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系統識別號 U0026-2608201602050000
論文名稱(中文) 應用地理加權迴歸探討台南都會區小客車與機車使用比例空間分布
論文名稱(英文) Modeling the Spatial Distribution of Modal Split of Passenger Cars and Powered Two-Wheelers in Tainan Metropolitan Areas Using Geographically Weighted Regression
校院名稱 成功大學
系所名稱(中) 都市計劃學系
系所名稱(英) Department of Urban Planning
學年度 104
學期 2
出版年 105
研究生(中文) 李柏昱
研究生(英文) Po-Yu Lee
學號 p26034258
學位類別 碩士
語文別 英文
論文頁數 67頁
口試委員 指導教授-李子璋
口試委員-張學聖
口試委員-葉如萍
口試委員-林楨家
中文關鍵字 二輪機動運具  可及性  地理加權迴歸  建成環境 
英文關鍵字 Powered Two-Wheelers  accessibility  geographically weighted regression  built environment 
學科別分類
中文摘要 近幾十年來包含摩托車在內的二輪機動運具(powered two-wheelers, PTW)以及家用小客車的數量,無論在開發中國家或是已開發國家都呈現成長態勢。此類運具在都會區廣泛的被使用,造成各國都市地區嚴重的環境汙染與交通問題,因此汽機車研究近年來成為都市計劃與運輸規劃界重視的課題之一。在此背景脈絡下,本研究主旨有二:一是針對都會區居民選擇汽機車作為其使用運具的影響因子進行更為全面的探討,二是藉由地理加權迴歸(Geographically Weighted Regression, GWR)技術的運用,揭示每個解釋變數對於兩種運具的影響程度隨空間變化的情形。本研究的研究變數主要可分為三大部分,包含可及性、都市建成環境,以及社會經濟條件變數。本研究選擇台南都會區作為實際操作範圍,共包含412個村里與15個行政區。為了檢驗GWR模型擁有更佳的配適度與解釋力,本研究運用最小平方法(Ordinary least squares, OLS)建立全域型迴歸模型,以進行具顯著性的變數篩選,OLS模型的結果並與GWR模型進行解釋力的比對。
研究結果顯示,在台南都會區各村里中,機車相對於汽車皆具有壓倒性的使用比例。除此之外,GWR模型的結果無論在汽車或是機車的使用比例模型中,GWR模型結果皆比OLS模型更佳。整體而言,可及性、土地混合使用程度以及平均收入對於影響運具使用比例擁有最大的影響程度。GWR模型的結果則顯示,對汽車而言,可及性對於汽車使用比例具有負向影響,大街廓的都市路網型態則會提高汽車的使用比例。而在機車的GWR模型中,達統計上顯著性的解釋變數與機車使用比例之間的關係,在空間上皆呈現從正相關到負相關皆有的現象。此結果顯示相對於汽車而言,機車在使用上更容易受到地區性特徵影響。本研究並針對台南市中心、市郊環狀地區、七股區以及台南科學園區一帶進行汽機車影響因素上的討論。本研究成果能夠為都市規劃者和決策者提供都會區內部不同地區,影響汽機車使用比例的因子更為詳實的資訊。
英文摘要 The number of powered two-wheelers (PTW) and passenger cars is still increasing in many developed and developing countries. Due to the huge amount of PTWs and cars in modern cities, they have become one of the most important issues in city planning and transportation. This study is aimed toward gaining a more comprehensive understanding of why people choose motor vehicles as their daily travel mode and illustrates the use of the Geographically Weighted Regression (GWR) technique to estimate the strength of the relationships between accessibility, urban form characteristics, and social economic characteristics for each administrative neighborhood in Tainan Metropolitan Areas in Taiwan. The study area includes 412 administrative neighborhoods and fifteen districts. Ordinary least squares (OLS) and GWR were conducted to examine the effect of potential covariates and to compare their outputs. The results show that PTWs dominate the road traffic in the Tainan metropolitan areas. On the other hand, results of the GWR models in both transportation modes were found to be better than in the OLS method. In general, the results indicated accessibility, entropy of land use, and average income have significant influences on vehicle usage. Accessibility was found to have a negative relationship with the usage of cars, while intersection distance and average income were found to be proportionate to the usage rate of cars. Entropy parameters, which reflect the degree of land use mix, varied across the study areas. The coefficients of all of the variables satisfied statistical significance tests in the usage of the PTW model, the results for which also varied from place to place. These results indicate that compared to cars, the use of PTWs is much more local and more easily influenced by local geographic characteristics. The findings may provide informative insights for planners and policy makers to shape vehicle use in urban areas and surrounding exurbs.
論文目次 1. Introduction 1
1.1. Background 1
1.2. Significance of this issue 2
1.3. Research Objectives 2
1.4. Research Framework 3
2. Literature Review 5
2.1. Household Vehicle Ownership and Usage 5
2.2. Travel Behavior Influenced by Built Environments 10
2.3. Spatial Variation Models and Modal Share 13
3. Methodology 17
3.1. Research Framework 17
3.1.1. Define Study Area 18
3.1.2. Data Processing and Variable Selection 18
3.1.3. Empirical Analysis Using OLS and GWR 18
3.2. Study Area 19
3.3. Data 21
3.3.1. Data Source 21
3.3.2. Road Networks Building 21
3.3.3. Variable Selection 25
3.4. Spatial Modeling 32
3.4.1. Ordinary least squares 32
3.4.2. Geographically weighted regression 32
3.5. Expected Relationship Between Vehicle Usage and Variables 36
4. Results 39
4.1. Descriptive Statistics 39
4.2. Correlation Analysis 41
4.3. Global Relationship 44
4.3.1. Results of the OLS Models 44
4.3.2. VIF Analysis 47
4.3.3. Moran’s I of Residuals 48
4.4. Geographically Weighted Regression 50
4.4.1. Car Usage 53
4.4.2. PTW Usage 57
5. Discussion and Conclusions 60
5.1. Transport Modes 60
5.2. Methods 61
5.3. Factors Influencing Vehicle Usage 61
5.3.1. The Downtown Area 61
5.3.2. Suburban Areas 62
5.3.3. The Qigu District 63
5.3.4. The Downtown Area 63
5.4. Future Works 64
Reference List 65
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