Browse > Article
http://dx.doi.org/10.7470/jkst.2014.32.4.401

Air Passenger Demand Forecasting and Baggage Carousel Expansion: Application to Incheon International Airport  

Yoon, Sung Wook (School of Business, Kwangwoon University)
Jeong, Suk Jae (School of Business, Kwangwoon University)
Publication Information
Journal of Korean Society of Transportation / v.32, no.4, 2014 , pp. 401-409 More about this Journal
Abstract
This study deals with capacity expansion planning of airport infrastructure in view of economic validation that reflect construction costs and social benefits according to the reduction of passengers' delay time. We first forecast the airport peak-demand which has a seasonal and cyclical feature with ARIMA model that has been one of the most widely used linear models in time series forecasting. A discrete event simulation model is built for estimating actual delay time of passengers that consider the passenger's dynamic flow within airport infrastructure after arriving at the airport. With the trade-off relationship between cost and benefit, we determine an economic quantity of conveyor that will be expanded. Through the experiment performed with the case study of Incheon international airport, we demonstrate that our approach can be an effective method to solve the airport expansion problem with seasonal passenger arrival and dynamic operational aspects in airport infrastructure.
Keywords
ARIMA model; baggage carousel capacity expansion; cost-benefit; passenger forecasting; simulation analysis;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Ministry of Land, Infrastructure and Transport (2011), Appraisal Guidelines for Transport Facilities Investment (Forth Edition) 교통시설 투자평가지침 제4차 개정.
2 Ronzani Borille G. M., Correia A. R. (2013), A method for evaluating the level of service arrival components at airports, J Air Transp Manag., 27, 5-10.   DOI   ScienceOn
3 Solar S., Clarke J. P. B., Johnson E. L. (2009), Airport terminal capacity planning, Transport Res B., 43(6), 659-676.   DOI   ScienceOn
4 Suryani E., Chou S. Y., Chen C. H. (2010), Air passenger demand forecasting and passenger terminal capacity expansion: A system dynamics framework, Expert Syst Appl., 37(3), 2324-2339.   DOI   ScienceOn
5 The Korea Transport Institute (2006), A Study on Restructuring of the Local Airports in Korea, 63.
6 Tsui W. H. K., Ozer Balli H., Gilbey A., Gow H. (2014), Forecasting of Hong Kong airport's passenger throughput, Tourism Manage., 42, 62-76.   DOI   ScienceOn
7 Andreoni A., Postorino M. (2006), A multivariate ARIMA model to forecast air transport demand, Proceedings of the Association for European Transport and Contributors, 1-14.
8 Box and Jenkins (1976), Time series analysis: Forecasting and control, Holden Bay, San Francisco, 10
9 Chen C., Chang Y., Chang Y. (2009), Seasonal ARIMA forecasting of inbound air travel arrivals to Taiwan, Transportmetrica 5, 125-140.   DOI
10 Jorge J. D., De Rus G. (2004), Cost-benefit analysis of investments in airport infrastructure: a practical approach, J Air Transp Manag., 10(5), 311-326.   DOI   ScienceOn
11 Kelton W. D., Sadowski R. P., Sturrock D. T. (2004), Simulation with Arena, 3d ed., McGraw-Hill, New York.
12 Kulendran N., Witt S. F. (2003), Forecasting the demand for international business tourism, J Travel Res., 41(3), 265-271.   DOI
13 Lewis C. D. (1982), Industrial and business forecasting methods, London: Butterworths, 37.