| DC 欄位 |
值 |
語言 |
| DC.contributor | 產業經濟研究所在職專班 | zh_TW |
| DC.creator | 詹曜鴻 | zh_TW |
| DC.creator | Yao-Hung Chan | en_US |
| dc.date.accessioned | 2016-7-27T07:39:07Z | |
| dc.date.available | 2016-7-27T07:39:07Z | |
| dc.date.issued | 2016 | |
| dc.identifier.uri | http://ir.lib.ncu.edu.tw:88/thesis/view_etd.asp?URN=103454010 | |
| dc.contributor.department | 產業經濟研究所在職專班 | zh_TW |
| DC.description | 國立中央大學 | zh_TW |
| DC.description | National Central University | en_US |
| dc.description.abstract | 大阪線及東京線是國人非常熱門的旅遊航線之一，而低成本航空公司的加入勢必對現有的全服務航空公司造成影響，對航空公司而言，如果能預測未來市場的走向，提早佈局及規劃，也許能在競爭激烈的市場搶得先機並降低風險。
本研究以2005年1月至2014年12月從臺灣飛往日本大阪及東京航線之全部(包括全服務航空及低成本航空)載客人數及全服務航空載客人數月資料作為分析資料，建構其ARIMA相乘季節性模式，再以2015年1月至12月大阪線及東京線的實際月運量資料作為預測觀察值，檢驗模式預測之精確度，亦探討研究期間外部事件是否會影響載客人數變動。
實證結果：(一) 大阪線全部載客人數之模式SARIMA(2,1,3)(0,1,1,12)、大阪線全服務航空載客人數之模式SARIMA(3,1,3)(0,1,1,12)、東京線全部載客人數之模式SARIMA(2,1,1)(0,1,1,12)、東京線全服務航空載客人數之模式SARIMA(2,1,3)(0,1,1,12)，以上述模式預測之MAPE皆小於10%，顯示預測能力具有高精確度。(二) 2005年至2015年落後一期的油價波動對大阪線全服務航空載客人數有顯著負向影響，另東京線當期油價波動對全服務航空載客人數有顯著正向影響。(三) 2005年至2015年日圓匯率對大阪線及東京線旅運量無顯著影響，惟若只考慮低成本航空加入期間，落後一期的日圓匯率波動對大阪線全部航空載客人數有顯著負向影響。(四) 2005年至2015年低成本航空飛行架次對大阪線及東京線全服務航空載客人數有顯著負向影響，惟若只考慮低成本航空加入期間，則低成本航空的班次對東京線旅運量無顯著影響。 | zh_TW |
| dc.description.abstract | Osaka and Tokyo (Japan) are two of the Taiwanese favorite travels routes. It will impact on the full-service carriers while the low-cost airlines enter the markets. If airline companies can forecast the market trend and make plans ahead, it might be able to seize the initiative and reduce the operation risk in a highly competitive markets.
The study uses Seasonal ARIMA Model to forecast the monthly total airline (including full-service carriers and low-cost airlines) and full-service carriers passenger’s numbers of Taiwan to Osaka and Tokyo (Japan) routes from Jan of 2005 to Dec of 2014, and then uses the observations to forecast the monthly total airline and full-service carriers passenger numbers of Osaka routes and Tokyo routes from Jan to Dec of 2015, and examines the forecasting creditability of models, either analyze whether or not some interventions impact on the passenger numbers.
The results show that: (1) the SARIMA(2,1,3)(0,1,1,12) model with total airline passenger numbers of Osaka routes、the SARIMA(3,1,3)(0,1,1,12) model with full- service carriers passenger numbers of Osaka routes、the SARIMA(2,1,1)(0,1,1,12) model with total airline passenger numbers of Tokyo routes、the SARIMA(2,1,3)(0,1,1,12) model with full-service carriers passenger numbers of Tokyo routes, and all of the MAPE of predictive values by these models are less than 10%, showing that forecasting creditability of models with high accuracy; (2) the lag-one-period oil price is significantly negative relating to the full-service carriers passenger numbers of Osaka routes from 2005 to 2015, and the current oil price is positively and significantly relating to the full-service carriers passenger numbers of Tokyo routes from 2005 to 2015; (3) the Japanese Yen of exchange rate is no significantly relating to the airline passenger numbers of Osaka routes and Tokyo routes from 2005 to 2015, but the lag-one-period of Japanese Yen is significantly negative relating to the total airline passenger numbers of Osaka routes after that the low-cost airlines enter the markets; (4) the number of flights on low-cost airlines is significantly negative relating to the full-service carriers passenger numbers of Osaka routes and Tokyo routes from 2005 to 2015, but the number of flights on low-cost airlines is not significantly relating to the airline passenger numbers of Tokyo routes after the low-cost airlines enter the markets. | en_US |
| DC.subject | 低成本航空 | zh_TW |
| DC.subject | ARIMA 相乘季節性模式 | zh_TW |
| DC.subject | 介入模式 | zh_TW |
| DC.subject | Low-Cost Airlines | en_US |
| DC.subject | Seasonal ARIMA Model | en_US |
| DC.subject | Interventions Model | en_US |
| DC.title | 航空公司運量預測模式之研究─東京線及大阪線 | zh_TW |
| dc.language.iso | zh-TW | zh-TW |
| DC.type | 博碩士論文 | zh_TW |
| DC.type | thesis | en_US |
| DC.publisher | National Central University | en_US |