented ineffective specifications or divisions of the industrial manpower structure, and educational institutions selected the mismatching of time. Professional groups forecasted that the mismatching gap would expand in the future. To solve the gap, the participated professionals selected an industry-university institute collaboration course and gave an opinion to seek a method to foster manpower in the long-term perspective.

• Journal of the Korean earth science society
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• v.45 no.1
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• pp.19-36
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• 2024

The global seasonal forecasting system version 6 (GloSea6) operated by the Korea Meteorological Administration for 1- and 3-month prediction products does not include complex atmospheric chemistry-aerosol physical processes (UKCA). In this study, low-resolution GloSea6 and GloSea6 coupled with UKCA (GloSea6-UKCA) were installed in a CentOS-based Linux cluster system, and preliminary prediction results for the spring of 2000 were examined. Low-resolution versions of GloSea6 and GloSea6-UKCA are highly needed to examine the effects of atmospheric chemistry-aerosol owing to the huge computational demand of the current high resolution GloSea6. The spatial distributions of the surface temperature and daily precipitation for April 2000 (obtained from the two model runs for the next 75 days, starting from March 1, 2000, 00Z) were compared with the ERA5 reanalysis data. The GloSea6-UKCA results were more similar to the ERA5 reanalysis data than the GloSea6 results. The surface air temperature and daily precipitation prediction results of GloSea6-UKCA for spring, particularly over East Asia, were improved by the inclusion of UKCA. Furthermore, compared with GloSea6, GloSea6-UKCA simulated improved temporal variations in the temperature and precipitation intensity during the model integration period that were more similar to the reanalysis data. This indicates that the coupling of atmospheric chemistry-aerosol processes in GloSea6 is crucial for improving the spring predictions over East Asia.

• Journal of Korean Society of Forest Science
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• v.81 no.3
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• pp.255-262
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• 1992

Urguay Round(UR) has lots of implication in the forest product market as well as the other sectors of the economy. Chestnut, one of the major forest product in Korea, would be affected by free trade resulting from the agreement on UR. To establish effective policy measures dealing with negative effects of free trade, if any, the effect of UR on producers should be figured out. In this contest, the purposes of this study are (1) estimating the demand, supply and its price functions of this market and (2) forecasting the effect of UR on growers. Using econometric method, demand, supply and price function of this market are estimated. The total amount of yearly money loss of growers due to free trade from 1992 to 2001 are estimated for four different scenarios. In each scenario, it is assumed that the tariffication reduction is 30%, 40%, 50% and 90%. Yearly money loss of chestnut growers at the year 2001 are forecasted such as 14 billion won, 18 billion won, 24 billion won and 25 billion won for the rate of tariffication reduction of 30%, 40%, 50%, and 90%, respectively.

• Journal of Preventive Medicine and Public Health
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• v.21 no.1 s.23
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• pp.195-206
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• 1988

Two major issues of the blood bank management are quality assurance and inventory control. Recently, in Korea blood donation has gained popularity increasingly to allow considerable improvement of the quality assurance with respect to blood collection, transportation, storage, component preparation skills and hematological tests. Nevertheless the inventory control, the other issue of blood bank management, has been neglected so far. For the supply of blood by donation barely meets the demand, the blood bank policy on the inventory control has been 'the more the better.' The shortage itself by no means unnecessitate inventory control. In fact, in spite of shortage, no small amount of blood is outdated. The efficient blood inventory control makes it possible to economize the blood usage in the practice of state-of-the-art medical care. For the efficient blood inventory control in Korean hospitals, this tudy is to develop formulae forecasting the standard blood inventory level and suggest a set of policies improving the blood inventory control. For this study informations of $A^+$ whole bloods and packed cells inventory control were collected from a University Hospital and the Central Blood Bank of the Korean Red Cross. Using this informations, 1,461 daily blood inventory records were formulated.48 varieties of blood inventory control environment were identified on the basis of selected combinations of 4 inventory control variables-crossmatch, transfusion, inhospital donation and age of bloods from external supply. In order to decide the optimal blood inventory level for each environment, simulation models were designed to calculate the measures of performance of each environment. After the decision of 48 optimal blood inventory levels, stepwise multiple regression analysis was started where the independent variables were 4 inventory control variables and the dependent variable was optimal inventory level of each environment. Finally the standard blood inventory level decision rule was developed using the backward elimination procedure to select the best regression equation. And the effective alternatives of the issuing policy and crossmatch release period were suggested according to the measures of performance under the condition of the standard blood inventory level. The results of this study' were as follows ; 1. The formulae to calculate the standard blood inventory level($S^*$)was $S^*=2.8617X(d)^{0.9342}$ where d is the mean daily crossmatch(demand) for a blood type. 2. The measures of performace - outdate rate, average period of storage, mean age of transfused bloods, and mean daily available inventory level - were improved after maintenance of the standard inventory level in comparison with the present system. 3. Issuing policy of First In-First Out(FIFO) decreased the outdate rate, while Last In-First Out(LIFO) decreased the mean age of transfused bloods. The decrease of the crossmatch release period reduced the outdate rate and the mean age of transfused bloods.

