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| 研究生： |
陸靜廷 Christina Jean Louis |
|---|---|
| 論文名稱： |
臺灣平台型共享運具與城市發展成果：以 2016–2024 年七大城市之 YouBike 與 WeMo 為例 Platform-Based Shared Mobility and Urban Outcomes in Taiwan: Evidence from YouBike and WeMo Across Seven Major Cities, 2016–2024 |
| 指導教授： |
史蘭亭
Alicia Say |
| 口試委員: |
吳文傑
Jack Wu 鄭輝培 Terry Cheng |
| 學位類別： |
碩士
Master |
| 系所名稱： |
社會科學學院 - 應用經濟與社會發展英語碩士學位學程(IMES) International Master's Program of Applied Economics and Social Development(IMES) |
| 論文出版年： | 2026 |
| 畢業學年度： | 114 |
| 語文別： | 英文 |
| 論文頁數： | 119 |
| 中文關鍵詞： | 共享移動 、平台型運輸服務 、Youbike 、WeMo 、城市空氣品質 、空氣品質指標 (AQI) 、通勤時間 、城市異質性 、雙向固定效果 、台灣 、智慧永續城市 |
| 外文關鍵詞： | Shared mobility, Platform-based transportation, YouBike, WeMo, Urban air quality, AQI, Commute time, City heterogeneity, Two-way fixed effects, Taiwan, Parking search, Smart sustainable city |
| 相關次數： | 點閱：20 下載：0 |
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本研究探討平台型共享移動服務強度與城市發展成果之關聯，研究對象為台灣七個主要直轄市及縣市 — 台北市、新北市、桃園市、台中市、台南市、高雄市及新竹市 — 研究期間為2016年至2024年。研究採用63個城市年度觀測值之平衡追蹤資料，以雙向固定效果模型估計，並以城市層級聚集標準誤進行推論，針對YouBike自行車共享平台之使用強度與WeMo電動機車共享平台之進入效應，就兩大城市成果構面進行4項假設之實證檢驗：城市移動效能（平均通勤時間與職場停車尋位時間）及智慧永續城市發展（以AQI超過100之監測站日比例衡量）。此外，本研究透過捷運乘客量與私人車輛依賴度之中介路徑進行分析，採用YouBike強度與城市交互作用項探討城市間異質性效果，並納入青年人口比例（20至39歲居民占比）作為控制變數。
本研究之核心發現為：YouBike使用強度是城市空氣品質改善之強健且穩健的預測因子。YouBike每人次使用量每增加1%，AQI超標比例平均下降約3.8個百分點（β = −3.829，p < 0.001）。WeMo平台進入則與城市平均通勤時間縮短約1.2分鐘相關，為模式替代機制提供了適度的實證支持。平台強度與職場停車尋位時間之關聯在完整規格下未達統計顯著水準。城市異質性分析顯示，YouBike對空氣品質之改善效果在高雄市（β = −4.31，p<0.01）與台南市（β = −5.03，p<0.05）最為顯著，與南部城市較高污染基準相符；而北部城市則呈現效果減弱甚至反轉之現象，與早期平台部署於低AQI情境下所產生之反向因果關係相符。捷運乘客量與私人車輛登記數之中介路徑分析均未達統計顯著水準，主要源於城市群組數量不足所帶來之統計檢力限制。
本研究提出四項理論貢獻：將平台理論延伸至以機動車輛為主之東亞城市情境；建立共享移動文獻中首個雙平台實證分析框架；正式檢驗直接效果與以車輛持有為中介之環境改善路徑；以及透過對實質性零結果之系統詮釋，建立具理論依據之研究議程。政策意涵方面，建議將YouBike網絡擴展納入環境政策工具評估，並結合國家電動機車推廣政策，形成降低城市車輛排放之雙軌策略。
This study examines how platform-based shared mobility intensity relates to urban outcomes across Taiwan’s seven major municipalities – Taipei City, New Taipei City, Taoyuan, Taichung, Tainan, Kaohsiung, and Hsinchu City – over 2016 to 2024. Using a balanced panel of 63 city-year observations estimated with two-way fixed effects and standard errors clustered at the city level, the study tests 4 hypotheses linking YouBike bike-sharing intensity and WeMo electric scooter-sharing entry to two core urban outcome dimensions: urban mobility performance (average commute time and workplace parking search time) and smart sustainable city development (operationalized as the proportion of monitoring station-days with AQI exceeding 100). The study additionally examines mediation pathways through MRT transit ridership and private vehicle dependence, incorporates city heterogeneity analysis through YouBike × city interaction terms, and controls for young population share (proportion of residents aged 20–39) alongside income, density, and tourism.
