• The Journal of the Korea Contents Association
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• v.11 no.8
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• pp.459-467
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• 2011

BIS(Bus Information Systems) supplies the bus arrived time information for users in using BIT(Bus Information Terminal) installed on the bus stop. BIT is the device using peoples directly. So, BIT need a quick response when it flew. These are an important factor in the strategy of the BIS maintenance. BIT need a maintenance cost to operate smoothly. So, Suppose that commercial advertisement can be displayed on BIT screen in this study. And we researched an advertisement rates of the optimum level in using Contingent Valuation Method. In addition, we analyzed a characteristic of user's depending on each time using multinomial Logit Modeling method, and studied for BIT operation and ad. displaying strategy considered user's sex, ages and using times.

• The Korean Journal of Applied Statistics
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• v.26 no.6
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• pp.889-902
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• 2013

Several goodness-of-fit test statistics have been proposed for a multinomial logit regression model; however, the properties of the proposed tests were not adequately studied. This paper evaluates three different goodness-of-fit tests using grouping strategies, proposed by Fagerland et al. (2008), Bull (1994), and Pigeon and Heyse (1999). In addition, Pearson (1900)'s method is also examined as a reference. Simulation studies were conducted to evaluate the four methods in terms of null distribution and power. A real data example is presented to illustrate the methods.

• Journal of the Korean association of regional geographers
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• v.13 no.6
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• pp.712-723
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• 2007

The objective of this paper is to search the factors inducing the visitors to revisit in Bangeo Festival of Jeju region. To get this objective, this study analyzed the data with the Multinomial Logit Model applied dependent variable to intention of revisit. As a result, 'festival program' factor is the most important thing and if the factor increases by 1 unit, the probability of 'revisit' can be increased by 5.255 times than the probability of 'no revisit'. Secondly, the next significant factors are 'festival convenience' and 'festival recognition in advance'. So the providers of the festival will intend to prepare the festival focused on the important factor proposed by this results.

• Journal of Korean Society of Transportation
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• v.2 no.1
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• pp.103-119
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• 1984

복잡다기해지는 도시교통문제를 효율적으로 대처하려면 제도시교통정책에 의한 교 통분담율효과를 사전에 추정할 수 있어야 한다. 단기간의 교통분담율효과를 추정하는데 미 국 및 구라파 등지에서 널리 이용되고 있는 모형이 개별교통모형(Disaggregate Travel Demand Model)이다. 본 연구의 목적은 로짓모형(Multinomial Logit Model)을 서울시의 종 로축을 이용하는 출근통행자를 대상으로 실시한 조사결과에 적용하여 매개함수(Parameters) 를 추정함에 있다. 조사는 1980년7월5일부터 7월15일까지 10일간 종로축을 이용하는 통행자 536명에게 실시되었다. 조사실시전 서울시의 교통체계의 특성과 통행자의 행태를 면밀히 분 석하여 적합한 변수를 선정하였다. 여러 가지로 변수와 표본의 변형을 시도한 결과 교통비 용을 소득으로 나눈 변수와 시기시간(OVTT)을 거리로 나눈 변수를 포함한 모형이 가장 논 리적인 것으로 나타났다. 한편 표본은 고소득층과 저소득층으로 구분하여 추정한 모형이 비 교적 만족스러운 결과를 나타내었다. 이는 우리나라 대도시의 경우 소득계층에 따라 교통수 단선택범위가 한정되기 때문이다. 마지막으로 고소득층과 저소득층의 시간가치를 각각 산정 하였는바, 이는 교통시간의 매개변수와 교통비용의 매개변수를 나눔으로서 구해질 수 있다. 시간가치는 고소득층은 910원 저소득층은 582원으로 각각 산출되었다.

• Journal of the Korean Data and Information Science Society
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• v.23 no.3
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• pp.587-593
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• 2012

We study Bayesian estimates for finite population proportions in multinomial problems. To do this, we consider a three-stage hierarchical Bayesian model. For prior, we use Dirichlet density to model each cell probability in each cluster. Our method does not require complicated computation such as Metropolis-Hastings algorithm to draw samples from each density of parameters. We draw samples using Gibbs sampler with grid method. We apply this algorithm to a couple of simulation data under three scenarios and we estimate the finite population proportions using two kinds of approaches We compare results with the point estimates of finite population proportions and their standard deviations. Finally, we check the consistency of computation using differen samples drawn from distinct iterates.

