• 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.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.06a
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• pp.204-206
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• 2022

The global economy and the air transport business have been affected since the spread of the COVID-19 pandemic. As countries tighten restrictions on international movements, the growing emphasis on air cargo puts pressure on airports to maintain and upgrade their cargo policies, facilities, and operations. Hence, ensuring the competitiveness of cargo airports becomes pivotal for airports survival under the volatile global demand. The study aims to evaluate the importance of the competitiveness factors for cargo airports and identify areas for further improvement. The study applies the Best-Worst Method (BWM) to assess the cargo airports' competitiveness factors: 'Transport Capacity,' 'Airport Operations and Facility Capacity,' 'Economic Growth,' 'Financial Performance,' and 'Airport Brand Value.' The selected airports include Heathrow Airport, Aéroport de Paris-Charles de Gaulle, Hong Kong International Airport, and Incheon International Airport. The results identify 'Transport Capacity' as the most significant competitiveness factor, and Hong Kong International Airport the best performing cargo airport. This research forms a reference framework for evaluating cargo airports' competitive position, which may help identify airports' relative strengths and weaknesses. Moreover, this framework can also serve as a tool facilitating the strategic design of airports that may accommodate both air cargo and passenger demand flexibly under the demand uncertainty.

• Journal of the Korea Society of Computer and Information
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• v.13 no.7
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• pp.281-289
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• 2008

In this study, a discrete-event simulation model is developed to estimate load factors and the corresponding revenues under different pricing and dispatching policies. The model has been employed to forecast the inbound and the outbound air cargo demands of the major Northeastern Chinese cities, and the simulation results were compared to the actual demands obtained from real-life airline operations. The statistical analysis confirms that the simulation model is able to provide accurate estimates for air cargo demands, and thus, the model may be employed to be a useful tool for air cargo demand management.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2000.11a
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• pp.7-14
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• 2000

The purpose of this study is to improvement strategies for transportation systems of container cargo in Busan port. Therefore, it was forecasted the future container cargo demand using logistic curve formula. In 2011, container cargo demand was forecasted 8.791 million TEU(T/S including 12.559 million TEU). In order to improvement transportation systems of container cargo, this study presented following; $\circled1$ port facilities expansion, $\circled2$ diversity of container transport modes. $\circled3$ make up ICD and exclusive container roads, $\circled4$ the second Seoul-Busan Expressway.

• Journal of Korea Port Economic Association
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• v.37 no.1
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• pp.71-85
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• 2021

A variety of methods have been proposed through a number of studies on sophisticated demand forecasting models that can reduce logistics costs. These studies mainly determine the applicable demand forecasting model based on the pattern of demand quantity and try to judge the accuracy of the model through statistical verification. Demand patterns can be broadly divided into regularity and irregularity. A regular pattern means that the order is regular and the order quantity is constant. In this case, predicting demand mainly through regression model or time series model was used. However, this demand is called "intermittent demand" when irregular and fluctuating amount of order quantity is large, and there is a high possibility of error in demand prediction with existing regression model or time series model. For items that show intermittent demand, predicting demand is mainly done using Croston or HOLTS. In this study, we analyze the demand patterns of various items of air cargo with intermittent patterns and apply the most appropriate model to predict and verify the demand. In this process, intermittent optimal demand forecasting model of air cargo is proposed by analyzing the fit of various models of air cargo by item and region.

• The Journal of Bigdata
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• v.8 no.2
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• pp.165-175
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• 2023

LNG is a representative imported cargo at Incheon Port and has a relatively high contribution to the increase/decrease in overall cargo volume at Incheon Port. In addition, in the view point of nationwide, LNG is the one of the most important key resource to supply the gas and generate electricity. Thus, it is very essential to identify the factors that have impact on the demand fluctuation and build the appropriate forecasting model, which present the basic information to make balance between supply and demand of LNG and establish the plan for power generation. In this study, different to previous research based on macroscopic annual data, the weekly demand of LNG is converted from the cargo volume unloaded by LNG carriers. We have identified the periodicity and correlations among internal and external factors of demand variability. We have identified the input factors for predicting the LNG demand such as seasonality of weekly cargo volume, the peak power demand, and the reserved capacity of power supply. In addition, in order to predict LNG demand, considering the characteristics of the data, time series prediction with weekly LNG cargo volume as a dependent variable and prediction through an artificial neural network model were made, the suitability of the predictions was verified, and the optimal model was established through error comparison between performance and estimates.

• Journal of Korean Society of Transportation
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• v.35 no.2
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• pp.143-159
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• 2017

For forecasting air cargo demand from Incheon National Airport to all of airports in the United States (US), this study employed the Seasonal Autoregressive Integrated Moving Average (SARIMA) method and the time-series data collected from the first quarter of 2003 to the second quarter of 2016. By comparing the SARIMA method against the ARIMA method, it was found that the SARIMA method performs well, relatively with time series data highlighting seasonal periodic characteristics. While existing previous research was generally focused on the air passenger and the air cargo as a whole rather than specific air routes, this study emphasized on a specific air cargo demand to the US route. The meaningful findings would support the future research.

• Journal of Korean Society of Transportation
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• v.31 no.3
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• pp.3-18
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• 2013

This study develops a forecasting method to estimate air cargo demand from ICN(Incheon International Airport) to all airports in EU with Seasonal Autoregressive Integrated Moving Average (SARIMA) Model using volumes from the first quarter of 2000 to the fourth quarter of 2009. This paper shows the superiority of SARIMA Model by comparing the forecasting accuracy of SARIMA with that of other ARIMA (Autoregressive Integrated Moving Average) models. Given that very few papers and researches focuses on air route, this paper will be helpful to researchers concerned with air cargo.

• THE INTERNATIONAL COMMERCE & LAW REVIEW
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• v.69
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• pp.371-388
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• 2016

International air transport for cargo services is a facilitator for various trade sectors and, by itself, an important service industry. Although international air cargo industry is expected to grow continuously, industry stakeholders complain about legal constraints in the industry and demand more liberalized regime. From its birth at the beginning of the 20th century, the airline industry was tightly regulated by governments with a strong tradition of protectionism. In the past few decades, however, protectionism in the airline industry has steadily declined. Indeed, the airline industry is largely in the process of liberalization. Interestingly, it has been easier to liberalize air cargo service than passenger service. Indeed, states have traditionally shown far more willingness to provide market access for foreign carriers carrying cargo than passengers. Given the impact of air cargo service in a state's wider economy and own characteristics of cargo services (i.e. air cargo traffic is inherently one-way, unlike passenger traffic, which tends to involve round trips), more liberalized approach is necessary for air cargo services. Among three approaches: bilateral, regional (block-based) and multilateral, it is desirable to adopt a multilateral treaty (a new multilateral all-cargo agreement) so as to harmonize and simplify complicated trade regulations on air cargo services.

• Journal of the Korean Society for Railway
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• v.19 no.1
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• pp.97-108
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• 2016

Over 80% of Korea's cargo transport relies on public roads. Such over-reliance has created a range of social problems including environmental damage. Korea thus faces an urgent need to establish an alternative mode of cargo transportation. In view of the changing international logistics environment, railway is clearly a superior mode of transport. The prerequisite task today for Korea is to enhance the competitiveness of inland railway by switching to railway and spurring new demand for cargo. This study examines inland railway cargo transport from the demand perspective. Specifically, through the use of a survey, the study identifies key factors influencing the decisions of container shippers regarding the use of railway for cargo transport. The survey responses were statistically tested using Smart PLS structural equation modeling. The results indicate that attitude toward railway use had the strongest influence on choice, while the key variable affecting attitude formation was cost.