都市運輸與土地使用的關係是長久以來討論的重要議題，一些研究顯示高密度與混合的土地使用可以減少開車意願，配合友善步行與自行車的設計，可增加步行和大眾運輸的使用。然而，當中一個重要的問題是，不同建成環境如何去影響整個都市運輸系統的運量。本研究試圖以過去研究使用的5D建成環境因素在整個都會區分析大眾運輸的運量。該5D因素分別為土地使用密度、多樣性、友善行人與自行車的設計、目的地可及性和轉乘便利性。
本研究以臺北捷運系統作為實證對象，調查共108個場站與其周遭地區，使用來自不同政府機關的資料庫以及開放街圖的POI資料。根據不同範圍進行模型建置，發現以0-600公尺的範圍抓取資料將得出最佳配適度的模型，同時也發現以季運量來做同心圓的分群，配適度更高，解決以往建置模型時樣本數不足的問題。相較於OLS的結果，本研究使用多重尺度地理加權迴歸（Multi-scale geographically weighted regression）來處理變數不同尺度帶寬的問題，以解釋空間異質性的現象。
研究結果顯示，中和、松山、大安與信義一帶的十字路口密度與公車路線數對當地捷運站運量較有顯著正向影響，反而其他變數則較無顯著影響或負向影響；淡水、北投一帶以土地使用多樣性、公共設施與公車路線數呈現顯著正向影響；內湖、新莊與板橋一帶多以人口密度、公共設施呈現顯著正向影響。值得一提的是，中和的人口密度呈現正向顯著影響，推測可能跟中和的高人口密度有關，但卻因為稀少的大型公共設施，中和的行人目的地可及性呈現負向顯著影響。就實務面來說，本研究能在解釋運量影響因素上建立參考的基礎，並應用於大眾運輸導向發展規劃當中。

Over the past few decades of research on relationship between built environment and urban transport, a number of issues have appeared that density development and mixed land use would reduce the choice of private vehicle and encourage the transit system. One key concern for urban planners is how the built envirnment influences on urban transit system at the different neighborhood area. Therefore, our purpose attempts to analyse the different factors of transit ridership at an empirical area by five-D concepts including density, divesity, design, destination accessibility, and distance to transit. This study took Taipei Metro as an example to investigate the factors of built environment on ridership in 108 metro stations. The data consisted of different database of government and Open Street Map from December, 2017 to November, 2018. We compiled this data around station by 600 meters as the best goodness-of-fit model, and conducted seasonal ridership as dependent variables due to the small sample problem. Furthermore, compared with this OLS model, we also developed the multi-scale geographically weighted regression (GWR) in different area to fix the spatial scale problem of coefficient. Our findings indicate the seasonal ridership and repeated independent variables of OLS model in more local area, to some extent, deal with the worse coefficient of determination. To conclude, this study may be of importance in explaining the factors of built environment on transit ridership, and furthor applied for Transit-oriented development (TOD) strategy.

一、中文期刊（按筆劃排序）
林楨家、施亭伃（2007）。大眾運輸導向發展之建成環境對捷運運量之影響-臺北捷運系統之實證研究。運輸計劃季刊，36（4），451-476。doi：10.6402/TPJ.200712.0451
林楨家、楊恩捷（2006）。都市型態對旅運需求影響之結構化分析。運輸學刊，18（4），391-416。doi：10.6383/JCIT.200612.0391
林楨家, & 蕭博正（2006）。台北市土地混合使用特性對旅次發生之影響。臺灣土地研究，9(1)，89-114。doi：10.6677/JTLR.200605_9(1).0004
白仁德、劉人華（2014）。大眾運輸導向建成環境特性對捷運運量影響之研究—以臺北捷運為實證對象。建築與規劃學報，15（2/3），111-128。
doi：10.30054/JAP
紀雲曜、李上妤、葉光毅（2004）。公共設施最適服務／影響距離之評估模式。建築與規劃學報，5(2)，150-177。doi：10.30054/JAP.200412.0003

二、中文書籍、技術報告書與學位論文（按筆劃排序）
王周偉、崔百勝、張元慶（2017）。空間計量經濟學：現代模型與方法。北京大學出版社。
林楨家、李嘉儂、馮正民、羅健文、蔡耀慶、陳志豪、李欣庭（2011）。都市計畫案綠色運輸衡量指標之研訂。交通部運輸研究所。
施鴻志（1997）。都市規劃。建都文化。
凌瑞賢（2015）。運輸規劃與實務（第三版）。鼎漢國際工程顧問股份有限公司。

三、英文期刊（按字母排序）
An, D., Tong, X., Liu, K., & Chan, E. H. (2019). Understanding the impact of built environment on metro ridership using open source in Shanghai. Cities, 93, 177-187.
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Bhat, C. R., & Guo, J. Y. (2007). A comprehensive analysis of built environment characteristics on household residential choice and auto ownership levels. Transportation Research Part B: Methodological, 41(5), 506-526.
https://doi.org/10.1016/j.trb.2005.12.005
Cardozo, O. D., García-Palomares, J. C., & Gutiérrez, J. (2012). Application of geographically weighted regression to the direct forecasting of transit ridership at station-level. Applied Geography, 34, 548-558.
https://doi.org/10.1016/j.apgeog.2012.01.005
Cervero, R. (2006). Alternative approaches to modeling the travel-demand impacts of smart growth. Journal of the American Planning Association, 72(3), 285-295.
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Cervero, R., Sarmiento, O. L., Jacoby, E., Gomez, L. F., & Neiman, A. (2009). Influences of built environments on walking and cycling: lessons from Bogotá. International journal of sustainable transportation, 3(4), 203-226.
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Fotheringham, A. S., Yang, W., & Kang, W. (2017). Multiscale geographically weighted regression (MGWR). Annals of the American Association of Geographers, 107(6), 1247-1265. https://doi.org/10.1080/24694452.2017.1352480
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四、英文書籍、技術報告書與學位論文（按字母排序）
Berke, P., Godschalk, D. R., Rodríguez, D. A., & Kaiser, E. J. (2006). Urban Land Use Planning. University of Illinois Press.
Calthorpe, P. (1990). Transit-Oriented Development Design Guidelines. Calthorpe Association, 5.
Calthorpe, P. (1993). The next American metropolis: Ecology, community, and the American dream. Princeton architectural press.
Duckham, M., Goodchild, M. F., & Worboys, M. F. (2003). Foundations of Geographic Information Science. CRC Press.
Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2003). Geographically weighted regression: the analysis of spatially varying relationships. John Wiley & Sons.
Kutner, M. H., Nachtsheim, C. J., & Neter, J. (2003). Applied Linear Regression Models. McGraw-Hill Higher Education.
Maier, G. (2015). Forecasting ridership impacts of transit oriented development at MARTA rail stations. Georgia Institute of Technology.
Nakaya, T., Charlton, M., Lewis, P., Brunsdon, C., Yao, J., & Fotheringham, S. (2017). GWR4 User Manual. 2014: GWR.
Wooldridge, J. M. (2013). Introductory Econometrics: A Modern Approach. South-Western Cengage Learning.

五、網路資源與其他出版品（依筆劃和字母排序）
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