The headline finding is that YouBike intensity is a robust predictor of improved urban air quality. A one percent increase in YouBike trips per capita is associated with a reduction of approximately 3.8 percentage points in the AQI exceedance rate (β = −3.829, p < 0.001). WeMo platform entry is associated with approximately 1.2 minutes shorter average commute times, providing moderate evidence for the mode substitution mechanism. Platform intensity does not show a statistically significant association with workplace parking search time in the full specification. City-level heterogeneity analysis reveals that the YouBike air quality effect is strongest in Kaohsiung (β = −4.31, p<0.01) and Tainan (β = −5.03, p<0.05), consistent with higher pollution baselines in southern cities, while northern cities show attenuated or reversed patterns consistent with reverse causality from early platform deployment in already low-AQI contexts. Mediation analyses for MRT transit ridership and private vehicle registrations do not achieve conventional statistical significance, primarily reflecting power limitations from the small number of city clusters.
The study makes four theoretical contributions: extending Platform Theory to a motorcycle-dominated East Asian context, introducing the first dual-platform empirical framework in the shared mobility literature, formally testing direct versus vehicle-ownership-mediated environmental channels, and establishing a theoretically grounded research agenda from substantive null findings. Policy implications include treating YouBike expansion as an environmental investment and combining platform-based shared mobility with national scooter electrification as a dual-track approach to reducing urban vehicular emissions.
Acknowledgements 1
中文摘要 3
Abstract 4
Table of Content 6
List of Tables 10
List of Figures 11
List of Abbreviations 12
Chapter 1: Introduction 14
1.1 Background and Rationale 14
1.2 Market and Industry Trends in the Transportation Sector 17
1.3 Research Questions and Objectives 20
1.4 Conceptual Framework and Research Flow 22
Chapter 2: Conceptual Background and Literature Review 25
2.1 Definition, Typology, and Analytical Focus 25
2.1.1 The Digital Sharing Economy: Definition and Scope 25
2.1.2 Transportation Sharing Platforms: A Market Overview 26
2.1.3 Analytical Focus: Bike-Sharing and Electric Scooter-Sharing 27
2.2 Urban Mobility Performance and Sustainable City Development 29
2.2.1 Urban Mobility Performance: Commute Time and Parking Search Time 29
2.2.2 Sustainable City Development: Air Quality 31
2.3 Literature Review and Research Gaps 31
2.3.1 Platform-Based Bike-Sharing Intensity (IV1: YouBike) 34
2.3.2 Platform-Based Scooter-Sharing Intensity (IV2: WeMo) 35
2.3.3 Mediator 1: Metro-Based Transit Uptake 36
2.3.4 Mediator 2: Private Vehicle Dependence 36
2.3.5 Dependent Variables: Commute Time, Parking Search Time, and Air Quality 37
2.3.6 City-Level Contextual Factors 38
2.3.7 Research Gaps 38
2.4 Theoretical Frameworks 39
2.4.1 Platform Theory 39
2.4.2 Urban Transport Economics and Efficiency Theory 40
2.4.3 Environmental Sustainability and Smart City Theory 40
2.4.4 Integration of the Three Theoretical Frameworks 41
2.5 Conceptual Model and Research Hypotheses 41
2.5.1 Overview of the Conceptual Model 42
2.5.2 H1(H1a &H1b): Platform Intensity and Urban Mobility Performance 42
2.5.3 H2: Platform Intensity and Smart Sustainable City Development 43
