交通行動服務(Mobility as a Service, MaaS)整合一系列的交通工具、票務系統、旅行行程與轉乘方案以提供的使用者所需服務，使用者僅需操作單一平台即可完成所有旅行規劃及交通費的計算與繳費。MaaS已經在日常通勤(通學)的研究中被廣泛討論，而在觀光上的研究還不及日常通勤(通學)的研究。觀光客具有使用經驗缺乏、使用時間集中、對資訊不熟悉之特性，而MaaS可以為旅遊提供有關大眾運輸或是共享運具的旅遊資訊，甚至安排行程，進而提高旅遊體驗。本研究使用敘述性偏好法針對旅遊至高雄市的民眾面訪調查以及網路調查，以491份有效問卷進行羅吉特模型以及潛在類別模型估計，了解觀光客對於交通行動服務(MaaS)選擇之偏好，更進一步觀察不同分群對於屬性偏好之差異。
根據混合羅吉特估計結果，整體受訪者對於方案屬性皆具有正向偏好，僅在大眾運輸表現為不顯著，從此可了解到在方案已包含捷運的情況下，觀光客對於額外納入輕軌及公車的意願並不顯著。而在觀察受訪者異質性的標準差上，大眾運輸及共享汽車標準差顯著，意即受訪者對於輕軌、公車及共享汽車納入方案的看法有所差異。在潛在類別模型的分析中，MaaS旅遊者對於交通以及旅遊服務屬性為正向偏好，且在成本表現上為正向；MaaS懷疑者僅對共享自行車、特約景點服務持正向偏好，傾向以較低的價格購買；MaaS旅運者則是傾向將MaaS僅作為交通套票使用且偏好機動性強的共享電動機車與共享汽車，同樣傾向以較低的價格購買。
綜合以上分析結果，未來應推出整合交通與旅遊服務之MaaS，也應針對不同族群的需求推出不同方案，例如針對單純旅運需求之民眾，推出整合共享電動機車與共享汽車之MaaS，以回應不同需求。

Mobility-as-a-Service provide service to users by a single digital platform that integrates travel information, booking and payment, and service offers. Users only use the platform to complete all travel planning and calculation and payment of transportation fees. In past, The main research of MaaS is to discuss commuters with long-term subscriptions, and seldom discuss tourists with short-term usage. In addition, tourists are not familiar with the information of attractions, and currently there is no single platform for tourists to browse all the travel information. They could benefit more from the integrated multimodal travel information provided by MaaS in the tourism context. With this information, tourists can choose the most satisfactory travel modes for their journeys. A stated choice survey of 491 individuals administered via a face-to-face method is conducted in Kaohsiung City and online. Mixed logit model and latent class choice model are estimated to reveal difference between mode choice and groups.
The mixed logit model indicate that respondents widely prefer all service while heterogenous preferences is found in public transportation and shared car. Next, three latent classes emerged through the analysis of latent class choice model, all with different MaaS package preferences and individual characteristics. Income, education and employment situation play an important role in determining an individual’s propensity to purchase MaaS packages. The results can provide valuable insights into the types of people to make MaaS package in the future.

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