시계열 모형을 이용한 항공수요 예측에 대한 실증연구 = (A) study on the air travel demand forecasting using time-series model

다국어 초록 (Multilingual Abstract)

Abstract

The demand for world-wide air traffic has increased about 5% per year for the past 20 years. This trend will continue for the next 20 years due to the global Open Sky Agreement. Air demand forecasting will be a very valuable indicator for the development of the world economy and industry for the future.

Demand forecasting is a technique to predict and analyze the various data from several different fields. In case the statistic demand forecasting among the demand forecasting technique is used in the air demand forecasting, the high reliance of the statistic forecasting would be the most confident.

This study is on the forecasting methodologies for the future international air passenger and air cargo traffic using the past air traffic data through the univariate seasonal Autoregressive Integrated Moving Average(ARIMA) models of Box and Jenkins(1976). To solve the non stationarity of the data especially for seasonality, seasonal differencing method has been adopted.

To explain and modeling the cooperative and movable relations between the univariate seasonal variables of ARIMA, multivariate time series model has been applied. To decide the differencing of the data selected from ARIMA, vector time series models are used. The multivariate time series models are regarded as general expansion of ARIMA uses. The air demand forecasting adopted multivariate time series model such as Vector AR(VAR) model and they are used to forecast the performance evaluation between international air passenger and cargo demand.

ARIMA and VAR models with actual air demands of international air traffic for passenger and cargo would be taken to show the accuracy between the univariate seasonal and multivariate time series models. And the elucidation of excellence for the multivariate time series model(VAR) than univariate seasonal model(ARIMA) is suggested.

This is very valuable and useful study because it is not only limited to air demand forecasting, in case there is past data with seasonal and repeating characteristic for the future, any kinds of demand forecasting is available to apply.

목차 (Table of Contents)

• Index
• Chapter 1 Introduction ··········································································· 1
• 1.1 Background ············································································ 1
• 1.2 Objective ············································································ 2
• 1.3 Methodology and Significance ························································· 4
• 1.3.1 Methodology and Data ······························································ 4
• 1.3.2 Significance ············································································· 4
• 1.4 Composition ·················································································· 5
• Chapter 2 Theoretic Background and Survey of Previous Study of Air Travel Demand Forecasting ··························· 6
• 2.1 Meaning of Demand Forecasting ················································· 6
• 2.1.1 Concept of Demand Forecasting ··············································· 6
• 2.1.2 Function of Demand Forecasting ·············································· 7
• 2.1.3 Methodic Organization of Demand Forecasting ·························· 7
• 2.2 Forecasting Method of Air Demand ··········································· 11
• 2.2.1 Judgement Technique of Specialists ······································ 12
• 2.2.2 Time-Series Models ··········································· 13
• 2.2.3 Market Investigation ···························································· 17
• 2.2.4 Econometric methods ··························································· 19
• 2.2.5 Modal Split Models ······························································ 21
• 2.2.6 Airlines Market Share ratio Models ····································· 22
• 2.3 Characteristic and Decision Factors of Air Demand ······················ 23
• 2.3.1 Characteristic ······························································· 23
• 2.3.2 Decision Factors ······························································· 27
• 2.4 Study of Domestic Air Demand ················································· 38
• 2.4.1 Previous Studies of Air Demand Forecasting ··························· 38
• 2.4.2 Example of Air Demand Forecasting ································· 39
• 2.4.3 Comparison Study of Forecasting Models of Air Demand ······· 42
• 2.4.4 Examples of Domestic Air Demand Elasticity Analysis ············ 45
• 2.5 Air Demand Forecasting of International Aviation Organization ······· 48
• 2.5.1 Demand Forecasting and Supply of ICAO ························ 49
• 2.5.2 Air Demand Forecasting of ACI ································· 55
• 2.5.3 Air Travel Forecasting of IATA ····························· 59
• Chapter 3 Air Demand Forecasting using Time-Series Models ················ 62
• 3.1 Comparison of Models for Air Travel Demand Forecasting 62
• 3.1.1 Data ···················································································· 62
• 3.1.2 Construction and Suitability of Forecasting Models ·················· 63
• 3.1.3 Evaluate of Model's Suitability ································ 78
• 3.1.4 Result of Analysis ······························································· 82
• 3.2 Comparison of Models for International Air Demand Forecasting ···· 83
• 3.2.1 Data ····················································································· 83
• 3.2.2 Suitability Result of Forecasting Models ································ 83
• 3.2.3 Suitability of Forecasting Seasonal ARIMA and VAR Models ····· 97
• Chapter 4 Conclusion ····································································· 102
• 4.1 Conclusion and Suggestion of Study ·········································· 102
• 4.1.1 Abstract and Conclusion of Study ·································· 102
• 4.1.2 Suggestion of Study ····················································· 102
• 4.2 Limitation and Future Direction of Study ························ 102

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