因十八世紀的工業革命，工業技術進步帶動汽機車產業蓬勃發展，加上各國投入公路建設，使得私人運具的持有率不斷上升，卻造成全球暖化、能源短缺的情況加劇。為此，各國開始提倡永續運輸，期望能透過發展公共運輸、建構人本運輸環境並制定政策以及使用綠色運具來減緩運輸部門對環境的衝擊。
於21世紀開始廣為流行的公共自行車系統，供民眾以租借代替擁有的方式騎乘自行車，其費用低廉，且擁有彈性旅次特性使得民眾不再受限於單程旅次，不僅可以代替部分私人運具的短程旅次，同時也能補足大眾運輸系統的第一哩或最後一哩路，間接提升大眾運輸使用率。許多研究針對公共自行車系統的使用者分析其旅運行為，與影響其使用意願之因素，發現除了城市既有條件與系統本身設置外，騎乘環境也愈來愈受到民眾的重視，但鮮少研究針對此一影響因子做量化分析。因此本研究以臺北市YouBike系統作為研究對象，透過資料探勘分析臺北市市區三橫三縱自行車道對民眾選擇騎乘行為的影響，並建置個人行為導向模型，除可觀察公共自行車系統在所有運具中的市占率外，亦可分析不同騎乘環境建置與政策下的環境效益影響。
本研究篩選經過臺北市市區三橫三縱自行車道的旅次對，並計數該路線經過自行車道上站點數量，作為該路線騎經自行車道的長度象徵，隨後以自行車道的建置月份為基準，分析固定旅次對在建置月份前後六個月的日平均旅次量，而結果顯示自行車道路線寬度足夠且一致的信義路與復興路有上升的趨勢，建置方式以在既有人行道上劃設標線的仁愛路、松江新生南路與中山北路的旅次吸引貢獻較為不明顯，另外同為路線寬度一致的南京路則因建置後大量興建站點且捷運松山信義線通車，致使原旅次對的旅次量遭瓜分，日平均旅次量大幅下降。
透過騎乘經過三橫三縱自行車道的旅次對，在自行車道建置前後的旅次量成長率作為民眾選用公共自行車意願的加成，藉此建構自行車道路網之於民眾選擇旅次運具的決策行為的個體行為導向模型。模型以1,000人進行為期7天的三種情境模擬，結果顯示有自行車道路網時，所能減少的空氣污染物排放占比比無自行車道路網時高出12.7%，減緩環境衝擊的部分亦高出10.8%的人類健康衝擊、11.5%的生態衝擊與12.2%的資源衝擊。而自行車專用道的優化，較優化前的空氣污染物減排多出0.08%，衝擊的減緩則多出0.2%的效益。
本研究所建構的個體行為導向模型可直接觀察公共自行車系統的旅次分布，且結合環境效益的計算 (空氣污染物排放量與環境衝擊)，可供往後決策者制定政策時的參考依據。

The rapid expansion of the Bike Sharing Systems (BSS) in recent years has not only complemented the first or last mile of the public transportation system, but also replaced some short-distance trips by private vehicles. Previous studies have analyzed the travel behaviors of BSS users and the factors that affect their willingness to use them. However, few studies have done quantitative analysis on riding environment factors. Therefore, using the YouBike system in Taipei City as the subject, this research analyzed the impacts of Taipei City’s three horizontal and three vertical bikeway networks. The trip data of the YouBike system were analyzed with tailor-made data mining for building a model with a bikeway network that allows the public to choose travel modes and times. The agent-based model was developed to simulate the decision-making behaviors of trips in Taipei. This study presents the simulation results of the scenarios with and without bike lanes and the scenario with improved bicycle lanes. The changes in people’s choice of their travel modes vehicles generate environmental benefits of reduced air pollution and environmental impacts. The results show that when a bike-lane network becomes accessible, the mobile air pollutant emissions can be 12.7% less than the situation without a bike-lane network. And the reduced environmental impact proportions are 10.8%, 11.5%, and 12.2% than the scenario without a bike-lane network at human health, ecosystem, and resource. In addition, the improvement on bike lane road conditions (including width and continuity) has a slightly higher effect on reducing air pollutants and environmental impacts by about 0.2%. The simulation model built in this study can display the distribution of the trips hiring public bikes and show the dynamic change in the calculated environmental benefits (air pollutant emissions and environmental impact), which can be used as a reference for policymakers in the future.

