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系統識別號 U0026-2408202003511900
論文名稱(中文) 費率政策變更對共享單車用量與行為變更之時空分析
論文名稱(英文) Defining the Spatial Impacts of Changing the YouBike Fees: A Case Study of Taipei City
校院名稱 成功大學
系所名稱(中) 測量及空間資訊學系
系所名稱(英) Department of Geomatics
學年度 108
學期 2
出版年 109
研究生(中文) 吳承諺
研究生(英文) Cheng-Yen Wu
學號 P66074078
學位類別 碩士
語文別 英文
論文頁數 64頁
口試委員 口試委員-溫在弘
口試委員-許聿廷
指導教授-郭佩棻
中文關鍵字 共享單車  價位變化  時空聚類  地理加權迴歸 
英文關鍵字 bike-sharing systems  Fee Policy  Spatial-temporal Cluster  Geographically Weighted Regression 
學科別分類
中文摘要 在1960年代,歐洲啟動了第一個共享單車系統”白色單車”。隨後,各個國家開始推廣自己的共享單車系統。隨著共享單車的普及化,人們對於共享單車的需求量也越來越大。共享單車的需求常常受到天氣和社會經濟因素的影響,除此之外,費用政策的轉變同時也是另一個會影響共享單車需求量的因素。在2018年4月,台北市政府推行了1280月票,可免費使用半小時的共享單車。因此,本研究希望透過此優惠政策,分析費用政策的轉變對共享單車使用模式的影響。
現存大多數的研究著重分析當前共享單車系統的營運狀況,很少有研究探討費用政策的變化會如何影響共享單車使用者的行為。本研究利用了空間分析工具來分析費用改變後的需求變化。時空聚類用於分析費用政策改變前後共享單車的起點-終點移動模式。並以每天每小時為單位,分析共享單車使用者的習慣變化,例如通勤時間的通勤旅行是否發生改變。在模型建置方面,與傳統的迴模型相比,地理加權迴歸模型具有更好的模型配適度,因為它可以捕獲空間異質性。
為了分析費用政策改變所帶來的影響,本研究蒐集了政策改變前後一年間的台北市403個共享單車站間的起點-終點旅次資料進行時空聚類分析。而在模型建置中,將使用資料縮小至政策變化前後的四個月。
此外,研究成果顯示:(1)透過時空聚類發現,費用改變後,通勤高峰時間的共享單車使用量有急遽的提升,故此政策的改變將會影響通勤族對共享單車的使用習慣。(2)透過地理加權迴歸模型發現,費用政策轉變對共享單車使用量的影響會隨著車站位置不同發生改變,特別是在大學附近的使用增加量尤為明顯。本研究分析共享單車使用者的行為模式,並量化了政策改變對共享單車帶來的影響。這些貢獻將有助於未來的決策者和共享單車系統。
英文摘要 In the 1960s, the first bike-sharing system, “White Bike,” was launched in Europe. Since then, many bike-sharing systems were promoted throughout the world and generated numerous trips. The trips of the bike-sharing system can be easily affected by weather and social-economic conditions. Aside from weather and social-economic conditions, the shift of fee policy is another key issue that would affect the demand of bike-sharing systems. In April 2018, the Taipei city government promoted a monthly ticket that provides the “first half hour is free” policy in Taipei’s bike-sharing system. Therefore, this study tried to analyze the impact of the change in the usage of bike-sharings through this preferential policy.
Most existing studies have focused on the current operation of bike-sharing systems, relatively few have studied how policy shifts might affect user’s behavior. This study utilized several spatial analysis tools to estimate the change in demand before and after the fee shifted. The spatial-temporal clustering was used to capture the OD moving pattern before and after the fee policy. The OD pattern could be shown hourly to investigate the change of habit of bike-sharing riders such as commuting trips in commuting time. Compared to the traditional regression model, Geographically Weighted Regression (GWR) based models had a better model performance because it could capture spatial heterogeneity.
In order to analyze the impact of the change in the fee policy, this study collected the O-D data of 403 bike-sharing stations in Taipei City during a year before and after the policy has changed for spatial-temporal cluster analysis. In the model building, the data used was reduced to four months before and after the policy has changed.
In addition, the results showed that: (1) Through spatial-temporal clustering, it was found that the usage of bike-sharings during commuting peak hours had increased rapidly after the fee had changed. Therefore, this policy change would affect the commuters' habits of using bike-sharings. (2) Through the geographically weighted regression model, it was found that the impact of fee policy changes on the usage of bike-sharings was different at different locations of the bike-sharing stations, especially the increase in usage near colleges. This study analyzed the habit of bike-sharing riders and quantified the impact of policy changes on bike-sharings. These contributions would benefit future decision-makers and system operators.
論文目次 摘要 ii
ABSTRACT iii
ACKNOWLEDGEMENT v
CONTENTS vii
LIST OF TABLES ix
LIST OF FIGURES x
INTRODUCTION 1
1.1 Background 1
1.2 Study Goals 2
LITERATURE REVIEW 4
2.1 Predictors and Factors of Bike-sharing Demands 4
2.2 YouBike system in Taipei, Taiwan 5
2.3 Policy Issue 6
2.4 Evaluation Methods for the Impacts of Fee Changes 7
2.5 Clustering of spatial movements with time varied flow data 8
METHODOLOGY 12
3.1 Study Data 12
3.1.1 Study Area and Bike-sharing data 12
3.1.2 Weather and Social Economic Data 13
3.2 Workflow 15
3.2.1 Ordinary Kriging 17
3.2.2 Ordinary Least Squares Method 19
3.2.3 Geographically Weighted Regression 19
3.2.4 Geographically and Temporally Weighted Regression 22
3.2.5 Visualization of YouBike trip patterns by spatial-temporal clustering method 26
RESULTS 30
4.1 Visualization of YouBike trip moving patterns before and after the fee policy 30
4.1.1 Spatial-Temporal Clustering of YouBike trips before the policy has changed 32
4.1.2 Spatial-Temporal Clustering of YouBike trips after the policy has changed 32
4.1.3 Spatial-Temporal Clustering of the difference of YouBike trips after the policy 35
4.2 Comparison of the GTWR, GWR and OLS Models 37
4.3 Spatial Distribution of Coefficients in the increase of YouBike demands 41
4.3.1 Spatial Weekdays bike-sharing demand prediction 43
4.3.2 Spatial Weekends bike-sharing demand prediction 48
DISCUSSIONS AND CONCLUSIONS 53
5.1 Discussions 53
5.1.1 The increase of bike-sharing demand in the commuting peak time 53
5.1.2 Model comparison between global OLS model and local GWR/GTWR model 53
5.1.3 The local effects of different variables 54
5.2 Conclusions 59
5.3 Study Limitations and Future Work 60
REFERENCES 62


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