氣候變遷使公共運輸發展愈趨普及，同時也為城市帶來大眾運輸導向發展(Transit-Oriented Development, TOD)的規劃熱潮，公共運具間的轉乘配合亦成為增進TOD 發展的規劃焦點，然而臺灣當前的 TOD 規劃策略較為侷限，僅以步行尺度的思維提升TOD 的發展效益；另外，運輸與發展政策研究所(ITDP)針對 TOD 發展提出了8 項指導原則，其中包含「優先發展自行車網絡」，據此可知，公共自行車今後於 TOD的整體規劃佔有舉足輕重的地位。因此本研究旨以公共自行車的觀點為公共運具的整合使用及 TOD 規劃提供另一種思路。
本研究之目的是透過大數據掌握公共自行車轉乘捷運騎乘距離的趨勢，並以機率模型釐清都市環境中影響該趨勢的關鍵因素及因果關係，冀能提供規劃上的建議及參考。研究範圍及對象為臺北市的公共自行車租借站與捷運站，資料來源為臺北公共運輸電子票證資料庫。研究方法首先將公共自行車轉乘捷運的騎乘距離建立次數分配統計表以獲取轉乘騎乘距離的分布曲線，再利用該分布曲線檢定出 Gamma 分布來描述不同分布型態(pattern)與界定出的都市環境變數之關係，並將兩者的因果關係分別呈現於地理空間中進行更完整的探討。研界結果顯示公共自行車轉乘捷運騎乘距離趨勢約為 2 公里，且影響該趨勢的主要因素為公共自行車站點的配置與捷運站周邊的商業使用強度，故建議能先以捷運站 2 公里服務範圍改善自行車的騎乘環境以促進公共自行車搭配捷運的使用，並可藉由公共自行車站點與商業使用的規劃調配，決定理想的公共自行車轉乘捷運騎乘距離趨勢，進而使TOD 發揮出更大的效益。

Climate change has made the development of public transportation more and more popular, and it has also brought Transit-Oriented Development(TOD) boom to the city. The transfer between public transportation has also become the focus of planning to enhance the effect of TOD. However, the TOD planning strategy in Taiwan is relatively rigid, and only improve the development benefits of TOD by walking scale. Therefore, this study aims to provide another way of thinking for the integrated use of public transportation and TOD planning from the perspective of public bicycles. The first step of the concept is to grasp the trend of the riding distance of the public bicycle transferring to MRT stations and based on big data, then to clarify the environmental factors that are able to affect the riding distance, and choose the appropriate probability distribution to model the variables and the trend of riding distance, and then explain the relationship for planning reference. Taipei City was selected as the study area and data were extracted from the smart card data from the Taipei City Government. First, we establish distribution patterns of the riding distance of public bicycles transferring to MRT stations, and then apply the Gamma distribution to describe the relationship between different patterns and the environmental variables, and connect to the spatial for more explorations. The results show that the trend of riding distance is about 2 kilometers, and the main factors affecting this trend are the configuration of public bicycle stations and the intensity of commercial function around MRT stations. Therefore, the bicycle riding environment in 2km service area of the MRT station should be improve to promote the usage of public bicycles and MRT systems, and we could determine the trend of riding distance by planning the deployment of public bicycle stations and commercial land use to make TOD exert a greater benefit.

林國源 (2005)。高中數學建模課程與實踐之研究。國立交通大學，新竹市，(碩士論文)。
胡宜萍 (2014)。高雄市公共腳踏車與捷運接駁距離暨公共腳踏車租賃站設置地點之探討。國立中山大學，高雄市，(碩士論文)。
賀力行、林淑萍與蔡明春 (2008)。統計學：觀念、方法、應用 (第四版)。新北：前程文化有限公司。
黃晏珊 (2015)。臺北市公共自行車系統營運特性分析。淡江大學，臺北市，(碩士論文)。
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，臺北市，(碩士論文)。
鄭群彥 (2014)。台北公共自行車租賃系統使用型態之分析。國立交通大學，新竹市，(碩士論文)。
鍾智林與李舒媛 (2018)，以悠遊卡大數據初探YouBike租賃及轉乘捷運行為，「都市交通」，第33卷，第1期，第16-36頁。
羅先豪 (2020)。用非負矩陣分解技術探討公共自行車使用之基本型態與租賃站點區位因素之影響。國立成功大學，臺南市，(未出版碩士論文)。
Bachand-Marleau J., Lee B., and El-Geneidy A. (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. doi:10.3141/2314-09.
Buck D., and Buehler R. (2012). Bike Lanes and Other Determinants of Capital Bikeshare Trips.
Côme E., and Oukhellou L. (2014). Model-Based Count Series Clustering for Bike Sharing System Usage Mining: A Case Study with the Vélib’ System of Paris. ACM Transactions on Intelligent Systems and Technology (TIST), 5. doi:10.1145/2560188.
Dieleman F. M., Dijst M., and Burghouwt G. (2002). Urban Form and Travel Behaviour: Micro-level Household Attributes and Residential Context. Urban Studies, 39(3): 507-527. doi:10.1080/00420980220112801.
Dill J., and Voros K. (2007). Factors Affecting Bicycling Demand: Initial Survey Findings from the Portland, Oregon, Region. Transportation Research Record, 2031(1): 9-17. doi:10.3141/2031-02.
Duran-Rodas D., Chaniotakis E., and Antoniou C. (2019). Built Environment Factors Affecting Bike Sharing Ridership: Data-Driven Approach for Multiple Cities. Transportation Research Record, 2673(12): 55-68. doi:10.1177/0361198119849908.
