進階搜尋


   電子論文尚未授權公開,紙本請查館藏目錄
(※如查詢不到或館藏狀況顯示「閉架不公開」,表示該本論文不在書庫,無法取用。)
系統識別號 U0026-2308201602590700
論文名稱(中文) 探討混合使用發展對於混合使用之影響-以台南市為例
論文名稱(英文) Does mixed-use development reduce travel distances?-empirical evidence from Tainan City, Taiwan
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) 李亭儀
研究生(英文) Ting-Yi Lee
學號 P26034232
學位類別 碩士
語文別 英文
論文頁數 71頁
口試委員 口試委員-張學聖
口試委員-林漢良
口試委員-黃家耀
指導教授-李子璋
中文關鍵字 混合使用發展  旅次目的  平均熵值  運輸規劃 
英文關鍵字 Mixed-use development  Trip purpose  Mean entropy  Transportation planning 
學科別分類
中文摘要 混合使用發展是近幾年規劃領域中一個具有一定重要性的規劃策略,其被認為可以幫助人們達到更永續的生活方式,目前有許多單位希望透過這樣的發展模式降低旅次或通勤的距離,然而,混合使用發展與旅次距離之間的關係卻較少被過往的文獻探討。
台南市共有752個交通分區,為本研究之實證地區,以探討混合使用發展之程度與平均旅次距離之間的關係,資料來源為民國100年的台南市家戶旅次調查及民國95年的國土利用調查,而在仔細討論了對混合使用發展程度之定義、土地使用類別之不同、都市活動系統及旅次目的等關鍵概念之後,得到各交通分區的13個旅次類別之平均距離及60個不同鄰域範圍大小的平均熵值(Mean entropy),並應用敘述統計、雙變量相關分析、空間自相關分析及雙變量空間自相關分析等分析方法探討。
綜和各分析之結果,可了解不同旅次類別之平均旅次距離對於混合使用發展程度有不一樣的關係。整題而言,混合使用發展程度與平均旅次距離有顯著負相關,其中家購物用餐旅次大部分可在約1,050公尺鄰域範圍內完成旅次,而大部分家學校(15歲以下)旅次可在約2,050公尺鄰域範圍內完成旅次;然而,家工作、家社交娛樂及家其他旅次之平均距離與混合使用發展程度則沒有顯著之關係。
此結果可幫助實務規劃者及政府單位之決策依據,並可以更準確的預估旅運行為之變化,除此之外,將建成環境與旅運行為之間關係依據不同旅次目的探討可做為過去相關文獻困境之解決方法。研究架構、混合使用發展程度之指標及實證分析結果皆可做為未來運輸規劃及後續相關研究之參考。
英文摘要 Mixed-use development has been regarded as important principle of planning and has also been recognized as a desirable zoning pattern to achieve a sustainable life style. Also, it is believed that mixed-use development can reduce trip distances. However, very few studies have been conducted to inspect the relationship between them.
Tainan City, Taiwan was selected as the study area to investigate the relationship between mixed-use levels and average trip distances, where a total of 752 traffic analysis zones were involved. Mean Entropy was employed to measure the level of mixed-use by calculating the average entropy of neighbourhoods in each traffic analysis zone, in which the different average level of mixed-use development, the categories of land uses, the trip purpose categories, and the systems of urban activities were carefully inspected and discussed. Data for this study were gathered from the Tainan Metropolitan Household Travel Survey in 2011 and the National Land Use Investigation of Taiwan in 2006. The travel survey data was grouped into 13 different travel types, and the land use data was also classified into a system composed of 5 major urban activities.
The results indicate that there are different relationship patterns between the levels of mixed-use development and travel behaviour with different travel types. Generally speaking, mixed level correlates significantly negatively with the average distances travelled. Home-based shopping and school (aged under 15) and dining travellers are willing to travel further for better choices up to 2,050 and 1,050 m, respectively. However, home-based work, home-based leisure and social, and home-based other travel were not found to not significantly correlate with mixed level development, which can help practitioners and governments making decisions and predict changes in travel behaviour more accurately. In addition, analysing the relationship between travel behaviour and built environments according to trip purposes may be the solution for the existing dilemma discussed in previous studies. The methodology framework, measurements for mixed level development, and empirical experience in this paper can be referred to in the future.
