Loading...
|
Please use this identifier to cite or link to this item:
https://nccur.lib.nccu.edu.tw/handle/140.119/141225
|
| Title: | 臺北市公共自行車使用特性與空間分佈型態之趨勢變化分析
Analysis on the Trend Change of the Use Characteristics and Spatial Distribution Patterns of Public Bicycles in Taipei City |
| Authors: | 鍾凱如
Chung, Kai-Ju |
| Contributors: | 白仁德
甯方璽
Pai, Jen-Te
Ning, Fang-Shii
鍾凱如
Chung, Kai-Ju |
| Keywords: | 公共自行車系統
大數據
空間自相關分析
地理加權迴歸
多尺度地理加權迴歸
Public bike system
Big data
Spatial autocorrelation analysis
Geographically Weighted Regression
Multiscale Geographically Weighted Regression |
| Date: | 2022 |
| Issue Date: | 2022-08-01 18:22:27 (UTC+8) |
| Abstract: | 近年來遭受能源消耗與氣候變遷導致之環境衝擊，各國城市積極推動擁有低污染、低耗能、可及性高又兼具運動休閒特性等優勢之公共自行車系統的發展，而我國最為完善且較具規模者為臺北市「YouBike微笑單車」。許多系統營運至今已日漸成熟，掌握時空變化下使用者之租借需求，有效管理供需平衡以提高整體租借量，應為系統長期經營之關鍵。然而，現有關於公共自行車之研究大多透過短期橫斷面數據或以抽樣、問卷調查形式評估其使用特性及影響因素。是故，本研究目的在於運用臺北市YouBike系統自2016年1月至2021年5月之站點資訊與租借資料，瞭解長時間下使用者之租借情況與分佈型態，並考量縱向時間與空間差異概念，建構公共自行車使用需求趨勢模型，評估不同因素對使用量之關係與影響程度，從而提供系統營運規劃與策略建議。
本研究首先爬梳文獻且歸納公共自行車有關議題及使用之影響因素，其次以統計分析、空間自相關分析，初步觀察使用量的時間變動趨勢及空間分佈型態變化。最後，根據歷年使用量、平日及假日使用變化量之縱貫性與橫斷面資料型態，分別以追蹤資料模型、全域型與地域型迴歸建構使用需求趨勢模型。
實證結果顯示，歷年租借時間趨勢相似且大部分為短程使用，而熱門租借站點與騎乘路線大多鄰近捷運站和學校周邊。另外，公共自行車使用量之顯著影響因素為人口數、從業員工人數、站點數量、假日與降雨天數，而使用變化量之顯著影響因素則為人口數、與捷運站點距離及站點數量。尤其從地域型迴歸模型結果得知，不同變數考量空間特徵下之影響程度和顯著性檢定皆有所差異，且應用多尺度地理加權迴歸可更有效地解釋空間變異下不同因素對於公共自行車使用之影響，據此提出系統站點佈局、容量調整、車輛調度以及自行車道路網規劃等相關策略建議，以期提升整體公共自行車使用量。
In recent years, due to the environmental impact caused by energy consumption and climate change, various cities have actively promoted the development of public bike systems with the advantages of low pollution, low energy consumption, high accessibility and both sports and leisure characteristics. "YouBike" in Taipei City is the most developed and large-scale one. As many systems have become more mature in operation, it should be the key to the long-term operation of the system to grasp the rental needs of users under the change of time and space, and effectively manage the balance between supply and demand to improve the overall rental. However, most of the existing studies on public bicycles evaluate their usage characteristics and influencing factors through short-term and cross-sectional data or in the form of sampling and questionnaire surveys. Therefore, the purpose of this research is to use the site and rental data of the Taipei YouBike system from January 2016 to May 2021 to understand the rental situation and distribution patterns of users over a long period of time. Furthermore, the study considered the concept of longitudinal time and space difference to construct a demand trend model for public bicycle use, and to evaluate the relationship and influence of different factors on the use of bicycles, so as to provide system operation planning and strategic suggestions.
The study firstly reviewed the literature and summarized the issues of public bicycles and the influencing factors of the use, then used statistical analysis and spatial autocorrelation analysis to preliminarily observe the temporal trends and spatial distribution patterns of bicycle usage. Finally, according to the longitudinal and cross-sectional data of usage over the years, usage changes on weekdays and holidays, the panel data model, global and regional regressions were used to construct usage demand trend models.
