菲律賓NAIA國內和國際空中交通的決定因素

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：62

、訪客IP：3.128.255.103

姓名

瑞諾索(Crisaulo Marquez Reynoso) 查詢紙本館藏

畢業系所

國際永續發展碩士在職專班

論文名稱

菲律賓NAIA國內和國際空中交通的決定因素
(Determinants of Domestic and International Air Traffic at NAIA, Philippines)

相關論文

★ 電廠工程工期相關衍生費用處理機制之研究	★ 應用建築資訊模型與傳統估價於工程數量計算效能比較之研究
★ 特種貨物稅及勞務稅條例對房地產的影響-以大臺北為例	★ 房地合一與所得稅改對於建設公司稅制利得與經營模式之個案研析
★ 營建工程現場之人力配置決策模式	★ 以財務比率導向建構使用衍生性金融商品避險之預測模式-以建設公司及營造廠為例
★ 生命週期管理導入軍艦採購模式之探討	★ 建築預鑄結構體生產難易度分級與分析之研究
★ 運用關聯法則探討預鑄構件生產派工之研究	★ Automatic Porosity Detection for Permeable Concrete using X-ray CT Images
★ 運用巨量資料與系統模擬建構預鑄產業人力彈性運用之決策評估模型	★ 運用深度神經網路建立H型鋼構件自動辨識系統之研究
★ 運用關聯法則探討協力廠商對營造廠報價行為之研究	★ 探討影響台灣工程顧問公司落實法律遵循反貪腐關鍵因素之研究
★ 以分包商角度探討對營造廠報價行為策略之研究	★ 運用SOMCM分群演算法開發設計雲端智慧平台之營運維護產業介面-以桃園市某園區為例

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

由於菲律賓的群島性質，尼諾·阿基諾國際機場 (NAIA) 經歷了來自國際和國內空中交通的大量空中交通。相應地，影響它們的因素也存在差異。因此，本研究旨在確定影響 NAIA 國內和國際客運量和貨運量的宏觀經濟因素。使用 2001 年至 2013 年的交通數據，連同九個潛在的宏觀經濟因素，形成一個 156 個月的模型訓練數據集。然後採用雙向逐步選擇的多元回歸來確定影響交通量的重要因素。結果顯示，國際航空旅客最重要的預測因素是旅客到達量，而國內旅客的主要決定因素是國際儲備總額。另一方面，貨量受貨幣匯率和出口數據等多種因素影響。本研究創建了四個方程，對應於國際航空客運和貨運模型及其國內對應模型。然後使用 2014 年至 2019 年為期 72 個月的測試集來評估每個模型的準確性。然後使用回歸結果預測 NAIA 到 2029 年 12 月的空中交通量。

摘要(英)

Due to the Philippines’ archipelagic nature, the Ninoy Aquino International Airport (NAIA) experiences high volumes of air traffic coming from both international and domestic air traffic. Correspondingly, there are differences in the factors influencing them. This study, therefore, sought to determine the macroeconomic factors affecting domestic and international passengers and cargo volume in NAIA. The traffic data from 2001 to 2013 were used, together with nine potential macroeconomic factors, to form a 156-month dataset for model training. Multiple regression with bi-directional stepwise selection was then employed to determine the significant factors affecting the traffic volumes. Results revealed that the most important predictor for international air passengers is Tourist Arrival volume, while the primary determinant for the domestic passenger is the Gross International Reserve. On the other hand, the cargo volumes are affected by several factors such as currency exchange rate and export figures. Four equations were created in this study, corresponding to the models for international air passenger and cargo and their domestic counterparts. A 72-month testing set, from 2014 to 2019, was then used to evaluate the accuracy of each model. The regression results are then used to forecast the air traffic in NAIA up to December 2029.

關鍵字(中)

★ 空中交通預測
★ 宏觀經濟因素
★ 計量經濟學模型

關鍵字(英)

★ air traffic forecasting
★ macroeconomic factors
★ econometric modelling

論文目次

Chinese Abstract - i
English Abstract - ii
Acknowledgement - iii
Table of Contents - iv
List of Figures - vi
List of Tables - vii
Chapter I Introduction - 1
1.1 Research Background - 1
1.2 Research Motive - 3
1.3 Objectives - 4
1.4 Scope of the Study 4

Chapter II Review of Literature - 7
2.1 Forecasting Models - 7
2.2 Economic Factors - 10
2.3 Air Traffic Volume - 12
2.3.1 Forecasting Form and Timeframe - 12
2.3.2 Towards Disaggregated forecasting - 14

Chapter III Variable Selection and Data Collection - 17
3.1 Variable Selection - 17
3.1.1 Correlation Analysis - 21
3.1.2 Regression Analysis - 21
3.1.3 Testing of Accuracy - 23
3.2 Data Collection and Basic Analysis - 23

Chapter IV Prediction of Air Traffic Volume - 28
4.1 Correlation Analysis - 28
4.2 Multiple Regression - 29
4.3 Comparison and Discussion - 32
4.3.1 Passenger Comparison - 32
4.3.2 Cargo Comparison - 33
4.3.3 Implications and comparison with other studies - 34
4.4 Testing and Validation - 36
4.5 Comparison with Aggregated Air Traffic Forecasting Model - 40
4.6 Forecasting - 43
4.5.1 Passenger Forecast - 44
4.5.2 Cargo Forecast - 45

Chapter V Summary and Future Works - 46
5.1 Summary - 46
5.2 Limitations and Future Work - 48

References - 49

參考文獻

[1] R.B. Carmona-Benítez and M. R. Nieto. “SARIMA damp trend grey forecasting model for airline industry,” Journal of Air Transport Management, vol. 82, 101736, 2020.