Ashqar, H. I., Elhenawy, M., Almannaa, M. H., Ghanem, A., Rakha, H. A., & House, L. (2017, 26-28 June 2017). Modeling bike availability in a bike-sharing system using machine learning. Paper presented at the 2017 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS).
Ashqar, H. I., Elhenawy, M., & Rakha, H. A. (2019). Modeling bike counts in a bike-sharing system considering the effect of weather conditions. Case Studies on Transport Policy, 7(2), 261-268. Retrieved from http://www.sciencedirect.com/science/article/pii/S2213624X16301018. doi:https://doi.org/10.1016/j.cstp.2019.02.011
Bachand-Marleau, J., Lee, B. H. Y., & El-Geneidy, A. M. (2012). Better Understanding of Factors Influencing Likelihood of Using Shared Bicycle Systems and Frequency of Use. Transportation Research Record, 2314(1), 66-71. Retrieved from https://journals.sagepub.com/doi/abs/10.3141/2314-09. doi:10.3141/2314-09
Bike-Share Opportunities in New York City. (2009). Retrieved from New York City Department of City Planning:
Bikeshare, C. (2012). Capital Bikeshare. 2011 Member Survey Report.
Bonabeau, E. (2002). Agent-based modeling: Methods and techniques for simulating human systems. Proceedings of the National Academy of Sciences, 99(suppl 3), 7280. Retrieved from http://www.pnas.org/content/99/suppl_3/7280.abstract. doi:10.1073/pnas.082080899
Borgnat, P., Abry, P., Flandrin, P., Robardet, C., Rouquier, J.-B., & Fleury, E. (2011). SHARED BICYCLES IN A CITY: A SIGNAL PROCESSING AND DATA ANALYSIS PERSPECTIVE. Advances in Complex Systems, 14(03), 415-438. Retrieved from https://doi.org/10.1142/S0219525911002950. doi:10.1142/S0219525911002950
Campbell, A. A., Cherry, C. R., Ryerson, M. S., & Yang, X. (2016). Factors influencing the choice of shared bicycles and shared electric bikes in Beijing. Transportation Research Part C: Emerging Technologies, 67, 399-414. Retrieved from http://www.sciencedirect.com/science/article/pii/S0968090X16000747. doi:https://doi.org/10.1016/j.trc.2016.03.004
Cao, Y., & Shen, D. (2019). Contribution of shared bikes to carbon dioxide emission reduction and the economy in Beijing. Sustainable Cities and Society, 51, 101749. Retrieved from http://www.sciencedirect.com/science/article/pii/S2210670719308765. doi:https://doi.org/10.1016/j.scs.2019.101749
Cervero, R., & Kockelman, K. (1997). Travel demand and the 3Ds: Density, diversity, and design. Transportation Research Part D: Transport and Environment, 2(3), 199-219. Retrieved from http://www.sciencedirect.com/science/article/pii/S1361920997000096. doi:https://doi.org/10.1016/S1361-9209(97)00009-6
Cervero, R., Sarmiento, O. L., Jacoby, E., Gomez, L. F., & Neiman, A. (2009). Influences of Built Environments on Walking and Cycling: Lessons from Bogotá. International Journal of Sustainable Transportation, 3(4), 203-226. doi:10.1080/15568310802178314
Chen, P., Zhou, J., & Sun, F. (2017). Built environment determinants of bicycle volume: A longitudinal analysis. Journal of Transport and Land Use, 10(1), 655-674. Retrieved from https://www.scopus.com/inward/record.uri?eid=2-s2.0-85021114602&doi=10.5198%2fjtlu.2017.892&partnerID=40&md5=0842cb9063c7886116c47bb587c96480. doi:10.5198/jtlu.2017.892
Corcoran, J., Li, T., Rohde, D., Charles-Edwards, E., & Mateo-Babiano, D. (2014). Spatio-temporal patterns of a Public Bicycle Sharing Program: the effect of weather and calendar events. Journal of Transport Geography, 41, 292-305. Retrieved from http://www.sciencedirect.com/science/article/pii/S0966692314001951. doi:https://doi.org/10.1016/j.jtrangeo.2014.09.003
Croci, E., & Rossi, D. (2014). Optimizing the position of bike sharing stations. The Milan case.
