• Journal of Korean Society of Transportation
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• v.18 no.6
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• pp.63-75
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• 2000

Stated Preference (SP) data has been regarded as more useful than Revealed Preference (RP) data, because researchers can investigate the respondents\` Preference and attitude for a traffic condition or a new traffic system by using the SP data. However, the SP data has two bias: the first one is the bias inherent in SP data and the latter one is the attrition bias in SP panel data. If the biases do not corrected, the choice model using SP data may predict a erroneous future demand. In this Paper, six route choice models are constructed to deal with the SP biases, and. these six models are classified into cross-sectional models (model I∼IH) and dynamic models (model IV∼VI) From the six models. some remarkable results are obtained. The cross-sectional model that incorporate RP choice results of responders with SP cross-sectional model can correct the biases inherent in SP data, and also the dynamic models can consider the temporal variations of the effectiveness of state dependence in SP responses by assuming a simple exponential function of the state dependence. WESML method that use the estimated attrition probability is also adopted to correct the attrition bias in SP Panel data. The results can be contributed to the dynamic modeling of SP Panel data and also useful to predict more exact demand.

• Journal of Korean Society of Transportation
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• v.13 no.4
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• pp.31-45
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• 1995

In recent year, especially in the mode choice analysis, it has been perceived that the importance of individual performance data using stated preference(SP) experiments as well as revealed preference data . Since SP experiments present respondent with various hypothetical alternatives, which are produced by a combination of a number of different attribute levels, and ask them to indicate a preference, it is possible to analyze travel behavior under a situation of potential environment change such as proposed alternative mode of transportation. The basic problems, however, remains that SP are not consistent with the actual travel behaviors and the research reports for stability of mode choice model using SP data has not been sufficient. Under this background, this study is to examine the characteristics of mode choice model using the SP data by the following items. $\circled1$ Analysis of factors affecting the mode choice behavior by the variance analysis of orthogonal-arrays-table $\circled2$ The reliability of SP data by transfer intention data $\circled3$ The stability of SP responses obtained from repetitive question by the comparison of model coefficient specified by each repetitive data. $\circled4$ The stability of ranking data in mode choice model For the analysis, we assumed subway operations in the Gwang-Ju , and set up a choice-set of hypothetical options based on Experimental Design Method.

• Journal of Korean Society of Transportation
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• v.21 no.2
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• pp.119-130
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• 2003

SP data have been widely used in assessing new transport policies and transport related plans. However, one of criticisms of using SP is that respondents may show different reaction between hypothetical experiments and real life. In order to overcome the problem, combination of SP and RP data has been suggested and the combined methods have been being developed. The purpose of this paper is to suggest a new SP and RP combined method using error component method and to verify the method. The error component method decomposes IID extreme value error into non-IID error component(s) and an IID error component. The method estimates both of component parameters and utility parameters in order to obtain relative variance of SP data and RP data. The artificial SP and RP data was created by using simulation and used for the analysis, and the estimation results of the error component method were compared with those of existing SP and RP combined methods. The results show that regardless of data size, the parameters of the error component method models are similar to those assumed parameters much more than those of the existing SP and RP combined models, indicating usefulness of the error component method. Also the values of time for error component method are more similar to those assumed values than those of the existing combined models. Therefore, we can conclude that the error component method is useful in combining SP and RP data and more efficient than the existing methods.

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.463-473
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• 2021

Recently, United States Geological Survey (USGS) distributed Landsat 8 Collection 2 Level 2 Science Product (L2SP). This paper aims to derive land surface temperature from L2SP and to validate it. Validation is made by comparing the land surface temperature with the one calculated from Landsat 8 Collection 1 Level 1 Terrain Precision (L1TP) and the one from Automated Synoptic Observing System (ASOS). L2SP is calculated from Landsat 8 Collection 2 Level 1 data and it provides land surface temperature to users without processing surface reflectance data. Landsat 8 data from 2018 to 2020 is collected and ground sensor data from eight sites of ASOS are used to evaluate L2SP land surface temperature data. To compare ground sensor data with remotely sensed data, 3×3 grid area data near ASOS station is used. As a result of analysis with ASOS data, L2SP and L1TP land surface temperature shows Pearson correlation coefficient of 0.971 and 0.964, respectively. RMSE (Root Mean Square Error) of two results with ASOS data is 4.029℃, 5.247℃ respectively. This result suggests that L2SP data is more adequate to acquire land surface temperature than L1TP. If seasonal difference and geometric features such as slope are considered, the result would improve.

• Journal of Korean Society of Transportation
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• v.10 no.3
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• pp.21-42
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• 1992

As for the travel demand analysis of the past, forcasting has been conducted by the use of revealed preference(RP) informations about actual or observed choices made by individuals. Forcasting method using RP data needs implicit assumptions that there will be no remarkable changes in existing transport conditions. However in case of occuring the great changes in existing conditions or adding a new choice-set of hypothetical options, it is very difficult to predict future travel demand. Fortunately in recent years, especially in the mode choice analysis, it has been perceived that the importance of individual performance data using stated preference(SP) experiments as well as RP data. But the research reports has not been reported sufficiently from models estimated using SP data. Under this background, we analyze the factors affecting the mode choice behavior as a fundamental study against the modelling task with SP choice data. For this analysis, we assumed subway operations in the secondary cities where there are no subway lines until now, and set up a choice-set of hypothetical options based on Experimental Design Method.