論文目次 1. INTRODUCTION 1
1.1 Motivation 1
1.2 Purposes 2
1.3 Research Structure 3
2. LITERATURE REVIEW 4
2.1 Travel Behaviour Factors 4
2.2 Differences between Travels 6
2.2.1 Trip purposes and activities 6
2.2.2 Classification of travels 7
2.3 Concept of Mixed-Use Development 10
2.3.1 Background 10
2.3.2 Related policy 10
2.4 Measurements of Level of Mixed-Use Development 11
2.4.1 Entropy 11
2.4.2 Dissimilarity 13
2.4.3 Mean Entropy 14
3. METHODOLOGY 16
3.1 Research Framework 16
3.2 Study Area 16
3.3 Trip Distances 18
3.3.1 Data collection 18
3.3.2 Travel types 19
3.3.3 Origin–destination (O-D) matrix 21
3.4 Level of Mixed-Use Development: Mean Entropy 22
3.4.1 Data collection 22
3.4.2 Activities and land uses 22
3.4.3 Modifiable areal unit problem (MAUP) 25
3.5 Spatial Statistics 27
3.5.1 Spatial Autocorrelation 28
3.5.2 Bivariate Spatial Autocorrelation Analysis 29
4. RESULTS AND DISCUSSION 31
4.1 Average Trip Distances of Different Travel Types 31
4.2 Level of Mixed-Use Development 33
4.2.1 Descriptive statistics 33
4.2.2 Spatial statistics 35
4.3 Bivariate Correlation Analysis 38
4.4 Bivariate Spatial Autocorrelation Analysis 44
5. CONCLUSIONS AND SUGGESTIONS 47
5.1 Research Findings 47
5.2 Research Implications 48
5.3 Research Limitations 50
5.4 Future Study 51
REFERENCES 52
APPENDIX 55
I. Classification Land Use Types and Activities 55
II. Correlations Coefficients of Bivariate Correlation Analysis 61
III. Correlation Coefficients of Bivariate Spatial Autocorrelation Analysis 68
參考文獻 BOTTCG, B. O. T., TAINAN CITY GOVERNMENT 2012. Midterm Report of Comprehensive Transportation System Planning in Tainan City (in Chinese). In: BUREAU OF TRANSPORTATION, T. C. G. (ed.). Bureau of Transportation, Tainan City Government.
BRAIL, R. K. & CHAPIN, F. S. 1973. Activity patterns of urban residents. Environment and Behavior, 5, 163.
CASTIGLIONE, J., BRADLEY, M. & GLIEBE, J. 2014. Activity-based travel demand models: a primer.
CERVERO, R. 1989. Land-use mixing and suburban mobility. University of California Transportation Center, 42, 429-446.
CERVERO, R. 1996. Mixed land-uses and commuting: evidence from the American Housing Survey. Transportation Research Part A: Policy and Practice, 30, 361-377.
CERVERO, R. & KOCKELMAN, K. 1997. Travel demand and the 3Ds: density, diversity, and design. Transportation Research Part D: Transport and Environment, 2, 199-219.
CHAPIN, F. S. & BRAIL, R. K. 1969. Human activity systems in the metropolitan United States. Environment and Behavior, 1, 107.
CHAPIN JR, F. S. 1968. Activity systems and urban structure: A working schema. Journal of the American Institute of Planners, 34, 11-18.
CNU. 2001. Charter of the New Urbanism [Online]. The Congress of the New Urbanism. Available: http://cnu.org/who-we-are/charter-new-urbanism [Accessed 11/20 2015].
DOMENCICH, T. A. & MCFADDEN, D. 1975. Urban Travel Demand-A Behavioral Analysis.
GETIS, A. & ORD, J. K. 1992. The analysis of spatial association by use of distance statistics. Geographical analysis, 24, 189-206.
JACOBS, J. 1961. The death and life of great American cities, New York, Random House.
