研究生: |
朱威領 Ju, Wei-Lin |
---|---|
論文名稱: |
電子票證數據輔助文字分析規劃旅程之研究 Adopting Electronic Ticket Data on Text Analysis for Journey Planning |
指導教授: |
王惠嘉
Wang, Hei-Chia |
學位類別: |
碩士 Master |
系所名稱: |
管理學院 - 工業與資訊管理學系碩士在職專班 Department of Industrial and Information Management (on the job class) |
論文出版年: | 2021 |
畢業學年度: | 109 |
語文別: | 中文 |
論文頁數: | 49 |
中文關鍵詞: | 文字探勘 、電子票證 、移動行為 、資料探勘 、旅遊推薦 |
外文關鍵詞: | Text Mining, Smart Card, Travel Behavior, Data Mining, Travel Recommendation |
相關次數: | 點閱:138 下載:20 |
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根據中華民國交通部觀光旅遊局來臺旅客統計資料,近10年來臺旅客以觀光目的比例由98年的46%提高至108年約76%,也因網際網路的普及,旅客可透過網路經驗分享進而規劃旅遊行程,不再以觀光團為主,自助旅行已成為一趨勢。為了解旅行者的需求,傳統上利用隨機抽樣、訪問調查等方式,通常須耗費大量時間與經費,隨著網際網路、社交媒體的發達,逐漸改變人們對於時、地、物體驗後的分享方式,越來越多研究開始針對網路上的文字進行分析,試圖從內容中找出文章撰寫者所推薦的景點組合,然而文字分析雖可找出推薦組合,卻較難挖掘出前往景點的先後順序,若能結合現行已存在的交易數據,如:「電子票證」的交易資料,進行分析,不僅方便,更可貼近真實情況。
電子票證目前於國內已廣泛使用於大眾運輸。由於電子票證使用數據的可記錄性,每位乘客搭乘大眾運輸工具之行為資訊,對於公共運輸系統之規劃與營運管理助益極大,其所累積之運量資料,可獲得乘客的移動路線及運載量的分配結果。除可了解旅客的喜好傾向,也可協助使用者規劃行程。
交通部觀光局來臺旅客調查資料中顯示,大部分旅客選擇行程時常利用網路上旅遊推薦,但該資料僅針對行程推薦旅客前往的景點,並未說明前往該景點後的路線規劃,考量目前研究皆以文字分析網頁資訊為多,故本研究將利用旅遊網頁所推薦之景點名稱、路線,並結合電子票證具有紀錄移動時間、空間的特性,對旅遊景點周圍人潮移動資料進行分析,以實際大眾運輸資訊來方便旅客有效的規劃旅遊路線。實證方面,本研究將以文字探勘技術,找出網頁內容所推薦的景點集合及景點間的關聯性,並搭配資料探勘技術分析捷運乘客的移動行為,找出身份為旅客的交易資料,藉此分析旅客於起迄站間移動的順序及捷運站點與點的關聯,綜合以上因素計算行程分數,推薦予以捷運作為旅程運具之旅客其搭載需求的最佳化行程。
Currently most of the research is still based on the web textual analysis, but in this thesis, we introduced the smart card transaction which contains the characteristic of traceability such as timestamp and location information, alone with the recommended routes and spots from the travel website, to analysis the traveling behavior of people around the attractive spots, plus the actual public transportation information, we can facilitate the tourists to effectively planning their tourist attractions and routes. In practice, we use the text exploration technology to discover the attractions from travel website and their relation of each other, then we use the data mining technology to analyze the movement behavior of MRT passengers in order to identify the traveler. By that we can retrieve the sequence of traveler moving between the starting and ending stations and the associations between MRT stations. Finally, the itinerary score is calculated based on the above factors, so as to recommend the optimized result that meets the needs of passengers.
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