機場運量需求預測模式之研究—南部國際機場 = The Study of ...
國立高雄大學都市發展與建築研究所

 

  • 機場運量需求預測模式之研究—南部國際機場 = The Study of Air Traffic Forecasting Model—The Southern International Airport
  • 紀錄類型: 書目-語言資料,印刷品 : 單行本
    並列題名: The Study of Air Traffic Forecasting Model—The Southern International Airport
    作者: 李婉婷,
    其他團體作者: 國立高雄大學
    出版地: [高雄市]
    出版者: 撰者;
    出版年: 2009[民98]
    面頁冊數: 106面圖、表 : 30公分;
    標題: ARIMA模式
    標題: ARIMA model
    電子資源: http://handle.ncl.edu.tw/11296/ndltd/70733350542405243640
    附註: 參考書目:面
    附註: 指導教授:王瑞民
    摘要註: 準確的運量預測不但可以提供規劃人員做為運輸規劃的依據,亦可做為各項重大投資建設與各類作業計畫之參考,如:航點與航線選擇、機隊規模與人力配置等問題;短期預測可以作為機場短期營運策略調整的參考依據因為可以偵測預期最新的市場變化。本研究將針對南部國際機場運量需求特性,以時間數列的方法,分析與預測各項影響航空運量之因素。本研究回顧國內外各研究領域,利用時間數列模式為研究主題之各類相關文獻,並就該研究之重點與應用於本研究之概念與模式構建基礎加以歸納。針對本研究主題,分析南部國際機場運量需求特性,並以高雄國際機場為例,蒐集旅運、貨運及航機架次資料,依據資料特性及內容,將資料分類方法加以整理,並利用敘述統計分析法與圖表方式,敘述高雄國際機場之運量需求特性與運量變動的情形,並探討國內外重大事件與顯著衝擊事件與運量需求之間的影響程度。在時間數列模式方面,本研究利用 Box and Jenkins 在1970 年所提出的自我迴歸移動平均整合模式(ARIMA)構建運量需求模式,預估模式中各變數之參數值,並分析參數的意義及合理性。針對本研究所構建的自我迴歸移動平均整合模式進行實證分析,蒐集民國93年至97年高雄國際機場的運量月資料,建構短期的需求預測模式,將模式所預測出來的運量需求量與實際資料作比較,並討論模式的預測準確度與預測能力。經研究發現高雄國際機場的旅運量與兩岸三通及國際油價的波動並無顯著影響,而國際油價的漲跌與高雄國際機場國際入境、出境及國內到站貨運量卻有相互的影響,其原因除國際貨物運輸距離較長油價占整體運輸成本比例較高外,國內貨運運輸因運輸距離較短,運具選擇替代性較高亦為可能之因素。至於兩岸三通政策對於高雄國際機場國際出境人數有顯著影響,其原因可能為此政策的發生時間點為春節期間,國人於春節期間出國旅遊的風氣極為盛行,故造成此模式預測之結果。此外,研究發現造成機場旅貨運量波動原因為多因素的複合結果,本研究的方法可提供簡單可靠的短期分析預測模式,其結果可作為政府單位或營運單位於擬定短期計畫施政及營運計畫時之參考依據。 Accurate traffic forecast not only can be used for transportation planning but also be referred by large scale development planning such as selection of route and airports, size of fleet, and allocation of human resources etc. Short term traffic forecast can provide airport’s short term operational strategy adjustment aiming the predicted short term market. This study is to use time series method to analyze each of key factors for the traffic forecasting based on the unique characteristics of the southern international airport. The study reviewed and summarized domestic and foreign pushed literatures for the comparison of key issues against this study’s concept and model which lead to the conclusions. The core of this study was to analyze the characteristics of the southern international airport traffic forecast. The data was from Kaohsiung International Airport such as passenger and cargo flight numbers then classify the collected data, and then the results were presented by using descriptive statistics and charts. Otherwise, major incident events and traffic demand were also included in the model as intervening variables.The study adopted “Auto-Regressive Integrated moving Average” model published by Box and Jenkins in 1970 for time serious modeling and that lead to the forecasting of variable factors. These variable factors were analyzed for meanings and rationalities. For validation of the short-term demand forecast modeling results, monthly traffic data from 2004 to 2008 were collected for comparison, analysis for its accuracy and capability of the short-term forecasting. The study found that traffic volume at Kaohsiung International Airport had not been impacted by the opening up of direct flight between Taiwan and China and the international oil price hiking but there was inter-relationship between the oil price and quantities of the depart/arrival and the arrival cargo. The important factors could be the higher fuel cost for the longer distance of international flights and more non-air shipping alternatives of the domestic cargo. Regarding the increase of international departures at Kaohsiung International Airport was because of the timing of directly flight policy become effect during the Spring Festival holidays whilst most people would take overseas tours during the holidays, thus the phenomenon fit well of the short-term modeling results. In addition, the study showed that the fluctuation of the traffic volume was resulted by compounding effect of factors. The model provided a reliable short-term forecast and suggestions for the authority or the airport management to develop their short-term management strategies and operational planning.
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