KIM, H.-M. & KWAN, M.-P. 2003. Space-time accessibility measures: A geocomputational algorithm with a focus on the feasible opportunity set and possible activity duration. Journal of Geographical Systems, 5, 71-91.
KOCKELMAN, K. M. 1997. Travel behavior as function of accessibility, land use mixing, and land use balance: evidence from San Francisco Bay Area. Transportation Research Record: Journal of the Transportation Research Board, 1607, 116-125.
KRIZEK, K. J. 2003. Neighborhood services, trip purpose, and tour-based travel. Transportation, 30, 387-410.
LAWRENCE A. BROWN, J. H., AND JOHN F. JAKUBS 1970. URBAN ACTIVITY SYSTEMS IN A PLANNING CONTEXT.
LEE, S.-I. 2001. Developing a bivariate spatial association measure: An integration of Pearson's r and Moran's I. Journal of Geographical Systems, 3, 369-385.
LU, X. & PAS, E. I. 1999. Socio-demographics, activity participation and travel behavior. Transportation Research Part A: Policy and Practice, 33, 1-18.
MANAUGH, K. & KREIDER, T. 2013. What is mixed use? Presenting an interaction method for measuring land use mix. Journal of Transport and Land Use, 6, 63-72.
MORAN, P. A. 1950. Notes on continuous stochastic phenomena. Biometrika, 37, 17-23.
MORLOK, E. K. 1978. Introduction to transportation engineering and planning, McGraw-Hill New York.
NOWROUZIAN, R. & SRINIVASAN, S. 2013. Modeling the Effect of Land Use on Person Miles Traveled by Using Geographically Weighted Regression. Transportation Research Record: Journal of the Transportation Research Board, 2397, 108-116.
REICHMAN, S. 1976. Travel adjustments and life styles: a behavioral approach. Behavioral travel-demand models, 143-152.
SCHWANEN, T., DIELEMAN, F. M. & DIJST, M. 2001. Travel behaviour in Dutch monocentric and policentric urban systems. Journal of Transport Geography, 9, 173-186.
SCHWANEN, T., DIELEMAN, F. M. & DIJST, M. 2004. The impact of metropolitan structure on commute behavior in the Netherlands: a multilevel approach. Growth and Change, 35, 304-333.
SHANNON, C. E. 2001. A mathematical theory of communication. ACM SIGMOBILE Mobile Computing and Communications Review, 5, 3-55.
SONG, Y. & KNAAP, G.-J. 2004. Measuring the effects of mixed land uses on housing values. Regional Science and Urban Economics, 34, 663-680.
STEAD, D. & MARSHALL, S. 2001. The relationships between urban form and travel patterns. An international review and evaluation. European Journal of Transport and Infrastructure Research, 1, 113-141.
TOBLER, W. R. 1970. A computer movie simulating urban growth in the Detroit region. Economic geography, 46, 234-240.
USEPA, U. S. E. P. A. 2016. Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2014. In: AGENCY, U. S. E. P. (ed.). Washington, DC: U.S. Environmental Protection Agency.
VAN ACKER, V., WITLOX, F. & VAN WEE, B. 2007. The effects of the land use system on travel behavior: a structural equation modeling approach. Transportation planning and technology, 30, 331-353.
WEISSTEIN, E. W. Moore Neighborhood. [Online]. Available: http://mathworld.wolfram.com/MooreNeighborhood.html [Accessed 2015/9/13.
WELCH, A., BENFIELD, K., RAIMI, M. & COUNCIL, U. G. B. 2010. A Citizen's Guide to LEED for Neighborhood Development: How to Tell If Development is Smart and Green, US Green Building Council.
WONG, D. W., WONG, J. D. W. & LEE, J. 2005. Statistical analysis of geographic information with ArcView GIS and ArcGIS.
論文全文使用權限
  • 同意授權校內瀏覽/列印電子全文服務,於2021-08-15起公開。
  • 同意授權校外瀏覽/列印電子全文服務,於2021-08-15起公開。


  • 如您有疑問,請聯絡圖書館
    聯絡電話:(06)2757575#65773
    聯絡E-mail:etds@email.ncku.edu.tw