The results showed that the trend of rental time over the years is similar and most of them are used for short distances, while popular sites and riding routes are mostly near MRT stations and schools. In addition, the significant factors affecting the use of public bicycles are the population, the number of employees, the number of stations, the number of holidays and rainy days. The significant factors affecting the usage changes are the population, the distance from the MRT station, and the number of stations. Especially from the results of the regional regression model, it is known that the degree of influence and significance test of different variables considering spatial characteristics are different, and the application of multiscale geographically weighted regression can more effectively explain the effect of different factors on the use of public bicycles under spatial variation. Based on this, the relevant strategic suggestions on system site layout, capacity adjustment, vehicle scheduling, and bikeway planning are proposed, in order to increase the overall use of public bicycles. |
| Reference: | 一、中文參考文獻
（一）期刊論文
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。
行政院經濟建設委員會經濟研究處，2008，「永續能源政策綱領－節能減碳行動方案」，『台灣經濟論衡』，6(10)：8-17。
江穎慧、莊喻婷、張金鶚，2017，「臺北市公共自行車場站對鄰近住宅價格之影響」，『運輸計劃季刊』，46(4)：399-428。
李沃牆、李惠娜、劉雅鳳，2013，「公股銀行放款集中度對經營績效及逾放之影響分析」，朝陽商管評論，12(2)，93-112。
李恆綺、楊大輝、楊明德、巫妮蓉，2016，「公共腳踏車使用者特性及偏好分析 -以高雄市 C-Bike 為例」，『運輸計劃季刊』，45(4)：331-356。
呂千慈、鍾易詩、馮正民，2018，「環境空間與站點數對公共自行車租借量之影響分析」，『運輸學刊』，30(2)：113-137。
李俊毅、王聖鐸，2019，「空氣品質、天氣概況與公共自行車租借行為之關聯性分析：以臺北市為例」，『航測及遙測學刊』，24(3)：161-171。
邱皓政，2017，「多元迴歸的自變數比較與多元共線性之影響：效果量、優勢性與相對權數指標的估計與應用」，『臺大管理論叢』，27(3)： 65-108。
林宜甲、黃柏霖，2017，「利用空間性局部指標(LISA)分析高雄市C-Bike站點與空間相關性初探」，『經營管理學刊』，12&13：125-141。
張國楨，張文菘，曾露儀，2012，「都市土地利用與人口老化對基層醫療資源分布之影響：以台北市為例」，『地理研究』，56：25-40。
張凱翔、張立韋、陳亮羽、林長郁，2015，「YouBike微笑單車熱門租借點(以台北市為例)」，『地理資訊系統季刊』，9(4)：26-28。
張文菘、陳嘉惠、張國楨，2017，「應用空間統計於桃園地區土地利用變遷因素分析」，『地理研究』，67：137-161。
張舜淵、王劭暐、鍾敦沛，2020，「都會區捷運整體路網規劃作業之研究」，『都市交通半年刊』，35(1)：15-42。
廖興中、呂佩安，2013，「臺灣縣市政府貪腐現象之空間自相關分析」，『臺灣民主季刊』，10(2)：39-72。
趙家芸、王聖鐸，2019，「以開放街圖與開放資料分析公共自行車使用率—以臺北市為例」，『航測及遙測學刊』，24(4)：245-255。
蔡佳菁、劉修祥，2010，「高雄成為自行車友善城市之論述」，『城市發展半年刊』，10：34-50。
鍾智林、黃晏珊，2016，「開放式數據為基礎之公共自行車營運特性分析－以臺北YouBike為例」，『運輸學刊』，28(4)：455-478。
羅孝賢、劉力銘，2010，「接駁型公共自行車租賃系統辦理經驗與未來展望」，『都市交通』，25(1)：56-61。
（二）碩博士論文
白詩滎，2013，「臺北市公共自行車使用行為特性分析與友善環境建構之研究」，國立政治大學地政學系碩士學位論文。
林尚德，2003，「以反應空間不穩定性為基礎之土地估價模型建立」，國立成功大學都市計劃學系碩士論文。
余書枚，2009，「公共自行車租借系統選擇行為之研究」，國立交通大學交通運輸研究所碩士學位論文。
李舒媛，2018，「以悠遊卡大數據探討YouBike租賃及轉乘捷運之使用者行為」，淡江大學運輸管理學系運輸科學碩士班學位論文。