• Journal of Korean Society of Transportation
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• v.29 no.6
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• pp.97-106
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• 2011

Mode choice model is an essential element for estimating- the demand of new means of transportation in the planning stage as well as in the establishment phase. In general, current demand analysis model developed for the mode choice analysis applies common parameters of utility function in each region which causes inaccuracy in forecasting mode choice behavior. Several critical problems from using common parameters are: a common parameter set can not reflect different distribution of coefficient for travel time and travel cost by different population. Consequently, the resulting model fails to accurately explain policy variables such as travel time and travel cost. In particular, the nonlinear logit model applied to aggregation data is vulnerable to the aggregation error. The purpose of this paper is to consider the regional characteristics by adopting the parameters fitted to each area, so as to reduce prediction errors and enhance accuracy of the resulting mode choice model. In order to estimate parameter of each area, this study used Household Travel Survey Data of Metropolitan Transportation Authority. For the verification of the model, the value of time by marginal rate of substitution is evaluated and statistical test for resulting coefficients is also carried out. In order to crosscheck the applicability and reliability of the model, changes in mode choice are analyzed when Seoul subway line 9 is newly opened and the results are compared with those from the existing model developed without considering the regional characteristics.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.4
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• pp.63-75
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• 2018

A Service Area plays an important role in preventing accidents in advance by creating a space for long distance drivers or drowsy drivers to rest. Therefore, proper positioning of the expressway service area is essential, and it is important to analyze accurate demand forecasting and user travel behavior. Thus, this study analysis travel behavior and developed odel of the probability of using the service area by using the DSRC data collected by the RSE on the highway. According to the analysis, the usage behavior of highway service areas was most frequently when travel time was 90 minutes or more on weekdays and 70 minutes or more on weekends. The utilization rate of the service area estimated from the probability model of use of the rest area in this study was 1 % to 2 % error. The results of this study are meaningful in analyzing the behavior of the use of rest areas using the structured data and can be used as a differentiated strategy for selecting the location of rest areas and enhancing the service level of users.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.15 no.4
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• pp.44-53
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• 2016

The era of Big Data has come and the importance of Big Data has been rapidly growing. The part of transportation, the Four-Step Travel Demand Model(FSTDM), a traditional Trip-Based Model(TBM) reaches its limit. In recent years, a traffic demand forecasting method using the Activity-Based Model(ABM) emerged as a new paradigm. Given that transportation means the spatial movement of people and goods in a certain period of time, transportation could be very closely associated with spatial data. So, I mined Spatial Big Data from SNS. After that, I analyzed the character of these data from SNS and test the reliability of the data through compared with the attributes of TBM. Finally, I built a database from SNS for the operation of ABM and manipulate an ABM simulator, then I consider the result. Through this research, I was successfully able to create a spatial database from SNS and I found possibilities to overcome technical limitations on using Spatial Big Data in the transportation planning process. Moreover, it was an opportunity to seek ways of further research development.