DeMaio, P. (2009). Bike-sharing: History, Impacts, Models of Provision, and Future. Journal of Public Transportation, 12. doi:10.5038/2375-0901.12.4.3
Diez, C., Sanchez-Anguix, V., Palanca, J., Julian, V., & Giret, A. (2018). Station Status Forecasting Module for a Multi-agent Proposal to Improve Efficiency on Bike-Sharing Usage, Cham.
Dill, J., & Carr, T. (2003). Bicycle commuting and facilities in major US cities: if you build them, commuters will use them. Transportation Research Record, 1828(1), 116-123.
Duran-Rodas, D., Chaniotakis, E., & Antoniou, C. (2019). Built Environment Factors Affecting Bike Sharing Ridership: Data-Driven Approach for Multiple Cities. Transportation Research Record, 2673(12), 55-68. Retrieved from https://doi.org/10.1177/0361198119849908. doi:10.1177/0361198119849908
El-Assi, W., Salah Mahmoud, M., & 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. Retrieved from https://doi.org/10.1007/s11116-015-9669-z. doi:10.1007/s11116-015-9669-z
Eren, E., & Uz, V. E. (2020). A review on bike-sharing: The factors affecting bike-sharing demand. Sustainable Cities and Society, 54, 101882. Retrieved from http://www.sciencedirect.com/science/article/pii/S2210670719312387. doi:https://doi.org/10.1016/j.scs.2019.101882
Faghih-Imani, A., & Eluru, N. (2015). Analysing bicycle-sharing system user destination choice preferences: Chicago’s Divvy system. Journal of Transport Geography, 44, 53-64. Retrieved from http://www.sciencedirect.com/science/article/pii/S0966692315000459. doi:https://doi.org/10.1016/j.jtrangeo.2015.03.005
Faghih-Imani, A., Eluru, N., El-Geneidy, A. M., Rabbat, M., & 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. Retrieved from http://www.sciencedirect.com/science/article/pii/S0966692314000234. doi:https://doi.org/10.1016/j.jtrangeo.2014.01.013
Fernández, A., Timón, S., Ruiz, C., Cumplido, T., Billhardt, H., & Dunkel, J. (2018). A Bike Sharing System Simulator, Cham.
Fishman, E. (2016). Bikeshare: A Review of Recent Literature. Transport Reviews, 36(1), 92-113. Retrieved from https://doi.org/10.1080/01441647.2015.1033036. doi:10.1080/01441647.2015.1033036
Fishman, E., & Allan, V. (2019). Bike share. In Advances in Transport Policy and Planning: Academic Press.
Fishman, E., Washington, S., & Haworth, N. (2014). Bike share’s impact on car use: Evidence from the United States, Great Britain, and Australia. Transportation Research Part D: Transport and Environment, 31, 13-20. Retrieved from http://www.sciencedirect.com/science/article/pii/S1361920914000480. doi:https://doi.org/10.1016/j.trd.2014.05.013
Fishman, E., Washington, S., Haworth, N., & Watson, A. (2015). Factors influencing bike share membership: An analysis of Melbourne and Brisbane. Transportation Research Part A: Policy and Practice, 71, 17-30. Retrieved from http://www.sciencedirect.com/science/article/pii/S0965856414002638. doi:https://doi.org/10.1016/j.tra.2014.10.021
Frank, L. D., & Pivo, G. (1994). Impacts of mixed use and density on utilization of three modes of travel: single-occupant vehicle, transit, and walking. Transportation Research Record, 1466, 44-52.
Fricker, C., & Gast, N. (2016). Incentives and redistribution in homogeneous bike-sharing systems with stations of finite capacity. EURO Journal on Transportation and Logistics, 5(3), 261-291. Retrieved from http://www.sciencedirect.com/science/article/pii/S2192437620300959. doi:https://doi.org/10.1007/s13676-014-0053-5
Froehlich, J., Neumann, J., & Oliver, N. (2008). Measuring the pulse of the city through shared bicycle programs. Proc. of UrbanSense08, 16-20.