El-Assi W., Salah Mahmoud M., and Nurul Habib K. (2017). Effects of built environment and weather on bike sharing demand: a station level analysis of commercial bike sharing in Toronto. Transportation, 44(3): 589-613. doi:10.1007/s11116-015-9669-z.
Faghih-Imani A., Eluru N., El-Geneidy A. M., Rabbat M., and Haq U. (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. doi:https://doi.org/10.1016/j.jtrangeo.2014.01.013.
Fishman E., Washington S., and Haworth N. (2013). Bike Share: A Synthesis of the Literature. Transport Reviews, 33(2): 148-165. doi:10.1080/01441647.2013.775612.
Fishman E., Washington S., Haworth N., and Mazzei A. (2014). Barriers to bikesharing: an analysis from Melbourne and Brisbane. Journal of Transport Geography, 41: 325-337. doi:https://doi.org/10.1016/j.jtrangeo.2014.08.005.
Giuliano G. (2004). Land use impacts of transportation investments: Highway and transit. The Geography of Urban Transportation: 237-273.
Griffin G. P., and Sener I. N. (2016). Planning for Bike Share Connectivity to Rail Transit. Journal of public transportation, 19(2): 1-22. doi:10.5038/2375-0901.19.2.1.
Guo Y., Zhou J., Wu Y., and Li Z. (2017). Identifying the factors affecting bike-sharing usage and degree of satisfaction in Ningbo, China. PLOS ONE, 12(9): e0185100. doi:10.1371/journal.pone.0185100.
James G., Witten D., Hastie T., and Tibshirani R. (2014). An Introduction to Statistical Learning: with Applications in R: Springer Publishing Company, Incorporated.
Jonkeren O., Kager R., Harms L., and te Brömmelstroet M. (2019). The bicycle-train travellers in the Netherlands: personal profiles and travel choices. Transportation. doi:10.1007/s11116-019-10061-3.
Jäppinen S., Toivonen T., and Salonen M. (2013). Modelling the potential effect of shared bicycles on public transport travel times in Greater Helsinki: An open data approach. Applied Geography, 43: 13-24. doi:https://doi.org/10.1016/j.apgeog.2013.05.010.
Kou Z., and Cai H. (2019). Understanding bike sharing travel patterns: An analysis of trip data from eight cities. Physica A: Statistical Mechanics and its Applications, 515: 785-797. doi:https://doi.org/10.1016/j.physa.2018.09.123.
Liu H., and Lin J.-J. (2019). Associations of built environments with spatiotemporal patterns of public bicycle use. Journal of Transport Geography, 74: 299-312. doi:10.1016/j.jtrangeo.2018.12.010.
Médard de Chardon C., Caruso G., and Thomas I. (2017). Bicycle sharing system ‘success’ determinants. Transportation Research Part A: Policy and Practice, 100: 202-214. doi:https://doi.org/10.1016/j.tra.2017.04.020.
Ma X., Yuan Y., Van Oort N., and Hoogendoorn S. (2020). Bike-sharing systems’ impact on modal shift: A case study in Delft, the Netherlands. Journal of Cleaner Production, 259: 120846. doi:https://doi.org/10.1016/j.jclepro.2020.120846.
Martens K. (2007). Promoting bike-and-ride: The Dutch experience. Transportation Research Part A: Policy and Practice, 41(4): 326-338. doi:https://doi.org/10.1016/j.tra.2006.09.010.
Noland R., and Ishaque M. (2006). Smart Bicycles in an Urban Area Smart Bicycles in an Urban Area: Evaluation of a Pilot Scheme in London. Journal of public transportation, 9. doi:10.5038/2375-0901.9.5.5.
Pucher J., and Buehler R. (2006). Why Canadians cycle more than Americans: A comparative analysis of bicycling trends and policies. Transport Policy, 13(3): 265-279. doi:https://doi.org/10.1016/j.tranpol.2005.11.001.
Rojas-Rueda D., de Nazelle A., Tainio M., and Nieuwenhuijsen M. J. (2011). The health risks and benefits of cycling in urban environments compared with car use: health impact assessment study. Bmj, 343: d4521. doi:10.1136/bmj.d4521.
Shaheen S. A., Zhang H., Martin E., and Guzman S. (2011). China's Hangzhou Public Bicycle: Understanding Early Adoption and Behavioral Response to Bikesharing. Transportation Research Record, 2247(1): 33-41. doi:10.3141/2247-05.
Tran T. D., Ovtracht N., and d’Arcier B. F. (2015). Modeling Bike Sharing System using Built Environment Factors. Procedia CIRP, 30: 293-298. doi:https://doi.org/10.1016/j.procir.2015.02.156.
Veryard D., and Perkins S. (2017). Re: Integrating Urban Public Transport system and Cycling. Message posted to https://www.itf-oecd.org/sites/default/files/docs/integrating-urban-public-transport-systems-cycling-roundtable-summary_0.pdf
Wang J. Y. T., Mirza L., Cheung A. C. K., and Moradi S. (2014). Understanding factors influencing choices of cyclists and potential cyclists: A case study at the University of Auckland. Road & Transport Research, 23: 37.
Wu Y.-H., Kang L., Hsu Y.-T., and Wang P.-C. (2019). Exploring trip characteristics of bike-sharing system uses: Effects of land-use patterns and pricing scheme change. International Journal of Transportation Science and Technology, 8(3): 318-331. doi:https://doi.org/10.1016/j.ijtst.2019.05.003.