• 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.

• Journal of the Korean Regional Science Association
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• v.32 no.4
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• pp.39-49
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• 2016

The main objective of this paper is to analyze university students' modal shift for commuting trip due to the introduction of new urban rail transit in a satellite city of metropolitan area. The paper uses SP(2011)/RP(2013) data collected from Yeungnam University in Gyeongsan City, which is a satellite city of Deagu Metropolitan City. So far few researches, especially using before-and-after individual SP/RP travel survey, have been conducted on analyzing university students' modal shift due to the introduction of new urban rail transit. For this research, some descriptive statistical analyses were conducted. Furthermore, some empirical logit models were estimated for analyzing factors affecting the modal shift. Finally, some important findings and policy implications are discussed. The significant findings from this research are summarized as follows. From the descriptive statistical analyses of SP and RP data, it is found that the rate of modal shift to rail transit is relatively high especially for bus travellers. Furthermore, from the empirical SP model estimation, it is found that time saving is the most important factor affecting the modal shift to urban rail transit. On the other hand, from the empirical RP model estimation, it is found that residential location is the most important factor affecting the modal shift to urban rail transit.

• Journal of Korean Society of Transportation
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• v.20 no.3
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• pp.83-92
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• 2002

Freight mode choice models are essential to the analysis of many areas of transport research. However, observations of actual market choices have only been made in a limited number of situations. Therefore, stated preference(SP) techniques have emerged as an alternative source of actual market choices to be used for estimating freight mode choice models. Considerable confidence exists about SP data, but little consideration has been given to the potential for estimation bias. This paper has been motivated by the theoretical side of estimating SP discrete choice models, focusing on a case study of freight mode choice. Recently developed simulation methods are used to construct inherent random heterogeneity legit models, which consider individual heterogeneity, its inheritance to the next choices and overcome the independence from irrelevant alternatives (IIA) property. This Paper contributes to the development of models dealing with heterogeneity and its inheritance, and sheds light on the heterogeneity of freight transport.

• 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.

• 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 the Korean Society for Railway
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• v.20 no.2
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• pp.265-274
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• 2017

For accurate demand forecasting of railway logistics, we estimated intercity freight mode choice models based on the binary logit model and using production-consumption data from the Korea Transport Database. We estimated two types of models and compared the results by major item of railway logistics, such as container, cement, and steel: 1) The aggregate freight mode choice models are based on the revealed preference (RP) data and 2) The disaggregate models are based on the stated preference (SP) data. With respect to the container, the travel time variable was found to be statistically significant; however, the travel cost variable was not statistically significant in the RP model, while the travel cost variable was statistically significant in the SP model. For cement and steel, the travel cost variables were statistically significant but the travel time variables were not statistically significant in either the RP or the SP models. These results are inconsistent with results from previous studies based on SP data, which showed that the travel time variables were significant. Consequently, it can be concluded that the travel time factor should be considered in container transport, but that this factor is negligible for cement and steel transport.

• International Journal of Highway Engineering
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• v.8 no.3 s.29
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• pp.63-76
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• 2006

For a private toll road project, deciding optimal toll is an important element of economic analysis for the project and a challengeable work. In this study, the optimal toll of a private toll bridge, Geoga Bridge which connects Geoje Island of Gyeongnam Province and Gaduk Island of Busan was estimated using Stated Preference (SP) data. The SP data were collected by interviewing the passenger car drivers travelling on the National Road 14. They are latent users of the bridge. A fuzzy approximate reasoning model to estimate the optimal toll was built using the SP data. For the input variable of the model, the saved travel time and toll level were employed and the diversion rate to the bridge was employed for the output variable. The diversion rates for each toll level and saved travel time were estimated and the toll level which had maximized the toll revenue was decided as optimal toll. The optimal toll was tested by comparing with the average pay rate of passenger car drivers. Since the optimal toll for passenger cars at one hour saving, the 6,250 won is about 50 % of the average pay rate of passenger car divers, the toll was evaluated not to be high. The technique employed in this study may be used for the estimation of the optimal tolls for other kinds of vehicles.

• 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.

• 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.

• Journal of Korean Society of Transportation
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• v.28 no.4
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• pp.119-128
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• 2010

Mode choice behavior is associated with travelers' latent behavior that is an unobservable preference to travel behavior or mode characteristics. This paper specifically addresses the problem of unobservable factors, that is latent behavior, in mode choice models. Consideration of latent behavior in mode choice models reduces the errors that come from unobservable factors. In this study, the authors defined the latent variables that mean a quantitative latent behavior factors, and developed the combined RP/SP model with latent variables using the mode choice behavior survey data. The data has traveler's revealed preference of existent modes along the Han River and stated preference of new water transit on the Han River. Also, The data has travelers' latent behavior. Latent variables were defined by factor analysis using the latent behaviour data. In conclusion, it is significant that the relationship between traveler's latent behavior and mode choice behavior. In addition, the goodness-of-fit of the mode choice models with latent variables are better than the model without latent variables.