[2] T.M. Dantas, F.L.C. Oliveira, H.M.V. Repolho. “Air transportation demand forecast through Bagging Holt Winters methods,” Journal of Air Transport Management, vol. 59, pp. 116-123, 2017.

[3] M.R. Nieto and R.B. Carmona-Benítez. “ARIMA + GARCH + Bootstrap forecasting method applied to the airline industry,” Journal of Air Transport Management, vol. 71, pp. 1–8, 2018.

[4] F. Jin, Y. Li, S. Sun, and H. Li. “Forecasting air passenger demand with a new hybrid ensemble approach,” Journal of Air Transport Management, vol. 83, 101744, 2020.

[5] Y. Bao, T. Xiong, and Z. Hu. “Forecasting air passenger traffic by support vector machines with ensemble empirical mode decomposition and slope-based method,” Discrete Dynamics in Nature and Society. Volume 2012, 431512, 2012.

[6] G. Xie, S. Wang, and K.K. Lai. “Short-term forecasting of air passenger by using hybrid seasonal decomposition and least squares support vector regression approaches,” Journal of Air Transport Management, vol. 37, pp. 20-26, 2014.

[7] P. Srisaeng and G. Baxter. “Modelling Australia’s outbound passenger air travel demand using an artificial neural network approach,” International Journal for Traffic and Transport Engineering, vol. 7, no. 4, pp. 406-423, 2017.

[8] S. Sun, H. Lu, KL. Tsui, and S. Wang. “Nonlinear vector auto-regression neural network for forecasting air passenger flow,” Journal of Air Transport Management, vol. 78, pp. 54-62, 2019.

[9] JH. Chen, HH. Wei, CL. Chen, HY. Wei, YP. Chen, and Z. Ye. “A practical approach to determining critical macroeconomic factors in air-traffic volume based on K-means clustering and decision-tree classification,” Journal of Air Transport Management, vol. 82, 101743, 2020.

[10] M.C. Gelhausen, P. Berster, and D. Wilken. “A new direct demand model of long-term forecasting air passengers and air transport movements at German airports,” Journal of Air Transport Management, vol. 71, pp. 140-152, 2018.

[11] V. Profillidis and G. Botzoris. “Air passenger transport and economic activity,” Journal of Air Transport Management, vol. 49, pp. 23-27, 2015.

[12] E. Erraitab, A. Hefnaoui, M. Moutmihi. “A cointegration analysis of air travel demand: the case of international air travel demand between Morocco and European union,” International Journal for Traffic and Transport Engineering, vol. 6, no. 1, pp. 104-120, 2016.

[13] M. Dziedzic, E.T. Njoya, WS. David, and N. Hubbard. “Determinants of air traffic volumes and structure at small European airports,” Research in Transportation Economics, vol. 79, 100749, 2020.

[14] Z. Wadud. “Simultaneous modeling of passenger and cargo demand at an airport,” Journal of the Transportation Research Board, vol. 2336, pp. 63-74, 2013.

[15] T. Sailauov and Z.W. Zhong. “Air traffic forecasting using optimization for econometric models,” International Journal of Technology and Engineering Studies, vol. 3, no. 5, pp. 197-203, 2017.

[16] V. Valdes. “Determinants of air travel demand in middle income countries,” Journal of Air Transport Management, vol. 42, pp. 75-84, 2015.

[17] T. Boonekamp, J. Zuidberg, and G. Burghouwt. “Determinants of air travel demand: The role of low-cost carriers, ethnic links and aviation-dependent employment,” Transportation Research Part A: Policy and Practice, vol. 112, pp. 18-28, 2018.

[18] F. Dobruszkes, M. Lennert, and G.V. Hamme. “An analysis of the determinants of air traffic volume for European metropolitan areas,” Journal of Transport Geography, vol. 19, pp. 755–762, 2011.

[19] P. Beria and A. Laurino. “Determinants of daily fluctuations in air passenger volumes: The effect of events and holidays on Milan Malpensa airport,” Journal of Air Transport Management, vol. 53, pp. 73-84, 2016.

[20] T. Sefrus, S. Priyanto, Dewanti, and M.Z. Irawan. “Modeling of domestic air passenger demand in The Papua Islands,” Songklanakarin Journal of Science and Technology. vol. 42, no. 5, pp. 1071-1076, 2020.