李冠頡，2020，「基於智慧票卡資料探索旅次及活動型態：土地利用與大眾運輸系統空間特性之觀點」，國立臺灣大學土木工程學系碩士論文。
李婕瑜，2021，「以個體行為導向模型模擬自行車路網對公共自行車系統使用之影響與環境效益－以臺北市為例」，國立成功大學環境工程學系碩士論文。
邱辰，2015，「從空間角度分析公共自行車系統對站點周邊土地活動影響」，國立臺灣大學工學院土木工程學系碩士論文。
林浩瑋，2016，「悠遊卡數據應用於大眾運輸乘客旅運型態之研究」，淡江大學運輸管理學系運輸科學碩士班碩士論文。
倪如霖，2016，「公共自行車租借量之影響因素分析－地理加權迴歸和函數資 料分析方法之應用」，國立交通大學運輸與物流管理學系碩士論文。
張辰尉，2017，「臺北市公共自行車站點需求分析之研究」，國立政治大學地政學系碩士學位論文。
黃雅燕，2006，「中國大陸地級城市經濟成長收斂性假說驗證 －空間 Panel 計量的應用」，世新大學財務金融學系碩士學位論文。
曾韋齊，2013，「城市公共自行車租賃系統定價研究－以臺北微笑單車為例」，淡江大學運輸管理學系運輸科學碩士班學位論文。
楊孝博，2011，「鐵路車站旅客運輸需求之實證性研究－地理加權迴歸之應用」，國立臺灣大學地理環境資源學系碩士學位論文。
蔡宛容，2013，「運用地理加權迴歸方法探討都市土地混合使用空間分佈之影響因素」，國立成功大學都市計劃學系碩士論文。
蔡宗佑，2017，「公共自行車之替代性與互補性－以臺北都會區為例」，國立交通大學 運輸與物流管理學系。
鄭雨桐，2016，「建成環境對公共自行車使用之影響」，國立臺灣大學地理環境資源學系研究所碩士學位論文。
戴威，2018，「臺北市YouBike開放大數據為基礎的公共自行車旅次與租賃站特性分析」，淡江大學運輸管理學系運輸科學碩士班學位論文。
羅惟元，2008，「以悠遊卡交易資料探索公車路線之旅客起迄」，淡江大學運輸管理學系運輸科學碩士班學位論文。
（三）其他
水敬心，2020，「YouBike 2.0於臺灣大學校總區試辦計畫營運績效評估與需求分析」，109年2月17日至109年6月30日。
李佳倩，2018，「臺北市 YouBike 使用特性報乎你知」，『統計應用分析報告』，臺北市政府交通局統計室。
張學聖，2009，「都市地區人為災害空間特性與災害風險區劃設之研究」，補助優秀新進教師學術研究計畫。
二、外文參考文獻
（一）期刊論文
Andreas Kaltenbrunner, Rodrigo Meza, Jens Grivolla, Joan Codina, Rafael Banchs, 2010, “Urban cycles and mobility patterns: Exploring and predicting trends in a bicycle-based public transport system”, Pervasive and Mobile Computing, 6: 455-466.
Alexander Rixey, 2013, “Station-Level Forecasting of Bike Sharing Ridership: Station Network Effects in Three U.S. Systems”, Transportation Research Record Journal of the Transportation Research Board, 2387(-1): 46-55.
Ahmadreza Faghih-Imani, Naveen Eluru, Ahmed M. El-Geneidy, Michael Rabbat, Usama Haq, 2014, “How land-use and urban form impact bicycle flows: evidence from the bicycle-sharing system (BIXI) in Montreal”, Journal of Transport Geography, 41: 306-314.
Ahmadreza Faghih-Imani and Naveen Eluru, 2015, “Analyzing bicycle-sharing system user destination choice preferences: Chicago’s Divvy system”, Journal of Transport Geography, 44: 53-64.
Ahmadreza Faghih-Imani and Naveen Eluru, 2016, “Incorporating the impact of spatio-temporal interactions on bicycle sharing system demand: A case study of New York CitiBike system”, Journal of Transport Geography, 54: 218-227.
Ahmadreza Faghih-Imani, Robert Hampshire, Lavanya Marla, Naveen Eluru, 2017, “An empirical analysis of bike sharing usage and rebalancing: Evidence from Barcelona and Seville”, Transportation Research Part A: Policy and Practice, 97: 177-191.
Aritra Pal and Yu Zhang, 2017, “Free-floating bike sharing: Solving real-life large-scale static rebalancing problems”, Transportation Research Part C: Emerging Technologies, 80: 92-116.
