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研究生:黃俊勳
研究生(外文):Chun-Hsun Huang
論文名稱:應用隸屬函數及倒傳遞網路於都市通勤旅程預估模式之研究
論文名稱(外文):Application of Membership Function and Back-Propagation Network on Urban Commute-Journey Forecasting Model
指導教授:廖祐君
指導教授(外文):Yu-Chun Liao
學位類別:碩士
校院名稱:中原大學
系所名稱:土木工程研究所
學門:工程學門
學類:土木工程學類
論文種類:學術論文
論文出版年:2001
畢業學年度:89
語文別:中文
論文頁數:127
中文關鍵詞:旅運行為邏輯斯迴歸倒傳遞網路隸屬函數旅次鏈
外文關鍵詞:travel behaviortrip chaininglogistic regressionmembership functionback-propagation network
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個人一日旅程型式之決策受到能力限制、聯結限制及權限限制之影響而決定旅程活動之排程。個人為使其旅程效益最佳化,在實際旅程中傾向以旅次鏈結方式安排旅程。本研究以旅次鏈為分析單位,並以不同分析方法建構都市通勤旅程預估模式。
以迴歸方法分析及建構模式時,往往必須滿足諸多基本假設;但都市旅運行為受到外在環境、個人家戶社經特性及家戶成員互動影響,其複雜性通常很高,實際旅運資料很難滿足迴歸分析之種種基本假設。近年來部分研究應用倒傳遞網路探討旅運行為[5][15][16],藉由隱藏層與各神經元間之相互鍵結以反映實際旅運行為,其推估結果大致皆較迴歸方法理想。本研究將倒傳遞網路結合模糊理論隸屬函數,將部分影響較顯著之語意變數透過隸屬函數之處理,進一步考慮旅運行為中所存在之模糊特性,以提昇模式推估能力。本研究分別以邏輯斯迴歸、倒傳遞網路及倒傳遞網路結合隸屬函數等方法建構都市通勤旅程預估模式,並對模式推估結果進行比較評估。
利用81年台北都會區住戶交通旅次調查之資料所建構之模式顯示,透過隸屬函數與倒傳遞網路之結合,以輸入及輸出變數模糊化模式之建構結果較其他模式好。透過隸屬函數之結合,對反映旅運行為中所存在之複雜性及模糊性有相當助益。此外,性別特性亦使旅運決策存在顯著之差異性。研究中分別建立男性與女性之行為模式,結果顯示,女性之模式推估結果優於男性及整體資料所建立之模式。
The arrangement of daily journeys and activities depends on capability constraints, coupling constraints and authority constraints faced by individuals. In order to optimize the activities out of journeys, travelers tend to chain trips along the way to or from work. In this study, we use trip chains as analysis units and establish urban commute-journey forecasting models based on various methods.
By using the regression method, numbers of assumptions need to be satisfied. However, the activity-travel behaviors are often affected by external environments, personal/household social-economics characteristics and the household-member interrelationships. The complexity makes the assumptions hard to be fulfilled. Recently, some studies apply back-propagation networks (BPNs) to simulate travel behaviors. By links of hidden layers and neurons, BPN models are capable of reflecting travel behaviors to a certain degree and generating better results than regression methods. In this study, back-propagation networks are combined with fuzzy membership functions to reflect the fuzziness in travel behaviors in order to improve the forecasting ability of models. Logistic regressions, back-propagation networks (BPN) and back-propagation networks combined membership functions (FBPN) are utilized separately to establish forecasting models on urban commute journeys.
By using the travel data collected in Taipei metropolitan area in 1992, the forecasting results generated from the combination of back-propagation networks and membership functions are better than other models. Furthermore, the input and output fuzzification models (FBPN-all) seem to perform well among FBPNs. By using the membership functions, it is helpful to reflect the complexity and fuzziness existing in travel behaviors. In addition, certain personal characteristics, especially gender, make significant differences during the decision-making process of travel. In the latter section of this study, separate models based on genders are also established for comparison. Following the model evaluation, the results indicate the models by female travelers generate much better forecasting than the counterpart.
目 錄
中文摘要……………………………………………………………………...Ⅰ
英文摘要……………………………………………………………………Ⅱ
誌謝………………………………………………………………………Ⅳ
目錄…………………………………………………………………………Ⅴ
圖目錄………………………………………………………………………Ⅶ
表目錄………………………………………………………………………Ⅷ
第一章緒論
1.1研究動機……………………………………………………………………1
1.2研究目的……………………………………………………………………2
1.3研究內容與流程……………………………………………………………3
第二章研究假設與方法
2.1研究假設……………………………………………………………………5
2.2邏輯斯迴歸理論……………………………………………………………6
2.3倒傳遞網路…………………………………………………………………8
2.4模糊集概論與隸屬函數…………………………………………………11
2.5倒傳遞網路與隸屬函數之結合…………………………………………13
2.5.1網路架構層別說明……………………………..…………………15
2.5.2網路參數推導………………………………………………………19
第三章文獻回顧
3.1活動基礎理論……………………………………………………………22
3.2類神經網路於運輸領域之應用…………………………………………26
3.3模糊理論於運輸領域之應用……………………………………………28
3.4類神經網路與模糊理論結合於運輸領域之應用………………………30
第四章通勤旅程預估模式之建構
4.1資料整理…………………………………………………………………31
4.2變數選取及定義…………………………………………………………33
4.3邏輯斯迴歸建構通勤旅程預估模式……………………………………35
4.4倒傳遞網路(BPN)建構通勤旅程預估模式……………………………36
4.5倒傳遞網路結合隸屬函數(FBPN)建構通勤旅程預估模式……………38
第五章模式建構結果
5.1邏輯斯迴歸模式之建構結果及分析……………………………………40
5.2倒傳遞網路模式(BPN)之建構結果及分析……………………………43
5.3倒傳遞網路與隸屬函數結合模式之建構結果及分析…………………47
5.3.1輸入變數模糊化模式(FBPN-in)之建構結果及分析……………47
5.3.2輸入及輸出變數模糊化模式(FBPN-all)之建構結果及分析……53
5.4都市通勤旅程之性別差異………………………………………………59
5.4.1男性通勤者之FBPN-all模式建構結果及分析…………………59
5.4.2女性通勤者之FBPN-all模式建構結果及分析…………………64
5.4.3男性及女性模糊變數隸屬函數特性分析………………………68
第六章模式建構及預估效能評估
6.1模式建構有效性之評估比較……………………………………………74
6.1.1不同模式之建構有效性……………………………………………74
6.1.2模式於離家旅程與返家旅程之建構有效性………………………77
6.1.3模式於直接旅次與間接旅次之建構有效性………………………78
6.1.4不同性別旅運行為預估模式之建構有效性………………………81
6.2模式驗證資料推估結果…………………………………………………83
6.2.1邏輯斯迴歸模式推估結果…………………………………………83
6.2.2倒傳遞網路模式(BPN)推估結果…………………………………84
6.2.3輸入及輸出變數模糊化模式(FBPN-all)推估結果………………85
6.2.4性別之輸入及輸出變數模糊化模式推估結果……………………87
6.3模式推估效能之評估比較………………………………………………89
第七章結論與建議
7.1研究結論…………………………………………………………………93
7.1.1特性比較……………………………………………………………93
7.1.2模式比較……………………………………………………………94
7.2相關研究建議……………………………………………………………96
參考文獻…………………………………………………………………………98
附錄A……………………………………………………………………………103
附錄B……………………………………………………………………………111
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