Fuller, D., Gauvin, L., Kestens, Y., Daniel, M., Fournier, M., Morency, P., & Drouin, L. (2011). Use of a New Public Bicycle Share Program in Montreal, Canada. American journal of preventive medicine, 41, 80-83. doi:10.1016/j.amepre.2011.03.002
Gebhart, K., & Noland, R. B. (2014). The impact of weather conditions on bikeshare trips in Washington, DC. Transportation, 41(6), 1205-1225. Retrieved from https://doi.org/10.1007/s11116-014-9540-7. doi:10.1007/s11116-014-9540-7
Ghosh, S., Varakantham, P., Adulyasak, Y., & Jaillet, P. (2017). Dynamic repositioning to reduce lost demand in bike sharing systems. Journal of Artificial Intelligence Research, 58, 387-430.
Grimm, V., & Railsback, S. F. (2005). Individual-based modeling and ecology (Vol. 8): Princeton university press.
Habib, K. N., Mann, J., Mahmoud, M., & Weiss, A. (2014). Synopsis of bicycle demand in the City of Toronto: Investigating the effects of perception, consciousness and comfortability on the purpose of biking and bike ownership. Transportation Research Part A: Policy and Practice, 70, 67-80.
Haider, Z., Nikolaev, A., Kang, J. E., & Kwon, C. (2018). Inventory rebalancing through pricing in public bike sharing systems. European Journal of Operational Research, 270(1), 103-117. Retrieved from http://www.sciencedirect.com/science/article/pii/S0377221718302030. doi:https://doi.org/10.1016/j.ejor.2018.02.053
Hamilton, T. L., & Wichman, C. J. (2018). Bicycle infrastructure and traffic congestion: Evidence from DC’s Capital Bikeshare. Journal of Environmental Economics and Management, 87, 72-93. Retrieved from http://www.sciencedirect.com/science/article/pii/S0095069616300420. doi:https://doi.org/10.1016/j.jeem.2017.03.007
Hood, J., Sall, E., & Charlton, B. (2011). A GPS-based bicycle route choice model for San Francisco, California. Transportation Letters, 3(1), 63-75. Retrieved from https://doi.org/10.3328/TL.2011.03.01.63-75. doi:10.3328/TL.2011.03.01.63-75
HU, G., PEKKARINEN, H., HÄNNINEN, O., YU, Z., GUO, Z., & TIAN, H. (2002). Commuting, leisure-time physical activity, and cardiovascular risk factors in China. Medicine & Science in Sports & Exercise, 34(2), 234-238. Retrieved from https://journals.lww.com/acsm-msse/Fulltext/2002/02000/Commuting,_leisure_time_physical_activity,_and.9.aspx.
Huijbregts, M., Steinmann, Z., Elshout, P. M. F., Stam, G., Verones, F., Vieira, M., . . . Zelm, R. (2016). ReCiPe 2016. A harmonized life cycle impact assessment method at midpoint and endpoint level. Report 1: Characterization.
Huijbregts, M. A. J., Steinmann, Z. J. N., Elshout, P. M. F., Stam, G., Verones, F., Vieira, M., . . . van Zelm, R. (2017). ReCiPe2016: a harmonised life cycle impact assessment method at midpoint and endpoint level. The International Journal of Life Cycle Assessment, 22(2), 138-147. Retrieved from https://doi.org/10.1007/s11367-016-1246-y. doi:10.1007/s11367-016-1246-y
Hyland, M., Hong, Z., de Farias Pinto, H. K. R., & Chen, Y. (2018). Hybrid cluster-regression approach to model bikeshare station usage. Transportation Research Part A: Policy and Practice, 115, 71-89.
International Energy Agency, I. (2020). Tracking Transport 2020. Retrieved from Paris: https://www.iea.org/reports/tracking-transport-2020
Jacobsen, P. L. (2003). Safety in numbers: more walkers and bicyclists, safer walking and bicycling. Injury Prevention, 9(3), 205-209. Retrieved from https://injuryprevention.bmj.com/content/injuryprev/9/3/205.full.pdf. doi:10.1136/ip.9.3.205
Jing, C., & Zhao, Z. (2015). Research on Antecedents and Consequences of Factors Affecting the Bike Sharing System---Lessons From Capital Bike Share Program in Washington, DC. Paper presented at the International Conference on Logistics Engineering, Management and Computer Science (LEMCS 2015).
Kim, D., Shin, H., Im, H., & Park, J. (2012). Factors influencing travel behaviors in bikesharing. Paper presented at the Transportation research board 91st annual meeting. Washington, DC: Transportation Research Board.
