台灣高鐵於2007年通車至今，前後推出許多不同的票種與優惠方案，但座位利用率仍無法有效提升，為了提升高鐵整體運量，本研究以營收管理之市場區隔概念，為價格敏感度較低的休閒旅次旅客提出高鐵新產品：高鐵低價商品。由於傳統運具選擇模式僅考量方案屬性變數與受訪者之社會經濟特性，並未考慮受訪者不可直接測得之心理因素，因此使用結合潛在變數模式與個體選擇模式的潛在變數選擇模式(Hybrid Discrete Choice Model, HDCM)，探討哪些因素會影響受訪者的運具選擇行為，並透過效用函數計算出各運具之選擇機率並進行彈性分析及敏感度分析。
本研究之潛在變數模式以行為推理理論(Behavioral Reasoning Theory, BRT)的構面作為潛在變數，分析受訪者對高鐵低價商品的認同程度，相較於計畫行為理論，行為推理理論增加了支持與反對行為的構面，能夠更具體的說明受訪者選擇或不選擇高鐵低價商品的原因。運具選擇部分則是將高鐵低價商品、客運、台鐵與自行開車的屬性變數水準值組合成不同情境供受訪者選擇。
研究結果在潛在變數模式部分顯示，行為推理理論的支持構面會顯著影響意圖；潛在變數選擇模式之混合羅吉特模式部分，除了基本的旅行時間、票價與成本，買不到票的機率、退票手續費也會影響選擇，模式整體配適度為0.42，72.77%受訪者選擇高鐵低價商品；在彈性部分，高鐵低價商品的直接價格彈性大於旅行時間與買不到票的機率之直接彈性，表示價格降低較能有效吸引受訪者。本研究結果可提供台灣高鐵作為營運與行銷之參考。

This study aims to enhance load factor of Taiwan High Speed Rail (THSR). We design a new low-fare product of THSR for non-business journey travelers to see whether the respondents will agree with the concepts and willingness to pay foy the product.
In order to measure the personality traits, we utilize the model which combines discrete choice model and latent variable model, entitled hybrid discrete choice model. Latent variables of this study are chosen from behavioral reasoning theory (BRT) which proposes specific reasons are important for decision making processes. In this study, three types of trip length are discussed. Logit models of discrete choice model estimates utility of the four travel modes, which are low-fare HSR product, Tze-Chiang Limited Express of Taiwan Rail, freeway bus, and cars. Then we can compute the choice probability, market share of different situations, and elasticity of each alternatives.
The sample size is 644. Results of structural equation modeling indicate that reason for behavior has positive effects on the behavioral intention of using low-fare product of THSR. Logit models show that not only price and travel time will influence travelers’ choices, no-booking probability and percentage of refund fee are also important factors during decision making. The choice probability of low-fare high speed rail products is 72.77%. The low-fare HSR product is proven effective to enhance load factor of THSR.

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