2.5.4 H3: Metro Transit Ridership as Mediator 43
2.5.5 H4: Private Vehicle Dependence as Mediator 44
Chapter 3: Research Methodology 45
3.1 Research Design 45
3.2 Variable Operationalization 45
3.2.1 Independent Variables: YouBike and WeMo Platform Intensity 46
3.2.2 Mediating Variables: MRT Transit Uptake and Vehicle Dependence 46
3.2.3 Dependent Variables 47
3.2.4 Control Variables 47
3.2.5 Variable Operationalization Table 48
3.3 Conceptual Framework 51
3.4 Data Sources and Collection 53
3.4.1 YouBike Platform Data (IV1) 53
3.4.2 WeMo Scooter Platform Data (IV2) 53
3.4.3 MRT Transit Uptake Data (M1) 53
3.4.4 Vehicle Registration Data (M2a and M2b) 54
3.4.5 Commute Time Data (DV1a) 54
3.4.6 Parking Search Time Data (DV1b) 54
3.4.7 Air Quality Data (DV2a) 54
3.4.8 Control Variable Data 55
3.4.9 Data Harmonization and Panel Assembly 55
3.5 Sample and Panel Structure 56
3.6 Empirical Strategy 59
3.6.1 Baseline Two-Way Fixed Effects Models 59
3.6.2 Mediation Models (H3 and H4) 60
3.6.3 Platform-Specific Solo Models 61
3.6.4 City Heterogeneity and Interaction Terms 61
3.6.5 Robustness Checks 62
3.6.6 Estimation and Inference 62
3.7 Methodological Limitations 63
3.7.1 Causal Identification 63
3.7.2 Small Panel and Statistical Power 63
3.7.3 WeMo Intensity Measurement 63
3.7.4 Data Coverage Constraints 63
3.7.5 Temporal and Contextual Constraints 64
Chapter 4: Empirical Results 65
4.1 Overview 65
4.2 Descriptive Statistics 65
4.3 Correlation Analysis 67
4.4 Model Specification Test: Hausman Test 68
4.5 Main Regression Results 69
4.5.1 Platform Intensity and Urban Mobility Performance (H1a & H1b) 71
4.5.2 Platform Intensity and Sustainable City Development (H2) 72
4.5.3 Platform-Specific Models 72
4.5.4 City Heterogeneity Analysis: YouBike × City Interaction Terms 74
4.5.6 Regional Subsample Analysis 75
4.6 Mediation Analysis 76
4.6.1 H3: MRT Transit Uptake as Mediator 78
4.6.2 H4: Vehicle Dependence as Mediator 78
4.7 Robustness Checks 78
4.7.1 Commute Time Robustness (R1–R4) 80
4.7.2 Air Quality Robustness: Confirmed AQI Data and City Exclusions 80
4.8 Summary of Hypothesis Tests 80
Chapter 5: Discussion 83
5.1 Overview 83
A. Significant Results 83
5.2 Discussion of H2: Platform Intensity and Sustainable City Development 83
5.2.1 YouBike Air Quality Results 83
5.2.2 City Heterogeneity in the Air Quality Effect 84
5.2.3 WeMo Air Quality Result 85
5.3 Discussion of H1: Platform Intensity and Urban Mobility Performance 85
5.3.1 WeMo Entry and Commute Time 86
5.3.2 YouBike and Commute Time 86
B. Null Findings 88
5.4 Discussion of H3: MRT Transit Uptake as Mediator 88
5.5 Discussion of H4: Vehicle Dependence as Mediator 89
5.6 Unexpected and Noteworthy Findings 90
5.6.1 The Positive MRT Coefficient on AQI Exceedance 90
5.6.2 The Passenger Car Registration Coefficient on AQI 90
5.6.3 City Heterogeneity and Context-Dependence 91
5.7 Contributions 91
5.7.1 The Positive MRT Coefficient on AQI Exceedance 91
5.7.2 The Passenger Car Registration Coefficient on AQI 92
5.7.3 City Heterogeneity and Context-Dependence 93
5.7.4 Substantive Contributions: Null Findings as Structured Research Agenda 93
5.8 Policy Implications 95
5.8.1 YouBike: Environmental Infrastructure, Deployment Strategy, and AQI Monitoring 95
5.8.2 WeMo: Congestion Management, Electrification, and the Dual-Track Strategy 96
5.8.3 Transit Integration as the Key Amplifier 96
5.9 Shared Mobility, Urban Well-Being, and the Commuting Paradox 97
Chapter 6: Conclusion 100
6.1 Summary of the Study 100