A. Stewart Fotheringham, Wenbai Yang, Wei Kang, 2017, “Multiscale geographically weighted regression (MGWR)”, Annals of the American Association of Geographers, 107(6): 1247-1265.
Andrew Bell, Malcolm Fairbrother, Kelvyn Jones, 2019, “Fixed and random effects models: making an informed choice”, Quality & Quantity, 53: 1051-1074.
Arefeh Nasri, Hannah Younes, Lei Zhang, 2020, “Analysis of the effect of multi-level urban form on bikeshare demand: Evidence from seven large metropolitan areas in the United States”, The Journal of Transport and Land Use, 13(1): 389-408.
Andreas Nikiforiadis, Georgia Ayfantopoulou, Afroditi Stamelou, 2020, “Assessing the Impact of COVID-19 on Bike-Sharing Usage: The Case of Thessaloniki, Greece”, Sustainability, 12(19) 8215: 1-12.
Abdullah Kurkcu, Ilgin Gokasar, Onur Kalan, Alperen Timurogullari, Burak Altin, 2021, “Insights into the Impact of COVID-19 on Bicycle Usage in Colorado Counties”, Transportation Research Board, Grey literature, 1-17.
Boyd Cohen, Esteve Almirall, Henry Chesbrough, 2016, “The City as a Lab: Open Innovation Meets the Collaborative Economy”, Calif. Manag. Rev, 59(1): 5-13.
Cheng Hsiao, 2007, “Panel data analysis—advantages and challenges”, Test, 16: 1-22.
Cheng Lyu, Xinhua Wu, Yang Liu, Xun Yang, Zhiyuan Liu, 2020, “Exploring multi-scale spatial relationship between built environment and public bicycle ridership: A case study in Nanjing”, The Journal of Transport and Land Use, 13(1): 447-467.
Doug Laney, 2001, “3D Data Management: Controlling Data Volume, Velocity, and Variety”, Application Delivery Strategies, File 949.
DeMaio, Paul, 2003, “Smart bikes: Public transportation for the 21st century”, Transportation Quarterly, 57(1): 9-11.
David Duran-Rodas, Emmanouil Chaniotakis, Constantinos Antoniou, 2019, “Built Environment Factors Affecting Bike Sharing Ridership: Data-Driven Approach for Multiple Cities”, Transportation Research Record, 00(0), 1-14.
Ezgi Eren and Volkan Emre Uz, 2020, “A review on bike-sharing: The factors affecting bike-sharing demand”, Sustainable Cities and Society, 54: 1-12.
Francis X. Diebold, 2003, “Big Data dynamic factor models for macroeconomic measurement and forecasting”. Advances in Economics and Econometrics: Theory and Applications, Eighth World Congress of the Econometric Society, 115-122.
Fleura Bardhi and Giana M. Eckhardt, 2012, “Access-Based Consumption: The Case of Car Sharing”, Journal of Consumer Research, 39(4): 881-898.
Getis, A. and J. K. Ord, 1992, “The Analysis of Spatial Association by Use of Distance Statistics”, Geographical Analysis, 24: 189-207.
Giuseppe Arbia and Gianfranco Piras, 2007, “Convergence in per-capita GDP across EU-NUTS2 regions using panel data models extended to spatial autocorrelation effects”, European Regional Science Association, 1-33.
Hua Zhang, Susan A. Shaheen, Xingpeng Chen, 2014, “Bicycle Evolution in China: From the 1900s to the Present”, International Journal of Sustainable Transportation, 8(5): 317-335.
Haojie Li, Yingheng Zhang , Manman Zhu , Gang Ren, 2021, “Impacts of COVID-19 on the usage of public bicycle share in London”, Transportation Research Part A, 150: 140-155.
John Pucher and Ralph Buehler, 2006, “Why Canadians Cycle More Than Americans: A Comparative Analysis of Bicycling Trends and Policies”, Transport Policy, 13(3): 265-279.
Julie Bachand-Marleau, Brian H. Y. Lee, Ahmed M. El-Geneidy, 2012, “Better understanding of factors influencing likelihood of using shared bicycle systems and frequency of use”, Transportation Research Record: Journal of the Transportation Research Board, 2314: 66-71.
Jonathan Corcoran, Tiebei Li, David Rohde, Elin Charles-Edwards, Derlie Mateo-Babiano, 2014, “Spatio-temporal patterns of a Public Bicycle Sharing Program: the effect of weather and calendar events”, Journal of Transport Geography, 41: 292-305.