Kim, K. (2018). Investigation on the effects of weather and calendar events on bike-sharing according to the trip patterns of bike rentals of stations. Journal of Transport Geography, 66, 309-320. Retrieved from http://www.sciencedirect.com/science/article/pii/S0966692317304659. doi:https://doi.org/10.1016/j.jtrangeo.2018.01.001
Kong, H., Jin, S. T., & Sui, D. Z. (2020). Deciphering the relationship between bikesharing and public transit: Modal substitution, integration, and complementation. Transportation Research Part D: Transport and Environment, 85, 102392. Retrieved from http://www.sciencedirect.com/science/article/pii/S1361920920305794. doi:https://doi.org/10.1016/j.trd.2020.102392
Kou, Z., & 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. Retrieved from http://www.sciencedirect.com/science/article/pii/S0378437118312561. doi:https://doi.org/10.1016/j.physa.2018.09.123
Kutela, B., & Kidando, E. (2017). Towards a Better Understanding of Effectiveness of Bike-share Programs: Exploring Factors Affecting Bikes Idle Duration. American Scientific Research Journal for Engineering, Technology, and Sciences, 29, 33-46.
Kutela, B., & Teng, H. (2019). The influence of campus characteristics, temporal factors, and weather events on campuses-related daily bike-share trips. Journal of Transport Geography, 78, 160-169. Retrieved from http://www.sciencedirect.com/science/article/pii/S0966692318308378. doi:https://doi.org/10.1016/j.jtrangeo.2019.06.002
Lathia, N., Ahmed, S., & Capra, L. (2012). Measuring the impact of opening the London shared bicycle scheme to casual users. Transportation Research Part C: Emerging Technologies, 22, 88-102. doi:10.1016/j.trc.2011.12.004
Leao, S. Z., & Pettit, C. (2017). Mapping Bicycling Patterns with an Agent-Based Model, Census and Crowdsourced Data, Cham.
Lin, J.-J., Zhao, P., Takada, K., Li, S., Yai, T., & Chen, C.-H. (2018). Built environment and public bike usage for metro access: A comparison of neighborhoods in Beijing, Taipei, and Tokyo. Transportation Research Part D: Transport and Environment, 63, 209-221. Retrieved from http://www.sciencedirect.com/science/article/pii/S1361920917307198. doi:https://doi.org/10.1016/j.trd.2018.05.007
Litman, T., & Steele, R. (2017). Land use impacts on transport: Victoria Transport Policy Institute Canada.
Lu, M., Hsu, S.-C., Chen, P.-C., & Lee, W.-Y. (2018). Improving the sustainability of integrated transportation system with bike-sharing: A spatial agent-based approach. Sustainable Cities and Society, 41, 44-51. Retrieved from http://www.sciencedirect.com/science/article/pii/S2210670718304700. doi:https://doi.org/10.1016/j.scs.2018.05.023
Lu, W., Scott, D. M., & Dalumpines, R. (2018). Understanding bike share cyclist route choice using GPS data: Comparing dominant routes and shortest paths. Journal of Transport Geography, 71, 172-181. Retrieved from http://www.sciencedirect.com/science/article/pii/S0966692318300875. doi:https://doi.org/10.1016/j.jtrangeo.2018.07.012
Lu, Y., Yang, Y., Sun, G., & Gou, Z. (2019). Associations between overhead-view and eye-level urban greenness and cycling behaviors. Cities, 88, 10-18. Retrieved from http://www.sciencedirect.com/science/article/pii/S0264275118307546. doi:https://doi.org/10.1016/j.cities.2019.01.003
Luo, H., Kou, Z., Zhao, F., & Cai, H. (2019). Comparative life cycle assessment of station-based and dock-less bike sharing systems. Resources, Conservation and Recycling, 146, 180-189. Retrieved from http://www.sciencedirect.com/science/article/pii/S0921344919301090. doi:https://doi.org/10.1016/j.resconrec.2019.03.003
Mao, C., Zou, M., Liu, Y., Xue, W., & Li, M. (2015). Comparison of Agent-Based Modeling and Equation-Based Modeling for Transportation Behavioral Studies.
Meddin, R., DeMaio, P., O’Brien, O., Rabello, R., Yu, C., & Seamon, J. The Meddin Bike-sharing World Map. Retrieved from http://bikesharingworldmap.com/.
