本研究探討低成本航空公司的差別定價與營收最佳化。蒐集低成本航空票價的現況資料，透過敘述性問卷設定低成本航空差別費率情境，考量出發日期與航班以及購票時點的促銷(早鳥優惠)，並且模擬票價不定時降價的促銷活動，建構旅客選擇模式。本研究採用離散選擇模式分別針對低成本航空分析旅客出發日期、航班與購票日期之選擇行為，建立不同選擇模式。最後建立營收最佳化模式，研擬航空的定價策略。
本研究針對2017年端午節連續假期5月27至30日做敘述性模擬情境，訪問搭乘低成本航空公司(台灣虎航)航班之臺灣籍旅客於桃園機場進行調查539份問卷。根據模式估計結果顯示，民眾在進行航班選擇時，票價為優先考慮的因素。最佳化模式估計結果顯示，最佳化票價會和設限的最大值相同。本研究除了探討選擇航班的因素，進而探討營收最佳化，並且以距離出發日期的天數做區隔，更符合實際的差別定價情形，將來可提供給各家航空公司之實質的參考依據。

The purpose of this study is to explore differential pricing and revenue optimization for low-cost airlines. This study observes and collects actual data on air fares and designs questionnaire surveys to understand air travelers’ preferences for differential and dynamic pricing. Discrete choice models in the form of multinomial logit are used to explain the travelers’ choice of daparture date and time. In addition, the multinomial logit model is used to examine air travelers’ booking date preferences. The revenue optimization models integrated with choice models are formulated to determine the optimal fares.
This study collecred the stated preference datafor the 2017 Dragon Boat Festival holiday. Totally 539 distributed questionnaires were collected from the Taiwanese air travelers who took the low cost carriers (Tigerair Taiwan) at the Taiwan Taoyuan International Airport. The model estimates show that the fare is an important factor affecting flight selection. Then the optimization fares are the same as the maximum of the limit. Divide from the number of days before the departure date (150/90/30 days), so that more realistic with realistic dynamic pricing. The empirical results provide important insights for setting airfares.

致謝 i
摘　　要 ii
Abstract iii
Chapter 1 Introduction 1
1.1 Research Background and Motivation 1
1.2 Research Problem and Objectives 5
1.3 Research Methods 6
1.4 Flow Chart 7
Chapter 2 Literature Review 9
2.1 Dynamic Pricing 9
2.2 Revenue Management 11
2.3 The Purchase Behavior 13
2.4 Choice Models 15
2.5 Optimization Models 20
Chapter 3 Research Methods 22
3.1 Stated Preference Design 22
3.1.1 Flight Departure Choice Behavior 24
3.1.2 Purchase Behavior 29
3.1.3 Method of Investigation 33
3.2 Choice Modeling for Flight Choice 34
3.3 Choice Modeling for Booking Date 35
3.4 Revenue Optimization 36
3.4.1 The Objective Function 37
3.4.2 The Constraint Function 38
Chapter 4 Data 39
4.1 Survey and Sampling Design 39
4.2 Data Collection and Analysis 40
4.3 Crosstab Analysis 48
Chapter 5 Estimation Results 49
5.1 Model Estimation for Flight Departure Choice 49
5.2 Model Estimation for Booking Date Choice 54
5.3 Revenue Optimization 58
Chapter 6 Conclusion 66
6.1 Study Result 66
6.2 Contributions 68
6.3 Limitations and Directions for Future Studies 69
References 70
Appendix 78
Questionnaire 118

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