• Proceedings of the KSR Conference
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• 2011.05a
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• pp.1282-1289
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• 2011

The rail passenger demand for the railroad operations required a short-term demand rather than a long-term demand. The rail passenger demand can be classified according to the purpose. First, the rail passenger demand will be use to the restructure of line planning on the current operating line. Second, the rail passenger demand will be use to the line planning on the new line and purchasing the train vehicles. The objective of study is to analyze the travel behavior of rail passenger for modeling of short-term demand forecasting. The scope of research is the passenger of KTX. The travel behavior was analyzed the daily trips, origin/destination trips for KTX passenger using the ANOVA and the clustering analysis. The results of analysis provide the directions of the short-term demand forecasting model.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.27 no.3
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• pp.98-105
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• 2019

In the past 30 years, our aviation demand has been growing continuously. As such, the importance of the demand forecasting field is increasing. In this study, the factors influencing Korea's international air demand were selected, and the international air demand was analyzed, forecasted and reviewed through OLS multiple regression analysis. As a result, passenger demand was affected by GDP per capita, oil price and exchange rate, while cargo demand was affected by GDP per capita and private consumption growth rate. In particular, passenger demand was analyzed to be sensitive to temporary external shocks, and cargo demand was more affected by economic variables than temporary external shocks. Demand forecasting, OLS multiple regression analysis, passenger demand, cargo demand, transient external shocks, economic variables.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.27 no.1
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• pp.43-50
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• 2019

The aviation demand forecast field has been actively studied along with the recent growth of the aviation market. In this study, the demand for domestic passenger demand and freight demand was estimated through cross-validation method. As a result, passenger demand is influenced by private consumption growth rate, oil price, and exchange rate. Freight demand is affected by GDP per capita, private consumption growth rate, and oil price. In particular, passenger demand is characterized by temporary external shocks, and freight demand is more affected by economic variables than temporary shocks.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.27 no.4
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• pp.52-64
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• 2019

The purpose of this study is to analyze the impact of macroeconomic variables on air passenger demand and provide useful information to airport managers and policymakers. Therefore, using the quarterly macroeconomic indicators from 2002 to 2017, the relationship with air passenger demand was demonstrated by multiple regression analysis. In the previous studies, they used GDP, Korea Treasury Bond, KOSPI index, USD/KRW Exchange Rate, and WTI Crude Oil Price variables. In this study, we used the Coincident Composite Index, Employment Rate, Consumer Sentiment Index, and Private Consumption Rate used as additional variables. It has confirmed that if the consumption of research results expands or the economic environment is right, it will affect the increase in international passengers. In other words, it confirmed that the overall economic situation acts as the main factor determining air passenger demand. It confirmed that the economic environment at the past has a significant impact on air passenger demand.

• Journal of Digital Convergence
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• v.16 no.5
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• pp.137-143
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• 2018

Korea's air passenger traffic has been growing steadily. In this paper, we propose a forecasting model of air passenger demand to ascertain the growth trend of air passenger transportation performance in Korea. We conducted a simulation based on System Dynamics with the demand as a dependent variable, and international oil prices, GDP and exchange rates as exogenous variables. The accuracy of the model was verified using MAPE and $R^2$, and the proposed prediction model was verified as an accurate prediction model. As a result of the demand forecast, it is predicted that the air passenger demand in Korea will continue to grow, and the share of low cost carriers will increase sharply. The addition of the Korean transportation performance of foreign carriers in Korea and the transportation performance of Korean passengers due to the alliance of airlines will provide a more accurate forecast of passenger demand.

• Proceedings of the KSR Conference
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• 2011.10a
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• pp.1715-1721
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• 2011

For an efficient railroad operations the demand forecasting is required. Time series models can quickly forecast the future demand with fewer data. As well as the accuracy of forecasting is excellent compared to other methods. In this study is proposed the intervention ARIMA model for forecasting methods of KTX passenger demand. The intervention ARIMA model may reflect the intervention such as the Kyongbu high-speed rail project second phase. The simple seasonal ARIMA model is predicted to overestimate the KTX passenger demand. However, intervention ARIMA model is predicted the reasonable results. The KTX passenger demands were predicted to be a week units separated by the weekday and weekend.

• Proceedings of the KSR Conference
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• 2001.10a
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• pp.111-116
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• 2001

This paper introduces how to adopt the X-12-ARIMA to decompose the railway passenger demand of the Korea National Railroad Especially, selecting on proper filters is focused. The trend filter is identical to the low pass filter in the signal Processing field, and so the seasonal filter is to band pass filter too. Some considerations, selecting a filter, are provided from the view-point of the spectrum analysis. The technique introduced in this paper will be adopted to the project that is to develope the forecasting system of Korea railway passenger demand which is a part of the high speed rail information system.

• Journal of the Korean Society for Railway
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• v.17 no.4
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• pp.307-312
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• 2014

This study suggested the ARIMA model taking into consideration the seasonal characteristic factor as a method for efficiently forecasting passenger transport demand of the Joongang Line. The forecasting model was built including the demand for the central inland region tourist train (O-train, V-train), which was opened to traffic in April-, 2013 and run in order to reflect the recent demand for the tourism industry. By using the monthly time series data (103) from January-, 2005 to July-, 2013, the optimum model was selected. The forecasting results of passenger transport demand of the Joongang Line showed continuous increase. The developed model forecasts the short-term demand of the Joongang Line.

• Journal of Korea Port Economic Association
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• v.40 no.1
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• pp.119-131
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• 2024

This study examines the interrelationship between coastal passenger demand and fares for 101 coastal passenger routes in Korea during the 2018 to 2022 period. The two-stage least squares method through a panel data simultaneous equations model was estimated to the effects of individual route characteristics and regional characteristics on the performance and fares of coastal passenger transportation. The estimated results indicate that the endogenous variable, fare, and the exogenous variables, route characteristics, route distance, and the instrumental variable, frequency, affect the demand for coastal passengers. In the short-run pricing function, the exogenous variables, capacity, speed, and route distance, as well as the endogenous variable, coastal passenger transportation performance, affect the coastal passenger fare. This study is expected to provide useful implications for domestic coastal passenger demand and pricing in relation to coastal passengers.

• Proceedings of the KSR Conference
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• 2001.10a
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• pp.97.1-103
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• 2001

Railway passenger demand forecasts may be used directly, or as inputs to other optimization model which is use the demand forecasts to produce estimates of other activities. The optimization models require demand forecasts at the most detailed level. In this environment exponential smoothing forecasting methods such as Holt-Winters are appropriate because it is simple and inexpensive in terms of computation. There are several initialization methods for Holt-Winters Model. The purpose of this paper is to compare the initialization methods for Holt-Winters model.