• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.6
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• pp.32-48
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• 2023

Autonomous driving technology is shaping the future of personalized travel, encouraging personalized travel, and traffic impact could be influenced by individualized travel behavior during the transition of driving entity from human to machine. In order to evaluate traffic impact, it is necessary to estimate the total number of trips based on an understanding of individual travel characteristics. The Activity-based model(ABM), which allows for the reflection of individual travel characteristics, deals with all travel sequences of an individual. Understanding the relationship between travel and travel must be important for assessing traffic impact using ABM. However, the ABM has a limitation in the data hunger model. It is difficult to adjust in the actual demand forecasting. Therefore, we utilized a Tour-based model that can explain the relationship between travels based on household travel survey data instead. After that, vehicle registration and population data were used for correction. The result showed that, compared to the KTDB one, the traffic generation exhibited a 13% increase in total trips and approximately 9% reduction in working trips, valid within an acceptable margin of error. As a result, it can be used as a generation correction method based on Tour, which can reflect individual travel characteristics, prior to building an activity-based model to predict demand due to the introduction of autonomous vehicles in terms of road operation, which is the ultimate goal of this study.

• KDI Journal of Economic Policy
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• v.28 no.2
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• pp.1-49
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• 2006

Considering the fact that households' demographic characteristics affect consumption decision, it is conjectured that rapid demographic changes would lead to a substantial change in the composition of private consumption expenditure. This paper estimates the demand functions of various consumption items by applying the Quadratic Almost Ideal Demand System(QUAIDS) model to Household Income and Expenditure Survey data, and then provides a long-term forecast of the composition of household consumption expenditure for 2005-2020. The paper shows that Korea's consumption expenditure will maintain the recent years' rapid change, of which a considerable portion is due to rapid demographic changes. Results of the paper can be utilized in forecasting the change in the industrial structure of the economy, as well as in firms' investment planning.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.21 no.4
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• pp.77-89
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• 2013

The purpose of this study is to anticipate the air travel demands over the period of 164 months, from January 1997 to August 2010 using ARIMA-Intervention modeling on the selected sample data. The sample data is composed of the number of the passengers who in the domestic route for Jeju route. In the analysis work of this study, the past events which are assumed to have affected the demands for the air travel routes to Jeju in different periods were used as the intervention variables. The impacts of such variables were reflected in the presupposed demand. The intervention variables used in this study are, respectively, the World Cup event in 2002 (from May to June), 2003 SARS outbreak (from April to May), Tsunami in January 2005, and the influenza outbreak from October to December 2009. The result of the above mentioned analysis revealed that the negative intervention events, like a global outbreak of an epidemic did have negative impact on the air travel demands in a risk aversion by the users of the aviation services. However, in case of the negative intervention events in limited area, where there are possible substituting destinations for the tourists, the impact was positive in terms of the air travel demands for substituting destinations due to the rational expectation of the users as they searched for other options. Also in this study, it was discovered that there is not a binding correlation between a nation wide mega-event, such as the World Cup games in 2002, and the increased air travel demands over a short-term period.

• The Korean Journal of Applied Statistics
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• v.32 no.1
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• pp.139-148
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• 2019

This study proposed a multiple intervention time series model to predict KTX passenger demand. In order to revise the research of Kim and Kim (Korean Society for Railway, 14, 470-476, 2011) considering only the intervention of the second phase of Gyeong-bu before November of 2011, we adopted multiple intervention seasonal ARIMA models to model the time series data with additional interventions which occurred after November of 2011. Through the data analysis, it was confirmed that the effects of various interventions such as Gyeong-bu and Ho-nam 2 phase, outbreak of MERS and national holidays, which affected the KTX transportation demand, are successfully explained and the prediction accuracy could be quite improved significantly.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.14 no.4
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• pp.62-72
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• 2015

Nowadays the issue of aging society has received considerable critical attention, especially in transportation planning and demand forecasting. This study identified the factors related to travel time budget for elderly by purpose using seemingly unrelated regression model (SUR model). The SUR model is suitable when error terms of each equation are assumed to be correlated across the equations in terms of travel time budget which is constant in 2 hours per day commonly. The results showed that elderly's travel time budget was affected by individual, household, urban facility and transportation service. The leisure travel comprised a large proportion of total travel time and had a positive relationship with elderly, sports, religious facilities. Moreover, the elderly who had low income or unemployed person had low frequency of social activity such as leisure, shopping and business. This study can provide a comprehensive implications of forecasting the future travel demand and analyzing the travel behavior.

