Please use this identifier to cite or link to this item: https://ah.lib.nccu.edu.tw/handle/140.119/131764
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dc.contributor.advisor范噶色<br>林子欽zh_TW
dc.contributor.advisorvan Gasselt, Stephan<br>Lin, Tzu-Chinen_US
dc.contributor.author蔡孟秦zh_TW
dc.contributor.authorTsai, Meng-Chinen_US
dc.creator蔡孟秦zh_TW
dc.creatorTsai, Meng-Chinen_US
dc.date2020en_US
dc.date.accessioned2020-09-02T04:41:25Z-
dc.date.available2020-09-02T04:41:25Z-
dc.date.issued2020-09-02T04:41:25Z-
dc.identifierG0107257033en_US
dc.identifier.urihttp://nccur.lib.nccu.edu.tw/handle/140.119/131764-
dc.description碩士zh_TW
dc.description國立政治大學zh_TW
dc.description地政學系zh_TW
dc.description107257033zh_TW
dc.description.abstract停車在都市規劃和政策決定過程中扮演著重要的角色,而一個較不完善的停車系統容易導致交通擁堵、空氣污染和其他問題,特別是在人口稠密的城市地區。過去數十年來學者也不斷討論停車行為會受各種因素影響。其中,調整停車費是最常見的實施措施,因此台北市政府於2015年推動全面收取路邊停車費的新政策,目的即希望能改變使用者停車行為,增加路邊停車周轉率,解決住宅區內車位被長期占用的問題。在本研究中,主要引用臺北市停車工程管理處公布的年度調查報告進行分析討論,其中涵蓋不同交通分區(TAZ)中的路面及路外停車需求、供給和違規停車數量。在分析停車模式時,透過應用空間自相關獲取停車費價格和停車比率的空間異質性。由於原本實施全市停車政策具其局限性,需要透過本地觀點進一步討論,利用地理加權回歸(GWR)可補充討論各停車分區間潛在停車因素與停車行為之間的關係。另外,優化加權函數的帶寬及考量混和參數的方式可改進估計模型,並解釋不同因子間的差異性。本論文主要採用了考慮主要變量、條件和特徵的方法,以便最終根據可解釋的單位調整停車政策,改善台北市的停車情況。最終,這種系統性分析也可望在其他城市實施,以從空間視角輔助政策審查分析。zh_TW
dc.description.abstractParking is extraordinary to be discussed during urban planning and policy-making, since a worse parking system might lead to traffic congestion, air pollutions and other problems, particularly in densely populated urban areas. It has been debated for decades that parking behavior is affected by various factors. Among those, adjustment on parking fee is the most common measure to implement, which was also taken as a new policy to generally charge on-road parking in Taipei in 2015. In this study, the main analyzing dataset was acquainted by a published annual governmental survey, covering numbers on the demand, supply, and illegal parking in different traffic zone (TAZ). Through the application of spatial autocorrelation techniques, it is possible to identify the similarity, and capture the spatial heterogeneity of the patterns of price and parking ratio in overall situation. Due to the limitation of responding a city-wide parking policy, it requires a further discussion on local perspectives, which can be examined through a Geographically Weighted Regression (GWR). This thesis examines the relationship between potential factors, showing the influence level of different variables. Additionally, other advanced technique which target at optimizing weighting kernels and consider different scales of parameters are also applied to improve the estimated models. With these calculated coefficients, the major objective of this thesis, an approach taking into account dominating variables, conditions and characteristics is implemented in order to ultimately adjust the parking-policy based on interpretable units and to improve the parking situation in Taipei City. Eventually, this structural analysis is also expectable to be implemented to other cities to assist the policy review in a spatial perspective.en_US
dc.description.tableofcontentsAbstract VI\nContent VII\nFigures IX\nTables XIII\n\nChapter 1. Introduction 1\n1.1 General Background and Motivation 1\n1.2 Purpose of Research 9\n1.3 Research Question 10\n(1) Policy Impact 10\n(2) Policy Assessment 11\n(3) Policy Adjustment 11\n1.4 Research Structure and Conceptual Framework 12\n\nChapter 2 Background 15\n2.1 Parking Demands and Parking Behavior 15\n(1) Influential Factors to Parking Choice 15\n(2) Modeling Approaches to Parking Choices 16\n2.2 Parking Pricing 19\n2.3 Spatial Analysis 22\n(1) Global Spatial Autocorrelation 22\n(2) Local Spatial Autocorrelation 23\n2.4 Geographically Weighted Regression (GWR) 26\n\nChapter 3 Research Methodology 29\n3.1 Data Description 31\n3.2 Initial Data Processing 35\n(1) Curb-parking Spaces Data 35\n(2) Parking Fee Data 35\n(3) Attributes of TAZ 36\n3.3 Spatial Analysis on Policy Impact and Situation 39\n3.4 Multiple Linear Regression 41\n(1) Assumptions Test of Model Estimation 41\n(2) Model Estimation 42\n(3) Statistical Significance of Test Regression Models 43\n(4) Heteroscedasticity test on models 44\n3.5 Geographic Weighted Regression 46\n(1) Model Estimation 46\n(2) Selecting an Appropriate Kernel 47\n(3) Selecting the Best Bandwidth 49\n(4) Validation of Regression Result and Advanced Topics 51\n(5) Optimize the Selected Bandwidth in Multi-scale 56\n(6) Software 57\n\nChapter 4 Results and Discussions 59\n4.1 Spatial Analysis of Parking Situation 59\n4.2 Empirical Analysis of Estimated Model 65\n(1) Situation before the Policy Implementation 68\n(2) Situation after the Policy Implementation 75\n(3) Heteroscedasticity Test on MLR Model Residuals 83\n4.3 Geographically Weighted Regression in Local Regions 86\n(1) Basic Geographically Weighted Regression 86\n(2) Validation Test and Extension of GWR 98\n(3) Model Optimization in Multi-Scale Perspective 106\n4.4 Summary of Analysis 116\n(1) Comparisons of Different Models 116\n(2) Discussions of Different Situations 118\n\nChapter 5 Conclusion and Future Prospects 121\n5.1 Conclusion 121\n5.2 Recommendations 123\n\nReferences 125zh_TW
dc.format.extent6146918 bytes-
dc.format.mimetypeapplication/pdf-
dc.source.urihttp://thesis.lib.nccu.edu.tw/record/#G0107257033en_US
dc.subject停車zh_TW
dc.subject政策影響zh_TW
dc.subject空間分析zh_TW
dc.subject地理加權迴歸(GWR)zh_TW
dc.subjectParkingen_US
dc.subjectPolicy Impacten_US
dc.subjectSpatial Analysisen_US
dc.subjectGeographically Weighted Regression(GWR)en_US
dc.title臺北市路邊停車研究: 以空間分析探討停車收費政策影響zh_TW
dc.titleOn-road Parking in Taipei City: Spatial Analysis of Parking Policy Impacten_US
dc.typethesisen_US
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dc.identifier.doi10.6814/NCCU202001224en_US
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item.openairecristypehttp://purl.org/coar/resource_type/c_46ec-
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