• Journal of Korean Society of Transportation
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• v.33 no.5
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• pp.461-469
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• 2015

The next generation high-speed train, HEMU-430X, was developed and is now being tested. However, the existing mode choice models based on the guidelines for feasibility studies do not consider a high-speed train with a higher speed than KTX. This limitation might result in inaccurate demand forecasting. In this research, a stated preference survey was conducted in order to supplement the problem by considering the characteristics of HEMU-430X. Based on the survey results, this research developed two mode choice models, including a multinomial logit model and a nested logit model. For this purpose, the utility functions of travel time and travel costs were estimated using a Limdep 8.0 NLOGIT 3.0 package. After comparing the two models, it was concluded that the nested logit model is appropriate. The paper suggested a plan to implement the nested logit model and presented a policy implication.

• Journal of Korean Society of Transportation
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• v.11 no.1
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• pp.67-86
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• 1993

A formulation of dynamic traffic assignment between multiple origins and single destination was first introduced in 1987 by Merchant and Nemhauser, and then expanded for multiple destination in the late 1980's (Carey, 1987). Based on behavioral choice theory which provides proper demand elasticities with respect to changes in policy variables, traffic phenomena can be analysed more realistically, especially in peak periods. However, algorithms for these models are not well developed so far(working with only small toy network) and solutions of these models are not unique. In this paper, a new model is developed which keeps the simplicity of static models, but provides the sensitivity of dynamic models with changes of O-D flows over time. It can be viewed as a joint departure time and route choice model, in the given time periods(6-7, 7-8, 8-9 and 9-10 am). Standard multinomial logit model has been used for simulating the choice behavior of destination, mode, route and departure time within a framework of the incremental network assignment model. The model developed is workable in a PC 386 with 175 traffic zones and 3581 links of Seoul and tested for evaluating the exclusive use of Namsan tunnel for HOV and the left-turn prohibition. Model's performance results and their statistical significance are also presented.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.325-329
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• 2001

This paper tests existence of precautionary saving motive under health uncertainty, using household level panel data from Korea. For this purpose, this paper considers a dynamic health capital model with health uncertainty and derives testable equations for changes in consumption and medical expenditures. Under this framework, households who face future health uncertainty will exhibit precautionary behavior by depressing consumption or increasing investment in health. To test this hypothesis, the paper uses the conditional variance of health as the direct measure of health uncertainty, obtained by estimating a multinomial logit model. Empirical results using the Korean Household Panel Study (KHPS, 1993 - 1997) suggest that Korean elderly households follow the precautionary behavior to insure against future health risk.

• Journal of the Korean Data and Information Science Society
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• v.28 no.3
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• pp.685-695
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• 2017

The traditional mixture of experts (ME) modeled the gate network using a certain parametric function. However, if the assumed parametric function does not properly reflect the true nature, the prediction strength of ME would become weak. For example, the parametric ME often uses logistic or multinomial logistic models for the network model. However, this could be very misleading if the true nature of the data is quite different from those models. Although, in this case, we may develop more flexible parametric models by extending the model at hand, we will never be free from such misspecification problems. In order to alleviate such weakness of the parametric ME, we propose to use the semi-parametric mixture of experts (SME) in which the gate network is estimated in a non-parametrical way. Based on this, we compared the performance of the SME with those of ME and neural networks via several simulation experiments and real data examples.

• Proceeding of Spring/Autumn Annual Conference of KHA
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• 1992.11a
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• pp.45-51
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• 1992

The purpose of this study was to predict the spatial housing demand of households in Seoul with the use of residential choice probabilitics. An multinomial legit model is developed using socio-demographic and housing characteristics. SAS package was utilized to estimate this model. This study used the data obtained by the Korea Rosearch Institute for Human Settlemente in 1989. The sample size was 3941 households in Seoul.The residential choice probability varicd depending upon the residential area, head age, head age, tenure and work place. The households with students were more likely to choose kangnam are. The households without young children had higher probability to choose new town near Seoul. Prime reason of this two results were considered the chi Id education and their better housing, Kangnam area was known to be the first consideration for residential choice regardless of work place. Low level of choice probability of kangman area for future residences however, was evidenced. Prime reason of such seemingly contradicting phenomenon is suspected for higher housing prices and limited affordability of people surveyed.

