客貨共載已經在其他國家開始實施，結合客運和貨運利用客運上剩餘的座位載送貨物或是原本的貨車載送乘客。以當地的需求進行轉換，不侷限於哪種方式比較好，而是以能改進當地的生活及降低成本為主要目標。
台灣偏鄉居民想要前往人口較密集的鄉鎮採買所花費的時間與成本都會比較高，大眾運數工具選擇較少且偏鄉的老年人口比例也很高，對於採買這件事情也非常不方便。因此配合最近正在修法的汽車運輸業管理規則第44條，讓原本的客運可以利用剩餘空位來代替貨運，進而延伸思考幫忙偏鄉居民從人口密集的鄉鎮進行採買外送服務。
因應偏鄉居民對於外送服務不太了解，大多數居民也沒有使用過。因此先以問卷調查方式詢問居民對於此服務型態的願付價格，以三界二元詢價法先給起始值再開始詢價以防止願付價格過於兩極化。蒐集完的問卷以迴歸模型及機器學習預測未來的願付價格，經過對比兩種預測分析方式結果幾乎相同。最後會參考受訪者願付價格的平均值、中位數、眾數、迴歸模型預測值以及機器學習預測值找出合適的參考價格。

Integrated passengers and freight transportation service has already been implemented in other countries. It combines passengers and freight to use the remaining seats to carry goods on the bus or to carry passengers on freight cars. It is not limited to which method is better, the main goal is to improve local life and reduce costs.
Residents in Taiwan rural areas will spend a lot of time and cost to go to populated towns to purchase. There are few choices of public transportation tools and the proportion of elderly people in remote rural areas is also high, which is very inconvenient for purchasing. Therefore, in line with amending Regulations for Automobile Transportation Operators. Buses can use the remaining vacancies to replace freights, and then we can extend this idea to help rural residents to purchase delivery services from populated towns.
Due to residents in remote areas do not know delivery service and use it. Therefore, the residents are asked the price what they are willing to pay for this type of service by questionnaire survey. The initial value is given first and used triple bound dichotomous choice to prevent the willingness to pay price are too polarized. The collected questionnaires are compared with the regression model and machine learning to predict the future willingness to pay price. The results of the two prediction analysis methods are almost the same. In the end, using mean, mode, median, regression model prediction and machine learning prediction to decide the reference price.

誌謝 III
摘要 IV
Abstract V
圖目錄 IX
表目錄 XII
第一章 緒論 1
1.1 研究背景與動機 1
1.1.1 花蓮縣外送平台比例 1
1.1.2 H客運搭公車人數 3
1.1.3 汽車運輸業管理規則第44條 4
1.2 研究目的 5
1.3 研究範圍 5
1.4 研究流程 6
第二章 文獻回顧 7
2.1 客貨共載案例 7
2.2 定價收費 10
2.2.1客運收費 10
2.2.2貨運收費 12
2.3 願付價格 14
2.4 預測分析 15
2.5 小結 17
第三章 研究方法 19
3.1 偏鄉使用客貨共載外送服務類型 19
3.2 三界二元詢價法 20
3.3 預測願付價格 21
3.4.1 迴歸模型估計 21
3.4.2 演算法預測 23
第四章 問卷設定 25
4.1 問卷設計 25
4.1.1 情境描述 25
4.1.2 情境設計 25
4.1.3 基本設定 26
4.2 問卷調查計畫 30
4.2.1調查時間 30
4.2.2調查地點 30
4.3 條件評估法 39
4.3.1情境一 39
4.3.2情境二 42
4.4 起始點設定 43
第五章 問卷分析 47
5.1 受訪者個人社會經濟資料敘述統計分析結果 47
5.1.1偏遠學校-壽豐鄉東華大學 47
5.1.2偏遠村落-萬榮鄉 52
5.1.3偏遠村落-卓溪鄉 56
5.2 迴歸模型預測願付價格 61
5.2.1線性迴歸模型 61
5.2.2 Logistic迴歸分析 66
5.3 機器學習預測願付價格 83
5.3.1預測分析 83
5.3.2評估指標 100
5.3.3結果比較 114
5.4 討論 115
第六章 結論與建議 117
6.1 結論 117
6.2 未來研究建議 119
參考文獻 121

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