• 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 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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• 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 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.

• 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 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.

• 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.

• Journal of Navigation and Port Research
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• v.46 no.5
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• pp.449-457
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• 2022

The Port of Busan is currently ranked as the seventh largest container port worldwide in terms of cargo throughput. However, port competition in the Far-East region is fierce. The growth rate of container throughput handled by the port of Busan has recently slowed down. In this study, we analyzed how economic conditions and multiple external shocks could influence cargo throughput and identified potential implications for port business. The aim of this study was to build a model to accurately forecast port throughput using the ARIMA model, which could incorporate external socio-economic shocks, and the VEC model considering causal variables having long-term effects on transshipment cargo. Findings of this study suggest that there are three main areas affecting container throughput in the port of Busan, namely the Russia-Ukraine war, the increased competition for transshipment cargo of Chinese ports, and the weaker growth rate of the Korean economy. Based on the forecast, in order for the Port of the Port of Busan to continue to grow as a logistics hub in Northeast-Asia, policy intervention is necessary to diversify the demand for transshipment cargo and maximize benefits of planned infrastructural investments.

• Journal of Korea Port Economic Association
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• v.23 no.2
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• pp.25-40
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• 2007

The sea/air intermodal transport is a time-saving and cost-saving transport for cargo shipments. In reaction to a recent growth of high value-added products in China, the volume of sea/air intermodal transport in Korean airport has been increasing continuously. This paper treats the situation, the system, and the possibility of sea/air intermodal transport, which are emerging as an alternative to solve the logistics problem related to the increase of international air cargo demand in China. Also, several strategies are considered to develope Korea into intermodal transport hub including the value added logistics activity which will secure the demand of sea/air intermodal transport.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.29 no.4
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• pp.1-20
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• 2021

Due to the recent rapid increase in passenger and cargo air transport demand, the capacity of Jeju International Airport has been approaching its limit. Even though in COVID-19 crisis which has started from Nov 2019, Jeju International Airport still suffers from strong demand in terms of air passenger and cargo transportation. However, it is an undeniable fact that the delay has also increased in Jeju International Airport. In this study, we analyze the correlation between weather and delayed departure operation based on both datum collected from the historical airline operation information and aviation weather statistics of Jeju International Airport. Adopting machine learning techniques, we then analyze weather condition Jeju International Airport and construct a delay prediction model. The model presented in this study is expected to play a useful role to predict aircraft departure delay and contribute to enhance aircraft operation efficiency and punctuality in the Jeju International Airport.