Jinbao Zhao, Wei Deng, Yan Song, 2014, “Ridership and effectiveness of bikesharing: The effects of urban features and system characteristics on daily use and turnover rate of public bikes in China”, Transport Policy, 35: 253–264.
Jihye Jeon, 2015, “The Strengths and Limitations of the Statistical Modeling of Complex Social Phenomenon: Focusing on SEM, Path Analysis, or Multiple Regression Models”, World Academy of Science, Engineering and Technology International Journal of Economics and Management Engineering, 9(5): 1634-1642.
Joseph Chibwe, Shahram Heydari, Ahmadreza Faghih Imani, Aneta Scurtu, 2021, “An exploratory analysis of the trend in the demand for the London bike-sharing system: From London Olympics to Covid-19 pandemic”, Sustainable Cities and Society, 69: 1-7.
Kate Ann Levin, 2006, “Study design III: Cross-sectional studies”, Evidence-Based Dentistry, 7, 24-25.
Kyle Gebhart and Robert B. Noland, 2014, “The impact of weather conditions on bikeshare trips in Washington, DC”, Transportation, 41:1205–1225.
Luc Anselin, 1995, “Local indicators of spatial association—LISA”, Geographical Analysis, 27(2): 93-115.
Le ́andre Tarpin-Pitre and Catherine Morency, 2020, “Typology of Bikeshare Users Combining Bikeshare and Transit”, Transportation Research Record: Journal of the Transportation Research Board, 2674: 475-483.
Maria Bordagaray, Angel Ibeas, Luigi dell’Olio, 2012, “Modeling User Perception of Public Bicycle Services”, Procedia-Social and Behavioral Sciences, 54: 1308-1316.
Maninder Singh Setia, 2016, “Methodology Series Module 3: Cross-sectional Studies”, Indian Journal of Dermatology, 61(3): 261-264.
Michael Ahillen, Derlie Mateo-Babiano, Jonathan Corcoran, 2016, “The Dynamics of Bike-Sharing in Washington, D.C. and Brisbane, Australia: Implications for Policy and Planning”, International Journal of Sustainable Transportation, 10: 441-454.
Merkebe Getachew Demissie, Santi Phithakkitnukoon, Titipat Sukhvibul, Francisco Antunes, Rui Gomes, and Carlos Bento, 2016, “Inferring Passenger Travel Demand to Improve Urban Mobility in Developing Countries Using Cell Phone Data: A Case Study of Senegal”, IEEE Transactions on Intelligent Transportation Systems, 17(9), 2466-2478.
Maddalena Favaretto, Eva De Clercq, Christophe Olivier Schneble, Bernice Simone Elger, 2020, “What is your definition of Big Data? Researchers’ understanding of the phenomenon of the decade”, PLoS ONE, 15(2): 1-20.
Oliver O’Brien, James Cheshire, Michael Batty, 2014, “Mining bicycle sharing data for generating insights into sustainable transport systems”, Journal of Transport Geography, 34: 262-273.
Oshan, T. M., Li, Z., Kang, W., Wolf, L. J., & Fotheringham, A. S., 2019, “MGWR: A Python implementation of multiscale geographically weighted regression for investigating process spatial heterogeneity and scale.”, ISPRS International Journal of Geo-Information, 8(6), 269.
Pucher, J., Komanoff, C. and Schimek, P., 1999, “Bicycling renaissance in North America? Recent trends and alternative policies to promote bicycling”, Transportation Research A, 33(7/8), 625-654.
Pu Wang, Timothy Hunter, Alexandre M. Bayen, Katja Schechtner, Marta C. González, 2012, “Understanding Road Usage Patterns in Urban Areas”, Scientific Reports, 2: 1-6.
Roger Beecham and Jo Wood, 2014, “Exploring gendered cycling behaviours within a large-scale behavioural data-set”, Transportation Planning and Technology, 37(1), 83-97.
Robert B. Noland, Michael J. Smart, Ziye Guo, 2016, “Bikeshare trip generation in New York City”, Transportation Research Part A, 94: 164-181.
Susan A. Shaheen, Stacey Guzman, and Hua Zhang, 2010, “Bikesharing in Europe, the Americas, and Asia”, Transportation Research Record: Journal of the Transportation Research Board, 2143: 159-167.