Midgley, P. (2009). The role of smart bike-sharing systems in urban mobility. Journeys, 2(1), 23-31.
Midgley, P. (2011). Bicycle-sharing schemes: enhancing sustainable mobility in urban areas. United Nations, Department of Economic and Social Affairs, 8, 1-12.
Moudon, A. V., Lee, C., Cheadle, A. D., Collier, C. W., Johnson, D., Schmid, T. L., & Weather, R. D. (2005). Cycling and the built environment, a US perspective. Transportation Research Part D: Transport and Environment, 10(3), 245-261. Retrieved from http://www.sciencedirect.com/science/article/pii/S1361920905000167. doi:https://doi.org/10.1016/j.trd.2005.04.001
Murphy, E., & Usher, J. (2015). The Role of Bicycle-sharing in the City: Analysis of the Irish Experience. International Journal of Sustainable Transportation, 9(2), 116-125. Retrieved from https://doi.org/10.1080/15568318.2012.748855. doi:10.1080/15568318.2012.748855
Noland, R. B., Smart, M. J., & Guo, Z. (2016). Bikeshare trip generation in New York City. Transportation Research Part A: Policy and Practice, 94, 164-181. Retrieved from http://www.sciencedirect.com/science/article/pii/S0965856416307716. doi:https://doi.org/10.1016/j.tra.2016.08.030
Ogilvie, F., & Goodman, A. (2012). Inequalities in usage of a public bicycle sharing scheme: Socio-demographic predictors of uptake and usage of the London (UK) cycle hire scheme. Preventive Medicine, 55(1), 40-45. Retrieved from http://www.sciencedirect.com/science/article/pii/S0091743512001685. doi:https://doi.org/10.1016/j.ypmed.2012.05.002
Otero, I., Nieuwenhuijsen, M. J., & Rojas-Rueda, D. (2018). Health impacts of bike sharing systems in Europe. Environment International, 115, 387-394. Retrieved from http://www.sciencedirect.com/science/article/pii/S0160412017321566. doi:https://doi.org/10.1016/j.envint.2018.04.014
Pucher, J., Komanoff, C., & Schimek, P. (1999). Bicycling renaissance in North America?: Recent trends and alternative policies to promote bicycling. Transportation Research Part A: Policy and Practice, 33(7-8), 625-654.
Ricci, M. (2015). Bike sharing: A review of evidence on impacts and processes of implementation and operation. Research in Transportation Business & Management, 15, 28-38. Retrieved from http://www.sciencedirect.com/science/article/pii/S2210539515000140. doi:https://doi.org/10.1016/j.rtbm.2015.03.003
Rixey, R. A. (2013). Station-Level Forecasting of Bikesharing Ridership: Station Network Effects in Three U.S. Systems. Transportation Research Record, 2387(1), 46-55. Retrieved from https://doi.org/10.3141/2387-06. doi:10.3141/2387-06
Rudloff, C., & Lackner, B. (2014). Modeling Demand for Bikesharing Systems:Neighboring Stations as Source for Demand and Reason for Structural Breaks. Transportation Research Record, 2430(1), 1-11. Retrieved from https://journals.sagepub.com/doi/abs/10.3141/2430-01. doi:10.3141/2430-01
Schoner, J. E., & Levinson, D. M. (2014). The missing link: Bicycle infrastructure networks and ridership in 74 US cities. Transportation, 41(6), 1187-1204.
Scott, D. M., & Ciuro, C. (2019). What factors influence bike share ridership? An investigation of Hamilton, Ontario’s bike share hubs. Travel Behaviour and Society, 16, 50-58. Retrieved from http://www.sciencedirect.com/science/article/pii/S2214367X18300188. doi:https://doi.org/10.1016/j.tbs.2019.04.003
Shaheen, S. A., Cohen, A. P., & Martin, E. W. (2013). Public Bikesharing in North America: Early Operator Understanding and Emerging Trends. Transportation Research Record, 2387(1), 83-92. Retrieved from https://doi.org/10.3141/2387-10. doi:10.3141/2387-10
Shaheen, S. A., Guzman, S., & Zhang, H. (2010). Bikesharing in Europe, the Americas, and Asia: Past, Present, and Future. Transportation Research Record, 2143(1), 159-167. Retrieved from https://doi.org/10.3141/2143-20. doi:10.3141/2143-20
Shaheen, S. A., Martin, E. W., Cohen, A. P., Chan, N. D., & Pogodzinski, M. (2014). Public Bikesharing in North America During a Period of Rapid Expansion: Understanding Business Models, Industry Trends & User Impacts, MTI Report 12-29.