[21] F. Kupfer, H. Meersman, E. Onghena, and E. V. de Voorde. “The underlying drivers and future development of air cargo,” Journal of Air Transport Management, vol. 61, pp.6-14, 2017.

[22] D. Raheja, R. Guo, and S.M. Phyoe, Y.X. Lee, Z.W. Zhong. “Air traffic and economic output: Projections for ASEAN,” International Journal of Business and Administrative Studies, vol. 3, no. 3, pp. 92-99, 2017.

[23] V. Suryan. “Econometric forecasting models for air traffic passenger of Indonesia,” Journal of the Civil Engineering Forum, vol. 3, no. 1, pp. 33-44, 2017.

[24] R. T. Carson, T. Cenesizoglu, and R. Parker. “Forecasting (aggregate) demand for US commercial air travel,” International Journal of Forecasting, vol. 27, no. 3, pp. 923-941, 2011.

[25] T. Sailauov and Z.W. Zhong. “An optimization approach towards air traffic forecasting: A case study of air traffic in Changi airport,” Statistics, Optimization and Information Computing, vol. 7, pp. 40-54, 2019.

[26] E. T. Njoya and A. Nikitas. “The role of air transport in employment creation and inclusive growth in the Global South: The case of South Africa,” Journal of Transport Geography, vol. 85, 102738, 2020.

[27] D. Baker, R. Merkert, and Md. Kamruzzaman. “Regional aviation and economic growth: cointegration and causality analysis in Australia,” Journal of Transport Geography, vol. 43, pp. 140-150, 2015.

[28] J. Chi and J. Baek. “Dynamic relationship between air transport demand and economic growth in the United States: A new look,” Transport Policy, vol. 29, pp. 257-260, 2013.

[29] R.A. Scarpel and L.C. Pelicioni. “A data analytics approach for anticipating congested days at the São Paulo International Airport,” Journal of Air Transport Management, vol. 72, pp.1-10, 2018.

[30] B. Ye, B. Liu, Y. Tian, and L. Wan. “A methodology for predicting aggregate flight departure delays in airports based on supervised learning,” Sustainability, vol. 12, 2749, 2020.

[31] G. Solvoll, T. A. Mathisen, Morten Welde. “Forecasting air traffic demand for major infrastructure changes,” Research in Transportation Economics, vol. 82, 100873, 2020.

[32] A. Nõmmik, S. Kukemelk. “Developing gravity model for airline regional route modelling,” Aviation, vol. 20, no. 1, pp. 32-37, 2016.

[33] M. Andersson, K. Brundell-Freij, J. Eliasson. “Validation of aggregate reference forecasts for passenger transport,” Transportation Research Part A, vol. 96, pp. 101-118, 2017.

[34] G. Smith. “Step away from stepwise,” Journal of Big Data, vol. 5, no. 32, pp 1-12, 2018.

[35] R.M. O’Brien, “A caution regarding rules of thumb for variance inflation factors,” Quality & Quantity, vol. 41, pp. 673–690, 2007.

[36] Y. Boquet. “Moving around the Philippines: Challenges and dynamics of inter-island transportation in a developing country,” Proceeding at the 17th International Conference of Hong Kong Society for Transportation Studies, pp. 29-36, 2012.

[37] International Civil Aviation Organization. “Manual on Air Traffic Foreasting,” 2006

[38] A. Odchimar II and S. Hanaoka, “Intermodal Road-RoRo Transport in the Philippines, its Development and Position in the Domestic Shipping,” Journal of the East Asia Society for Transportation Studies, vol. 11, pp.739-759, 2015.

[39] J.H. Stock, M.W. Watson. Introduction to Econometrics, 4th Ed, Pearson Education, 2020.

[40] P.K. Watson and S.S. Teelucksingh. A Practical Introduction to Econometric Methods: Classical and Modern, The University of the West Indies Press, 2002.

[41] G. James, D. Witten, T. Hastie, R. Tibshirani. An Introduction to Statistical Learning: with Applications in R, Springer Science & Business Media, 2013.

[42] C. McConnel, S. Brue, S. Flynn. Economics, 22nd ed, New York: McGraw-Hill Education, 2021.

[43] A. Matters. “Exchange rates and aviation: Examining the links,” IATA Economics: www.iata.org/economics, 2015.

[44] Asia Foundation. Roll-on Roll-off Transport: Connecting Maritime Southeast Asia, www.asiafoundation.org

[45] Philippine Statistics Authority. www.psa.gov.ph

[46] Civil Aviation Authority of the Philippines. www.caap.gov.ph

[47] Bangko Sentral ng Pilipinas. www.bsp.gov.ph

[48] Philippine Institute of Development Studies. www.pids.gov.ph

[49] J. Deegan, Jr. “On the Occurrence of Standardized Regression Coefficients Greater Than One,” Educational and Psychological Measurement, vol. 38, pp. 873-888, 1978.

指導教授

陳介豪(Jieh-Haur Chen)

審核日期

2021-7-14

推文