• Communications for Statistical Applications and Methods
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• v.18 no.6
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• pp.859-869
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• 2011

Interest has increased in academic research regarding key factors that drive box-office success as well as the ability to predict the box-office success of a movie from a commercial perspective. This study analyzed the relationship between key success factors of a movie and box office records based on movies released in 2010 in Korea. At the pre-production investment decision-making stage, the movie genre, motion picture rating, director power, and actor power were statistically significant. At the stage of distribution decision-making process after movie production, among other factors, the influence of star actors, number of screens, power of distributors, and social media turned out to be statistically significant. We verified movie success factors through the application of a Multinomial Logit Model that used the concept of choice probabilities. The Multinomial Logit Model resulted in a higher level of accuracy in predicting box-office success compared to the Artificial Neural Network and Discriminant Analysis.

• Journal of Korean Society of Transportation
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• v.25 no.1 s.94
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• pp.109-119
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• 2007

Increasingly, the emphasis in regional Passenger rail Planning is finding ways to more efficiently use existing facilities, with particular attention being Paid to Policies designed to spread Peak-Period travel demand more evenly throughout the week with consideration of train classification. In this context the individual's choice of time to travel is of crucial significance. This paper investigates the use of multinomial logit analysis to model ridership by rail classification using data collected for travel from Seoul to Busan during the one week in October 2004. The Particular model form that was successfully calibrated was the multinomial logit (MNL) model : it describes the choice mechanism that will Permit rail systems and operations to be planned on a more reliable basis. The assumption of independently and identically distributed(IID) error terms in the MNL model leads to its infamous independence from irrelevant alternatives (IIA) property. Relaxation of the IID assumption has been undertaken along a number or isolated dimensions leading to the development of the MNL model. For business and related rail travel patterns, the most important variables of choice were time and frequency to the chosen destination. The calibrated model showed high agreement between observed and Predicted market shares. The model is expected to be of use to railroad authorities in Planning and determining business strategies in the Increasingly competitive environment or regional rail transport.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.12 no.1
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• pp.147-157
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• 2013

This study developed the transit transfer mode choice model aimed Daegu transit users using multinomial logit model. Dependent variables of estimating multinomial logit model were transit transfer modes such as bus to bus, bus to subway, subway to subway, bus to others, and subway to others, and explanatory variables which affect transit transfer mode choice were sex, age, occupation, handicap, transfer area, purpose of travel and travel time. Also probability regarding explanatory variables was estimated using multinomial logit model and limit marginal analysis was carried out according to explanatory variables(cost, time). In the results, indicating goodness of fit is very reasonable as ${\rho}^2$=0.354. According to the result of marginal analysis for the selection of probability, when travel time is increased, users of bus to bus and bus to subway prefer to use subway to subway. Furthermore users of bus to bus and bus to subway prefer to use bus to others and subway to others when travel cost is increased in the result of marginal analysis for the selection of probability.

• The Journal of Engineering Geology
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• v.31 no.1
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• pp.31-42
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• 2021

Database of Cut-slope management system (CSMS) has been constructed based on investigations of all slopes on the roads of the whole country. The investigation data is documented by human, so it is inevitable to avoid human-error such as missing-data and incorrect entering data into computer. The goal of this paper is constructing a prediction model based on several machine-learning algorithms to solve those imperfection problems of the CSMS data. First of all, the character-type data in CSMS data must be transformed to numeric data. After then, two algorithms, i.g., multinomial logistic regression and deep-neural-network (DNN), are performed, and those prediction models from two algorithms are compared. Finally, it is identified that the accuracy of DNN-model is better than logistic model, and the DNN-model will be utilized to improve data-quality.

• The Korean Journal of Applied Statistics
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• v.31 no.2
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• pp.265-285
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• 2018

In this study, we applied a deep neural network model to predict four grades of fine dust $PM_{10}$, 'Good, Moderate, Bad, Very Bad' and two grades, 'Good or Moderate and Bad or Very Bad'. The deep neural network model and existing classification techniques (such as neural network model, multinomial logistic regression model, support vector machine, and random forest) were applied to fine dust daily data observed from 2010 to 2015 in six major metropolitan areas of Korea. Data analysis shows that the deep neural network model outperforms others in the sense of accuracy.