• The KIPS Transactions:PartD
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• v.9D no.4
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• pp.659-666
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• 2002

Park, Seo and Kim (12) developed the input domain-based SRGM, which was able to quantitatively assess the reliability of a software system during the testing and operational phases. They assumed perfect debugging during testing and debugging phase. To make this input domain-based SRGM more realistic, this assumption should be relaxed. In this paper we generalize the input domain-based SRGM under imperfect debugging. Then its statistical characteristics are investigated.

• Journal of Korean Society for Quality Management
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• v.26 no.1
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• pp.143-160
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• 1998

This article is concerned with the selection of subsets of predictor variables to be included in bulding the binary response logistic regression model. It is based on a Bayesian aproach, intended to propose and develop a procedure that uses probabilistic considerations for selecting promising subsets. This procedure reformulates the logistic regression setup in a hierarchical normal mixture model by introducing a set of hyperparameters that will be used to identify subset choices. It is done by use of the fact that cdf of logistic distribution is a, pp.oximately equivalent to that of $t_{(8)}$/.634 distribution. The a, pp.opriate posterior probability of each subset of predictor variables is obtained by the Gibbs sampler, which samples indirectly from the multinomial posterior distribution on the set of possible subset choices. Thus, in this procedure, the most promising subset of predictors can be identified as that with highest posterior probability. To highlight the merit of this procedure a couple of illustrative numerical examples are given.

• Knowledge Management Research
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• v.6 no.2
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• pp.23-47
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• 2005

This study is performed the multinomial logistic regression with the officials needs level about a component of knowledge administration for drawing a demand estimation model in the knowledge administration activities. This study is not that an activity and domain of knowledge administration is to apply and to operate uniformly it in public sector, one is suggested an application with a demand diagnose of knowledge administration in order to saw a course of the knowledge administration programs to suit a function and role of public administration. A result of this study is that an activity and domain of the knowledge administration is different from a component of it namely, knowledge creating, knowledge organizing, knowledge sharing and distribution, knowledge utility, and knowledge store. And the officials individual characteristics, administration agency, a kind of business, and a function and role of work are different from demand of knowledge administration. Also, the practical use of KMS (knowledge management system) is not so high in public sector. Accordingly, the tools of knowledge administration will deliberate on a consolidation with the existing system in the device.

• Korea Trade Review
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• v.47 no.2
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• pp.69-88
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• 2022

This paper aims to predict Busan's regional product and employment using the logistic regression models and machine learning models. The following are the main findings of the empirical analysis. First, the OLS regression model shows that the main industries such as electricity and electronics, machine and transport, and finance and insurance affect the Busan's income positively. Second, the binomial logistic regression models show that the Busan's strategic industries such as the future transport machinery, life-care, and smart marine industries contribute on the Busan's income in large order. Third, the multinomial logistic regression models show that the Korea's main industries such as the precise machinery, transport equipment, and machinery influence the Busan's economy positively. And Korea's exports and the depreciation can affect Busan's economy more positively at the higher employment level. Fourth, the voting ensemble model show the higher predictive power than artificial neural network model and support vector machine models. Furthermore, the gradient boosting model and the random forest show the higher predictive power than the voting model in large order.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.4
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• pp.148-163
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• 2021

The diversification of the New Deal urban regeneration projects, that started in 2017 in accordance with the "Special Act on Urban Regeneration Activation and Support", generated the increased demand for the accuracy of data-driven diagnosis and project type forecast. Thus, this research was conducted to develop an application model able to identify the most appropriate New Deal project type for "eup", "myeon" and "dong" across the country. Data for application model development were collected through Statistical geographic information service(SGIS) and the 'Urban Regeneration Comprehensive Information Open System' of the Urban Regeneration Information System, and data for the analysis model was constructed through data pre-processing. Four models were derived and simulations were performed through polynomial regression analysis and multinomial logistic regression analysis for the application of the appropriate New Deal project type. I verified the applicability and validity of the four models by the comparative analysis of spatial distribution of the previously selected New Deal projects by targeting the sites located in Seoul by each model and the result showed that the DI-54 model had the highest concordance rate.