• 한국지구물리탐사학회:학술대회논문집
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• 2005.05a
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• pp.71-76
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• 2005

The subject of this paper is research into the application of resistivity/SP monitoring to detecting the water leakage of water utilization facilities. For this purpose, we installed a comprehensive monitoring system consisting of resistivity/SP measurement, inclinometer, piezometer, and water gauge at an embankment, Using this monitoring system, we monitored the various kinds of measurement data and compared the resistivity structures and SP variations that of hydrological and engineering data in order to investigate the water leakage and stability of the embankment. The variations of resistivity and SP at the embankment were provided from the monitoring data and we could accurately locate the portions of which resistivities and SP have sharply changed, Furthermore, we could estimate the stability of the embankment more effectively and quantitatively by jointly interpreting the monitoring data of resistivity and SP, water level, pore water pressure, and subsurface displacement. The monitoring experiments in this study led us to the conclusion that for the efficient maintenance of the water utilization facilities, monitoring the resistivity and SP data would be much more preferable to performing the just one-time measurements.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.17 no.10
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• pp.1164-1174
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• 1992

In this paper, an Information Security protocol in LLC/MAC Layer Architecture is discussed. This paper examines the security Vulnerability and threats, the security Service required to protect these threats, and architectural considerations of security protocol in IEEE 802 LAN architecture. To provide an Information security service, an information security protocol(SP2 : Security Protocol 2) PDU construction with LLC/MAC service primitives is suggested. To construct the SP2 protocol, the ECB, CBC mode of DES algorithm and DAA(Data Authentication Algorithm) of FIPS is used. The SP2 protocol suggested in this paper provides data origin authentication, data confidentiality, data integrity service.

• Journal of the Korean Society for Railway
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• v.11 no.6
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• pp.588-595
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• 2008

This study is carried out for understanding mode choice behavior of shippers and introducing ideal mode share between rail and truck in Korea. The model type is individual behavioral model and the input data type is stated preference (SP) data. SP data was prepared by design of experiments. The explanatory variables in models are transport time, transport cost and service level. The research result shows that it is more effective to reduce transport cost rather than to implement other strategies. For container, reducing transport cost and transport time and increasing service level simultaneously can strengthen the competitiveness of rail over truck transportation. On the other hand, for bulk freight such as cement and steel, it is better to reduce the transport cost than to do other attributes.

• Asian-Australasian Journal of Animal Sciences
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• v.23 no.4
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• pp.444-449
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• 2010

The aim of this study was to measure the hair melanins of various colors and to find the relationship between the quantity of melanins and hair color phenotypes in alpacas. According to the Munsell color system, 3 healthy alpacas were selected for each of the 22 different hair color phenotypes (66 alpacas altogether). Alpaca hair was taken from the lateral thoracic region and then dissolved with different solutions to obtain melanins. The values of alkali-soluble melanins (ASM), eumelanin (EM) and pheomelanin (PM) were measured by spectrophotometric assay, and labeled as Sp.ASM, Sp.EM and Sp.PM, respectively. Data were analyzed using SPSS11.5 software. Results showed that average Sp.ASM and Sp.PM were increased as the color deepened from white to black, ranging from 0.500 to 4.543 for Sp.ASM and from 0.268 to 1.457 for Sp.EM. However, average Sp.PM had no such apparent relationship with color. Based on the value of Sp.ASM and EM, 7 hues were produced and gray was a single hue. Most of the data were in a normal distribution (p>0.10). ANOVA analysis showed that mean values of Sp.ASM, Sp.EM and Sp.PM were significantly different (p<0.05). The results also showed that Sp.ASM was positively correlated with Sp.EM but the correlation between Sp.ASM and Sp.PM was not significantly different from 0. It is concluded that EM is the major constituent of alpaca hair melanin; there is a significant correlation among ASM, EM and alpaca hair colors, and EM is the most reliable parameter for distinguishing these groups.

• Journal of Korean Society of Transportation
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• v.22 no.6
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• pp.197-206
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• 2004

In ranking data, respondents are required to rank a number of alternatives in order of their preferences and an exploded logit model is generally used. It assumes that each rank contains the same amount of random noise. This study investigates the reliability of ranking data and identifies whether there are different decision rules at each rank stage. The results show that there were differences in the amount of unexplained variation in different ranking stage. A single scaling parameter could not explain the difference of variations of individual coefficients between two ranking data average difference of variations. This paper also investigated the optimal explosion depth in the exploded logit model by using the suggested scaling approach. The scaling approach should be based on particular variables which have different variances rather than based on the whole data set. The empirical analysis show that an explosion depth of 2 is appropriate after scaling the second rank data set, while an explosion including the third rank is inappropriate even though the third rank data set is scaled.