Santi Phithakkitnukoon, Teerayut Horanont, Giusy Di Lorenzo, Ryosuke Shibasaki, Carlo Ratti, 2010, “Activity-Aware Map: Identifying Human Daily Activity Pattern Using Mobile Phone Data”, Human Behavior Understanding, 6219(3), 14-25.
Susan A. Shaheen and Stacey Guzman, 2011, “Worldwide Bikesharing”, Access Magazine, Number 39 Fall 2011, 22-27.
Shlomo Bekhor, Yehoshua Cohen, Charles Solomon, 2013, “Evaluating Long-Distance Travel Patterns in Israel by Tracking Cellular Phone Positions”, Journal of Advanced Transportation, 47: 435-446.
Susan Jia Xu and Joseph Y. J. Chow, 2020, “A longitudinal study of bike infrastructure impact on bikesharing system performance in New York City”, International Journal of Sustainable Transportation, 14: 886-902.
Shouheng Sun, Myriam Ertz, 2021, “Contribution of bike-sharing to urban resource conservation: The case of free-floating bike-sharing”, Journal of Cleaner Production, 280, 124416: 1-12.
Songhua Hu, Chenfeng Xiong, Zhanqin Liu, Lei Zhang, 2021, “Examining spatiotemporal changing patterns of bike-sharing usage during COVID-19 pandemic”, Journal of Transport Geography, 91: 1-16.
Tien Dung Tran, Nicolas Ovtrachta, Bruno Faivre d’Arcier, 2015, “Modeling Bike Sharing System using Built Environment Factors”, Procedia CIRP, 30: 293-298.
Ting Ma, Chao Liu, Sevgi Erdogan, 2015, “Bicycle Sharing and Public Transit-Does Capital Bikeshare affect Metrorail ridership in Washington, D.C.?”, Transportation Research Record: Journal of the Transportation Research Board, 2534: 1-9.
Taru Jain, Xinyi Wang, Geoffrey Rose, Marilyn Johnson, 2018, “Does the role of a bicycle share system in a city change over time? A T longitudinal analysis of casual users and long-term subscribers”, Journal of Transport Geography, 71: 45-57.
Vivian Yi-Ju Chen, Wen-Shuenn Deng, Tse-Chuan Yang, Stephen A. Matthews, 2012, “Geographically Weighted Quantile Regression (GWQR): An Application to U.S. Mortality Data”, Geographical Analysis, 44(2): 134-150.
Wafic El-Assi, Mohamed Salah Mahmoud, Khandker Nurul Habib, 2017, “Effects of built environment and weather on bike sharing demand: a station level analysis of commercial bike sharing in Toronto”, Transportation, 44: 589-613.
Wen-Long Shang, Jinyu Chen, Huibo Bi, Yi Sui, Yanyan Chen, Haitao Yu, 2021, “Impacts of COVID-19 pandemic on user behaviors and environmental benefits of bike sharing: A big-data analysis”, Applied Energy, 285, 116429: 1-14.
Xinwu Qian and Satish V. Ukkusuri, 2015, “Spatial variation of the urban taxi ridership using GPS data”, Applied Geography, 59: 31-42.
Xize Wang, Greg Lindsey, Jessica E. Schoner, Andrew Harrison, 2015, “Modeling Bike Share Station Activity: Effects of Nearby Businesses and Jobs on Trips to and from Stations”, Journal of Urban Planning and Development, 142: 1-9.
Ying Long, Jean-Claude Thill, 2015, “Combining smart card data and household travel survey to analyze jobs–housing relationships in Beijing”, Computers, Environment and Urban Systems, 53: 19-35.
Ying Zhang, Tom Thomas, Mark Brussel, Martin van Maarseveen, 2017, “Exploring the impact of built environment factors on the use of public bikes at bike stations: Case study in Zhongshan, China”, Journal of Transport Geography, 58: 59-70.
Yang Liu, Yanjie Ji, Tao Feng, Zhuangbin Shi, 2021, “A route analysis of metro-bikeshare users using smart card data”, Travel Behaviour and Society, 26: 108-120.
Zhitao Li, Yuzhen Shang, Guanwei Zhao, Muzhuang Yang, 2022, “Exploring the Multiscale Relationship between the Built Environment and the Metro-Oriented Dockless Bike-Sharing Usage”, International Journal of Environmental Research and Public Health, 19(4): 1-21.
（二）其他
Cliff, Andrew D. and John K. Ord., 1973, “Spatial Autocorrelation”, London: Pion.
