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

• Journal of Korean Society of Transportation
• /
• v.27 no.2
• /
• pp.83-91
• /
• 2009

This paper aims to predict air passenger and air freight demands in the air routes between South and North Korea. The air demands will be fostered by the visitors of Pyeongyang and Baekdu Mountain, whose forecasts will be used for supplying the air traffic services necessary for the active exchange and cooperation between South and North Korea in the future. The authors use the tool of regression analysis under the assumption of epoch-making progress in demand for aviation in accordance with the exchange and cooperation scenario between South and North Korea. After predicting the total number of travelers through regression analysis, the authors applied the share of air passengers among total travelers in order to predict the number of air passengers. Finally, the number of flights of each airport and route were forecasted by including the air freight, estimated from the number of air passengers.

• Journal of the Korean Society for Aviation and Aeronautics
• /
• v.31 no.1
• /
• pp.99-106
• /
• 2023

글로벌 팬데믹 사태는 항공 수요에 부정적인 영향을 끼치는 요소 중 하나다. 글로벌 팬데믹으로 인해 한국은 2020년과 2021년의 항공 승객 수가 2019년 대비 각각 68.1%와 47% 감소했다. 본 연구는 지난 20여년 동안 발생한 4대 팬데믹 특성을 분석, 전염병의 영향을 연구하는 것을 목표로 한다. SARS, H1N1, MERS 및 COVID-19의 발생기간 동안 한국의 항공 여객 및 화물 수요에 대한 실증 데이터를 활용하여 영향력을 분석한다. 또한 머신러닝 회귀 모델을 구축하여 향후 발생할 다른 전염병 대한 항공 수요를 예측하고자 한다. 연구 결과, 전염병이 항공 운항편수와 승객에 부정적인 영향을 미친다는 사실을 발견하였다. 반면화물 수송에는 긍정적인 영향을 미친다는 분석 결과를 도출하였다. 본 분석에 활용되는 회귀 모델은 팬데믹 기간 동안 항공수요를 예측하는 데 평균 86.8%의 기능을 보였다. 또한 본 연구는 특정 국가의 팬데믹 상황보다 전 세계적인 팬데믹 상황이 항공 운송 수요에 더 많은 영향을 미친다는 것을 보여준다.

• Aerospace Industry
• /
• v.61
• /
• pp.36-39
• /
• 1998

21세기에는 항공여객의 수송 수요가 비약적으로 증가할 것으로 예측되고 있다. 수요의 한쪽은 수송의 대형화이며 다른 한쪽은 수송의 고속화이다. 이런 수요를 충족하기 위해서는 경제성과 환경친화성을 겸비한 차세대 수송기 개발이 오래전부터 요망되어 항공기 제조 선진 각국은 한걸음 한걸음 이들 수요에 부응할 신종항공기의 개발을 추진중에 있다.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2021.01a
• /
• pp.281-282
• /
• 2021

본 논문에서는 항공 수요예측을 위한 빅데이터 기반 플랫폼의 설계 및 실증 결과를 제시한다. 항공 수요예측 통합 플랫폼은 항공산업 관련 데이터를 Open API, RSS Feed, 웹크롤러(Web Crawler) 등을 이용하여 수집 및 분석하여 자체 개발한 항공 수요예측 알고리즘을 기반으로 결과를 시각화하여 보여주도록 구현되어 있다. 또한, 제안하는 플랫폼의 사용자 인터페이스를 통해 변수 설정을 하여 단위별(Global, National 등), 기간별(단기, 중장기 등), 유형별(여객, 화물 등) 예측 통계 자료를 도출할 수 있다. 플랫폼의 성능 검증을 위해 정형화된 데이터를 비롯하여 소셜네트워크서비스(SNS), 검색엔진 등에서 수집한 비정형 데이터까지 활용하여 특정 키워드의 빈도와 특정 노선에 대한 항공 수요간 상관관계를 분석하였다. 개발한 통합 플랫폼의 지능형 항공 수요예측 알고리즘을 통해 전반적인 공항 운영 및 공항 운영 정책 수립에 기여할 것으로 예상한다.

• Communications for Statistical Applications and Methods
• /
• v.17 no.3
• /
• pp.473-481
• /
• 2010

Forecasting for air demand such as international passengers and freight has been one of the main interests for air industries. This research has mainly focus on the comparison of the performances of the multivariate time series models. In this paper, we used real data such as exchange rates, oil prices and export amounts to predict the future demand on international passenger and freight.

• The Korean Journal of Applied Statistics
• /
• v.22 no.5
• /
• pp.1007-1017
• /
• 2009

Forecasting for air demand such as passengers and freight has been one of the main interests for air industries. This research has mainly focus on the comparison the performance between the univariate seasonal ARIMA models and the multivariate time series models. In this paper, we used real data to predict demand on international passenger and freight. And multivariate time series models are better than the univariate models based on the accuracy criteria.

• Journal of the Korean Society for Aviation and Aeronautics
• /
• v.27 no.1
• /
• pp.43-50
• /
• 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 Korean Society of Transportation
• /
• v.31 no.3
• /
• pp.3-18
• /
• 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 Korean Society of Transportation
• /
• v.35 no.2
• /
• pp.143-159
• /
• 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.