Simons, D., Clarys, P., De Bourdeaudhuij, I., de Geus, B., Vandelanotte, C., & Deforche, B. (2013). Factors influencing mode of transport in older adolescents: a qualitative study. BMC public health, 13(1), 323.
Soriguera, F., Casado, V., & Jiménez, E. (2018). A simulation model for public bike-sharing systems. Transportation Research Procedia, 33, 139-146. Retrieved from http://www.sciencedirect.com/science/article/pii/S2352146518302412. doi:https://doi.org/10.1016/j.trpro.2018.10.086
Studio, B.-S. (2010). Seattle Bicycle Share Feasibility Study. Department of Urban Design and Planning, College of Built Environment, University of Washington.
Sun, F., Chen, P., & Jiao, J. (2018). Promoting public bike-sharing: A lesson from the unsuccessful Pronto system. Transportation Research Part D: Transport and Environment, 63, 533-547. Retrieved from http://www.sciencedirect.com/science/article/pii/S136192091731057X. doi:https://doi.org/10.1016/j.trd.2018.06.021
Taillandier, P., Gaudou, B., Grignard, A., Huynh, Q.-N., Marilleau, N., Caillou, P., . . . Drogoul, A. (2019). Building, composing and experimenting complex spatial models with the GAMA platform. GeoInformatica, 23(2), 299-322. Retrieved from https://doi.org/10.1007/s10707-018-00339-6. doi:10.1007/s10707-018-00339-6
Vogel, P., Greiser, T., & Mattfeld, D. C. (2011). Understanding Bike-Sharing Systems using Data Mining: Exploring Activity Patterns. Procedia - Social and Behavioral Sciences, 20, 514-523. Retrieved from http://www.sciencedirect.com/science/article/pii/S1877042811014388. doi:https://doi.org/10.1016/j.sbspro.2011.08.058
Vogel, P., & Mattfeld, D. C. (2011, 2011//). Strategic and Operational Planning of Bike-Sharing Systems by Data Mining – A Case Study. Paper presented at the Computational Logistics, Berlin, Heidelberg.
von Huth Smith, L., Borch-Johnsen, K., & Jørgensen, T. (2007). Commuting physical activity is favourably associated with biological risk factors for cardiovascular disease. European Journal of Epidemiology, 22(11), 771. Retrieved from https://doi.org/10.1007/s10654-007-9177-3. doi:10.1007/s10654-007-9177-3
Vythoulkas, P. C., & Koutsopoulos, H. N. (2003). Modeling discrete choice behavior using concepts from fuzzy set theory, approximate reasoning and neural networks. Transportation Research Part C: Emerging Technologies, 11(1), 51-73.
Wang, K., Akar, G., & Chen, Y.-J. (2018). Bike sharing differences among Millennials, Gen Xers, and Baby Boomers: Lessons learnt from New York City’s bike share. Transportation Research Part A: Policy and Practice, 116, 1-14. Retrieved from http://www.sciencedirect.com/science/article/pii/S0965856417306419. doi:https://doi.org/10.1016/j.tra.2018.06.001
Wang, M., & Zhou, X. (2017). Bike-sharing systems and congestion: Evidence from US cities. Journal of Transport Geography, 65, 147-154. Retrieved from http://www.sciencedirect.com/science/article/pii/S0966692317302715. doi:https://doi.org/10.1016/j.jtrangeo.2017.10.022
WCED, S. W. S. (1987). World commission on environment and development. Our common future, 17, 1-91.
Wernet, G., Bauer, C., Steubing, B., Reinhard, J., Moreno-Ruiz, E., and Weidema, B., 2016. The ecoinvent database version 3 (part I): overview and methodology. The International Journal of Life Cycle Assessment, [online] 21(9), pp.1218–1230. Available at: <http://link.springer.com/10.1007/s11367-016-1087-8> [Accessed 31 12 2020].