Cohen, J. 1988. “Statistical power analysis for the behavioral sciences (2nd ed.)”, Hillsdale, NJ: Eribaum.
Darren Buck and Ralph Buehler, 2011, “Bike lanes and other determinants of capital bikeshare trips”, Paper presented at the Transportation Research Board 91st Annual Meeting, Washington, DC.
Jon Froehlich, Joachim Neumann, Nuria Oliver, 2009, “Sensing and Predicting the Pulse of the City through Shared Bicycling”. IJCAI 2009, Proceedings of the International Joint Conference on Artificial Intelligence, Pasadena, California, USA.
Lindsay Kathryn Maurer, 2011, “Suitability study for a bicycle sharing program in Sacramento, California”, University of North Carolina at Chapel Hill, Department of City and Regional Planning.
NACTO, “Equitable bike share means building better places for people to ride”, Bike SHARE Equity Practitioners’ Paper #3, 1-12.
NCHRP, “Estimating Bicycling and Walking for Planning and Project Development: A Guidebook”, NCHRP Report 770, National Cooperative Highway Research Program, Transportation Research Board, Washington, D.C.
Naidong Zhao, Xihui Zhang, M. Shane Banks, Mingke Xiong, “Bicycle Sharing in China: past, present, and future”, AIS 2018 Proceedings 11, 1-7.
Peter Midgley, 2011 ,“Bicycle-Sharing Schemes: Enhancing Sustainable Mobility in Urban Areas”, United Nations Department of Economic and Social Affairs, Commission on Sustainable Development, Nineteenth Session, 2-13.
Paul DeMaio, Steve Chou, Oliver O’Brien, Renata Rabello, Chumin Yu, 2021, “The Meddin Bike-sharing World Map Mid-2021 Report”, 1-26.
Robert C. Hampshire and Lavanya Marla, 2012, “An Analysis of Bike Sharing Usage: Explaining Trip Generation and Attraction from Observed Demand”, TRB 2012 Annual Meeting, 1-17.
R. Christian Solem, 2015, “Limitation of a cross-sectional study”, Readers’ Forum Letter To The Editor, 148(2), 205.
Ripon Patgiri and Arif Ahmed, 2016, “Big Data: The V’s of the Game Changer Paradigm”, 2016 IEEE 18th International Conference on High Performance Computing and Communications, 17-24.
Thomas Holleczek, Liang Yu, Joseph Kang Lee, Oliver Senn, Carlo Ratti, Patrick Jaillet, 2014,“Detecting weak public transport connections from cellphone and public transport data”, in Proceedings of the Third ASE International Conference on Big Data Science and Computing (BigDataScience 2014), 1-8.
Thomas Holleczek, Spiros Antonatos, Han Leong Goh, 2015, “Traffic Measurement and Route Recommendation System for Mass Rapid Transit (MRT)”, KDD `15 Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1859-1868.
Xize Wang, Greg Lindsey, Jessica E. Schoner, Andrew Harrison, 2012, “Modeling bike share station activity: the effects of nearby businesses and jobs on trips to and from stations”, In: Paper Presented at the 92nd Transportation Research Board Annual Meeting 2012, Washington, DC.
三、網頁參考文獻
BikePortland (2018, January 18). City survey: Biketown’s 38,000 riders have boosted economy, reduced car trips since launch. Retrieved April 17, 2022 from BikePortland on the World Wide Web: https://bikeportland.org/2017/01/18/city-survey-biketowns-38000-riders-have-boosted-economy-reduced-car-trips-since-launch-213887.
CNNIC website (2017). The 40th China statistical report on Internet development. Retrieved June 13, 2022 from CNNIC website on the World Wide Web: http://www.cnnic.cn/hlwfzyj/hlwxzbg/hlwtjbg/201708/P020170807351923262153.pdf.
Curbed NY (2017, February 22). MTA reports NYC subway ridership drops for the first time since 2009. Retrieved April 17, 2022 from Curved NY on the World Wide Web: https://ny.curbed.com/2017/2/22/14702318/mta-nyc-annual-subway-ridership-decline-2016.
Ibold, S., Nedopil, C. (2018). The evolution of free-floating bike-sharing in China. Retrieved June 13, 2022 from STUP.org. on the World Wide Web: https://www.sutp.org/en/news-reader/the-evolution-of free-floating-bike-sharing-in-china.html.
