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系統識別號 U0026-2208201914253700
論文名稱(中文) 運輸營運行為與乘客特性大數據分析:以國道客運H公司為例
論文名稱(英文) Big Data Analysis on Transportation Operating Patterns and Passenger Behaviors-An Example of Intercity Bus Company “H”
校院名稱 成功大學
系所名稱(中) 電信管理研究所
系所名稱(英) Institute of Telecommunications and Management
學年度 107
學期 2
出版年 108
研究生(中文) 劉耿誌
研究生(英文) Geng-Jhih Liou
學號 R96061104
學位類別 碩士
語文別 中文
論文頁數 101頁
口試委員 指導教授-黃國平
口試委員-李威勳
口試委員-王逸琳
中文關鍵字 國道客運  大數據分析  類神經網路  滿意度 
英文關鍵字 Intercity Bus  Big Data Analysis  Artificial neural network  Satisfaction 
學科別分類
中文摘要 國道客運在台灣的交通發展中有著非常久的歷史,其便宜的票價使得現今在台灣南北往返的運輸方式中仍然是主要的交通工具之一。雖然探討國道客運相關的文獻從古至今已有相當大的數量,然而近年來因為科技發展快速而興起的大數據分析卻在國道客運領域中並沒有太多的研究。因此大數據研究在國道客運方面的研究便是一大課題。
本研究收集並整理了H公司一年份的售票資料進行分析,藉此尋找出各時段與各路線的乘客總量並檢定其差異性與各預售票訂票平台在各時段的使用狀況,再透過類神經網路進行乘客量的預測建模,並探討各輸入變數像是「月份」、「星期幾」、「上下午」、「連假與否」、「連假第幾日」、「路線方向」等變數對於整體乘客量的變化會的影響程度大小以及影響整體神經網路精準度的最關鍵變數,最後再透過問卷探討面對H公司在北高路線需轉乘的限制上,乘客對於H公司北高路線服務的滿意度與最關鍵選擇因素為何。
根據本研究結果顯示,乘客量的變化具有一周七日的規律性,然而遇到連假時會打破這樣的規律,而在各預售票訂購平台的使用狀況也確實會受到搭乘時間的不同而有不同的使用率。統計檢定結果則是有些路線在一星期中特定的日子會有著與其他日子不同的乘客變化量,另外在一星期中大部分的日子中上下午都會有不同的乘客變化量。在類神經網路的部分,不管有無「月份」變數的影響,「星期幾」都是影響整體乘客變化量最大的變數與神經網路精準度的變數。另外在問卷的部分也忠實的呈現出北高路線乘客對於H公司的服務滿意度與服務使用經驗。透過這樣的研究可讓業者在擬定營運策略時作為參考。
英文摘要 Intercity bus industry is a historical and important transportation in Taiwan, because of its cheaper price, it is still a considerable transportation. There are a lot of researches discussed about the intercity bus in Taiwan, but there are only a few researches discussed about emerging rising technology with intercity bus, like the application of ticketing data. As a result, Big Data Analysis of intercity bus is an important issue right now.
By this research, we collected the ticketing data of intercity bus company H for one year quantity, to find out how input value, like, “Month, Weekday, Morning or afternoon, Holiday or not, Day’s number of holiday, direction of route,” affect the number of passengers, differential analysis of the passengers’ amount-changed between every weekday, then modeling it by ANN, and find out the usage rate of each pre-sale ticketing platform. After that, we can realize the performance of company of the intercity bus. By this research, the change of number of passengers from some weekdays have the difference between others. In ANN part, weekday is the most important input for whole model. In pre-sale ticketing platform part, the usage rate is affected by the time section. At last, we designed the questionnaire for the passengers of Route Taipei-Kaohsiung to find out the reason why they still take the service of this route provide from company ‘H’ even though they could not arrive to the destination directly.
By this research, we found that the number of passengers has a regularly weekly change. But this pattern would not exist during the holiday. Then, statical result shows that the change of number of passengers from some weekdays have the difference between others, also the number of passengers from morning and afternoon are having difference, too. In ANN part, whether we took “month” as an input or not, weekday is still the most important input for whole model. In pre-sale ticketing platform part, the usage rate is affected by the time section. Finally, the questionnaire part shows the reason why the passengers of Route Taipei-Kaohsiung take the service provide from company “H”.
論文目次 摘要 I
目錄 VII
表目錄 IX
圖目錄 XI
一、 緒論 1
1.1 研究動機與背景 1
1.2 研究目的 4
1.3 研究流程 4
二、 文獻回顧 8
2.1 交通領域大數據分析方法 8
2.2 大數據分析在交通領域上的研究與應用成果 11
2.3 大數據在公路客運的應用研究 13
2.4 客運業者路線、班次服務與乘客特性 15
2.5 類神經網路在交通領域上的文獻回顧 18
三、 研究方法 22
3.1 資料取得 22
3.2 資料說明與類神經網路分析使用工具 23
3.3 類神經網路架構設計與統計檢定 27
3.4 問卷設計 31
四、 資料分析與因素重要性分析 34
4.1 路線運量資料整理 34
4.2 運量統計結果與分析 36
4.3 運量影響因素統計檢定與分析 46
4.4 類神經網路模型學習暨測試、驗證與結果 51
4.5 變數重要性敏感度測試 61
4.6 乘客搭乘因素與滿意度分析 63
4.7 類神經網路分析及滿意度分析應用意涵 69
五、 結論與建議 72
5.1 結論 72
5.2 後續研究建議 74
參考文獻 76
附件一:乘客搭乘因素與滿意度問卷 81
附件二:加入月份作為輸入變數後的類神經網路訓練結果 83
附件三:Deep Learning訓練結果 86
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