Yang, Y., Diez Roux, A. V., Auchincloss, A. H., Rodriguez, D. A., & Brown, D. G. (2011). A spatial agent-based model for the simulation of adults’ daily walking within a city. American journal of preventive medicine, 40(3), 353-361. Retrieved from https://pubmed.ncbi.nlm.nih.gov/21335269. doi:10.1016/j.amepre.2010.11.017
Yao, Y., Jiang, X., & Li, Z. (2019). Spatiotemporal characteristics of green travel: A classification study on a public bicycle system. Journal of Cleaner Production, 238, 117892. Retrieved from http://www.sciencedirect.com/science/article/pii/S0959652619327623. doi:https://doi.org/10.1016/j.jclepro.2019.117892
Zhang, Y., & Mi, Z. (2018). Environmental benefits of bike sharing: A big data-based analysis. Applied Energy, 220, 296-301. Retrieved from http://www.sciencedirect.com/science/article/pii/S0306261918304392. doi:https://doi.org/10.1016/j.apenergy.2018.03.101
Zhou, X. (2015). Understanding Spatiotemporal Patterns of Biking Behavior by Analyzing Massive Bike Sharing Data in Chicago. PLoS One, 10(10), e0137922. Retrieved from https://www.ncbi.nlm.nih.gov/pubmed/26445357. doi:10.1371/journal.pone.0137922
王少谷. (2015). 公共自行車租用與都市土地使用型態關聯性之探討. 成功大學, 取自 https://hdl.handle.net/11296/wm258z
公共運輸整合資訊流通服務平台 (2020) 取自 https://ptx.transportdata.tw/PTX/
白詩滎. (2014). 臺北公共自行車使用行為特性分析與友善環境建構之研究. 白詩滎, 取自 https://hdl.handle.net/11296/9a8n23
交通部統計處(2017)，「民眾日常使用運具狀況調查摘要分析」, 取自 https://www.motc.gov.tw/ch/home.jsp?id=1679&parentpath=0,6&mcustomize=statistics105.jsp
交通部運輸研究所(2017). 自行車道系統規劃設計參考手冊。臺北:交通部運輸研究所, 取自 https://www.iot.gov.tw/mp-1.html
交通網路地理資訊倉儲系統 (2020), 中華民國交通部取自 https://gist.motc.gov.tw/
余書玫. (2009). 公共自行車租借系統選擇行為之研究. (碩士), 國立交通大學, 新竹市. 取自 https://hdl.handle.net/11296/m6btd4
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，取自 http://www.AiritiLibrary.com/Publication/Index/U0002-2507201809283600
林宏易. (2018). 高雄市公共自行車系統旅運行為模擬. 成功大學, 取自 http://www.AiritiLibrary.com/Publication/Index/U0026-2102201818532700
陳品竹. (2014). 臺北市公共自行車使用者意向之研究. 臺灣大學, 取自 Airiti https://www.airitilibrary.com/Publication/alDetailedMesh1?DocID=U0001-0808201422145800
黃俊良. (2016). 臺北市公共自行車系統旅次特性分析. 淡江大學, 取自 http://www.AiritiLibrary.com/Publication/Index/U0002-0507201609003900
詹詩姿, & 蘇瑛敏. (2008). 淺談市區型自行車道之規劃原則-以台北市為例.
廖洵顏. (2018). 都市公共自行車健康影響評估-以台北市Youbike為例. 國立臺灣大學, 取自 https://www.airitilibrary.com/Publication/alDetailedMesh1?DocID=U0001-0908201822090000
鄭群彥. (2014). 台北公共自行車租賃系統使用型態之分析. 交通大學, 取自 http://dx.doi.org/10.6842/NCTU.2014.00621
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, 取自 http://www.AiritiLibrary.com/Publication/Index/U0002-0102201822071000
蘇振維, 張舜淵, 楊幼文, 鄭嘉盈, 高錫鉦, 黃志清, . . . 張耕碩. (2017). 自行車道系統規劃設計參考手冊. 臺北市: 交通部運輸研究所.
臺北市政府交通局(2018)，「107 年臺北市民眾日常使用運具狀況摘要分析」, 取自 https://www.dot.gov.taipei/News.aspx?n=44EAAF8913752298&sms=DADC9630355BA510
臺北市交通管制工程處自行車道建置表(2019), 取自 https://www.bote.gov.taipei/cp.aspx?n=9E7799DC94488A28
臺北市資料大平臺(2020), 臺北:臺北市政府資訊局, 取自 https://data.taipei/#/