NACTO (2018). Shared Micromobility in the U.S.: 2018. Retrieved April 17, 2022 from NACTO on the World Wide Web: https://nacto.org/shared-micromobility-2018/.
The Japan Times (2017, May 1). Urban Japan trying its hand at bicycle-sharing. Retrieved April 17, 2022 from The Japan Times on the World Web: https://www.japantimes.co.jp/news/2017/05/01/reference/urban-japan-trying-hand-bicycle-sharing/#.WaU7xZMjH-Z.
The Meddin Bike-sharing World Map (2021). Retrieved April 12, 2022 from The Meddin Bike-sharing World Map on the World Wide Web: http://bikesharingworldmap.com/。
K-Bike金門公共自行車官方網站，站點資訊，取用日期：2021年9月22日，檢自：https://www.k-bike.com.tw/Stations.aspx。
MOOVO官方網站，租賃站點查詢，取用日期：2021年9月22日，檢自：https://www.ridemoovo.com/city_map_C。
oBike Taiwan（2017/8/29），科技報橘，【三個共享單車產業發展關鍵】從共享單車發展歷史，看科技如何重新形塑城市文化？取用日期2022年4月17日，檢自：https://buzzorange.com/techorange/2017/08/29/bike-shring-history/。
Pbike屏東公共自行車官方網站，租賃站地圖，取用日期：2021年9月22日，檢自：https://pbike.pthg.gov.tw/Station/Map.aspx。
T-Bike臺南市公共自行車官方網站，租賃站查詢，取用日期2021年9月22日，檢自：https://tbike.tainan.gov.tw/portal/zh-tw/station/list。
YouBike微笑單車官方網站，YouBike Locations，取用日期2021年9月22日，檢自：https://www.youbike.com.tw/intro.html。
YouBike微笑單車官方網站，取用日期2021年9月6日，檢自：https://taipei.youbike.com.tw/home。
交通部運輸研究所，2020年版運輸政策白皮書－綠運輸，取用日期2021年8月5日，檢自：https://www.iot.gov.tw/cp-78-200080-7609f-1.html。
段楚禎（2015/3/11），卡優新聞網，取用日期2021年9月3日，檢自：https://www.cardu.com.tw/news/detail.php?nt_pk=27&ns_pk=25533。
陳伯安（2018/7/26），科技報橘，「共享」災難下，世界爭相學習的台灣智慧 YouBike，取用日期2022年4月12日，檢自：https://buzzorange.com/techorange/2018/07/26/taiwans-youbike-is-the-best/。
黃瑋程（2020/9/16），報導者，YouBike2.0試辦後，台灣公共自行車要邁向什麼樣的未來？取用日期2021年9月3日，檢自：https://www.twreporter.org/a/opinion-youbike-2-0-plot。
經濟部能源局，2021，109年度我國燃料燃燒二氧化碳排放統計與分析，取用日期2021年11月23日，檢自：https://www.moeaboe.gov.tw/ECW/populace/content/SubMenu.aspx?menu_id=114。
臺北市政府產業發展局，臺北市循環城市白皮書Circular Taipei 2.0，取用日期2021年8月10日，檢自：https://www.doed.gov.taipei/News_Content.aspx?n=7021B4E8BCB254D5&s=39EAB1CB389EAC05&sms=9391BC948EC0703C。
臺北市政府交通局，YouBike設站有準則 今後選點標準一致，取用日期2021年9月3日，檢自：https://www.dot.gov.taipei/News_Content.aspx?n=D739A9F6B5C0AB95&sms=72544237BBE4C5F6&s=9D0009D2FE920FAD。
臺北市政府交通局，105年公共自行車租賃站設置準則，取用日期2021年8月30日，檢自：https://www-ws.gov.taipei/Download.ashx?u=LzAwMS9VcGxvYWQvMzkwL3JlbGZpbGUvMTk3MjkvMzMyNzEwMS82ODgxNjEzNTM3MS5wZGY%3d&n=Njg4MTYxMzUzNzEucGRm&icon=.pdf。 |
| Description: | 碩士
國立政治大學
地政學系
109257008 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0109257008 |
| Data Type: | thesis |
| DOI: | 10.6814/NCCU202200909 |
| Appears in Collections: | [地政學系] 學位論文
|
Files in This Item:
| File |
Description |
Size | Format | |
|---|
| 700801.pdf | | 21933Kb | Adobe PDF2 | 0 | View/Open |
|
All items in 政大典藏 are protected by copyright, with all rights reserved.
|