• The Korean Journal of Applied Statistics
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• v.29 no.4
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• pp.581-594
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• 2016

We introduce innovations state space exponential smoothing models (ISS-ESM) that can analyze time series with multiple seasonal patterns. Especially, in order to control complex structure existing in the multiple patterns, the model equations use a matrix consisting of seasonal updating parameters. It enables us to group the seasonal parameters according to their similarity. Because of the grouped parameters, we can accomplish the principle of parsimony. Further, the ISS-ESM can potentially accommodate any number of multiple seasonal patterns. The models are applied to predict electricity demand in Korea that is observed on hourly basis, and we compare their performance with that of the traditional exponential smoothing methods. It is observed that the ISS-ESM are superior to the traditional methods in terms of the prediction and the interpretability of seasonal patterns.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.3
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• pp.617-631
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• 2019

In the four-step demand model, a gravity mode is used most commonly at the trip distribution stage. The purpose of this study was to develop a new friction factor that can express the accessibility property as a single friction factor to compensate for the variable limits of the gravity model parameters (travel time, travel cost). To derive a new friction factor, a new friction factor was derived using the space syntax that can quantify the characteristics of the urban space structure, deriving the link-unit integration degree and then using the travel time and travel distance relationship. Calibration of the derived friction factor resulted in a similar level to that of the existing friction factor. As a result of verifying the various indicators, the explanatory power was found to be excellent in the short - and long - distance range. Therefore, it is possible to derive and apply the new friction factor using the integration index, which can complement the accessibility beyond the limit of the existing shortest distance, and it is believed to be more advantageous in future utilization.

• Journal of Korea Trade
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• v.25 no.4
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• pp.17-36
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• 2021

Purpose - This study provides useful information to stakeholders by forecasting the tramp shipping market, which is a completely competitive market and has a huge fluctuation in freight rates due to low barriers to entry. Moreover, this study provides the most effective parameters for Baltic Dry Index (BDI) prediction and an optimal model by analyzing and comparing deep learning models such as the artificial neural network (ANN), recurrent neural network (RNN), and long short-term memory (LSTM). Design/methodology - This study uses various data models based on big data. The deep learning models considered are specialized for time series models. This study includes three perspectives to verify useful models in time series data by comparing prediction accuracy according to the selection of external variables and comparison between models. Findings - The BDI research reflecting the latest trends since 2015, using weekly data from 1995 to 2019 (25 years), is employed in this study. Additionally, we tried finding the best combination of BDI forecasts through the input of external factors such as supply, demand, raw materials, and economic aspects. Moreover, the combination of various unpredictable external variables and the fundamentals of supply and demand have sought to increase BDI prediction accuracy. Originality/value - Unlike previous studies, BDI forecasts reflect the latest stabilizing trends since 2015. Additionally, we look at the variation of the model's predictive accuracy according to the input of statistically validated variables. Moreover, we want to find the optimal model that minimizes the error value according to the parameter adjustment in the ANN model. Thus, this study helps future shipping stakeholders make decisions through BDI forecasts.

• Journal of Internet Computing and Services
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• v.23 no.6
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• pp.87-96
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• 2022

This paper is an empirical analysis as a reference model that can predict up to the maximum number of elementary school student care needs in local governments across the country. This study analyzed and predicted the characteristics of the region based on machine learning to predict the demand for elementary care in a new apartment complex. For this purpose, a total of 292 variables were used, including data related to apartment structure, such as number of parking spaces per household, and building-to-land ratio, environmental data around apartments such as distance to elementary schools, and population data of administrative districts. The use of various variables is of great significance, and it is meaningful in complex analysis. It is also an empirical case study that increased the reliability of the model through comparison with the actual value of the basic local government.

• Journal of Distribution Science
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• v.20 no.11
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• pp.109-120
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• 2022

Purpose: We would like to identify the appropriate size of stores in the commercial district suitable for the era of low growth. In addition, it is intended to present alternatives to prevent gentrification along with measures to revitalize commercial districts according to the selection of appropriate stores. Research design, data and methodology: The importance and commercial district usage patterns were identified through surveys by consumers and sellers. the demand and size of the commercial area were calculated based on the floating population and resident registration population. In addition, based on this, through metric analysis, the importance of the business district activation plan and what important matters can prevent gentrification were analyzed. Result: In this the study, 555 stores are currently operating in the target area, but it is seen as a commercial district with a scale that can operate 136 stores and 938 stores. In addition, it was analyzed that the Commercial Lease Protection Act needs to be strengthened to prevent gentrification. Conclusions: Due to the nature of small and medium-sized cities in Korea, commercial districts that have once lost their resilience must take much effort to find vitality. It is believed that local commercial districts will have resilience when diagnosis and recovery measures are adequately presented.