6.2 Summary of Key Findings 100
6.3 Theoretical, Methodological, and Policy Contributions 101
6.4 Limitations 102
6.4.1 Small Panel and Statistical Power 102
6.4.2 WeMo Measured as Binary Entry Only 103
6.4.3 Temporal and Data Coverage Constraints 103
6.4.4 AQI Extrapolation (2016–2019) 103
6.4.5 Commute Time Extrapolation (2023–2024) 104
6.5 Directions for Future Research 104
6.6 Closing Remarks 105
References 106
Academic Literature 106
Government Reports and Official Statistics 111
Appendices 114
Appendix A: Full Variable Operationalization and Data Quality Flags 114
Appendix B: YouBike and WeMo Platform Entry Timeline 116
Appendix C: City-Level Mean Values of Key Variables (2016–2024) 118
Appendix D: Hausman Specification Test Output 118
Appendix E: R Script Key Model Specifications 119
Academic Literature
Agarwal, S., Mani, D., & Telang, R. (2023). Impact of ride-hailing services on traffic congestion: Evidence from India. Management Science, 69(5), 2817–2838. https://doi.org/10.1287/mnsc.2022.4433
Albino, V., Berardi, U., & Dangelico, R. M. (2015). Smart cities: Definitions, dimensions, performance, and initiatives. Journal of Urban Technology, 22(1), 3–21. https://doi.org/10.1080/10630732.2014.942092
Arbeláez Vélez, A. M. (2024). Lifecycle environmental impacts of shared electric micro-mobility: A systematic review. Transportation Research Part D: Transport and Environment, 128, 104116. https://doi.org/10.1016/j.trd.2024.104116
Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173–1182. https://doi.org/10.1037/0022-3514.51.6.1173
Belk, R. (2014). You are what you can access: Sharing and collaborative consumption online. Journal of Business Research, 67(8), 1595–1600. https://doi.org/10.1016/j.jbusres.2013.10.001
Bergé, L. (2018). Efficient estimation of maximum likelihood models with multiple fixed-effects: The R package FENmlm. CREA Discussion Papers, 13. Centre for Research in Economic Analysis, University of Luxembourg.
Botsman, R., & Rogers, R. (2010). What's mine is yours: The rise of collaborative consumption. HarperBusiness.
Cameron, A. C., Gelbach, J. B., & Miller, D. L. (2008). Bootstrap-based improvements for inference with clustered errors. The Review of Economics and Statistics, 90(3), 414–427. https://doi.org/10.1162/rest.90.3.414
Cao, Y., Liu, X., & Meng, X. (2023). Can bike-sharing improve urban air quality? Evidence from Chinese cities. Transportation Research Part D: Transport and Environment, 120, 103801. https://doi.org/10.1016/j.trd.2023.103801
Caragliu, A., Del Bo, C., & Nijkamp, P. (2011). Smart cities in Europe. Journal of Urban Technology, 18(2), 65–82. https://doi.org/10.1080/10630732.2011.601117
Cervero, R., Golub, A., & Nee, B. (2007). City CarShare: Longer-term travel demand and car ownership impacts. Transportation Research Record, 1992(1), 70–80. https://doi.org/10.3141/1992-08
Chen, W.-S., Tsai, Y.-S., & Chen, C.-Y. (2018). Comparison of traffic-injury related hospitalisation between bicyclists and motorcyclists in Taiwan. PLoS ONE, 13(1), e0191221. https://doi.org/10.1371/journal.pone.0191221
Cloud, C., Heß, S., & Kasinger, J. (2022). Do shared e-scooter services cause traffic accidents? Evidence from six European countries (Working Paper). Goethe University Frankfurt. arXiv:2209.06870.
Dubey, S., Chapron, M., Ouyang, L., & Minet, L. (2025). Impact of bike-sharing on urban air quality: Evidence from IoT sensor data in Madrid. Environmental Science & Technology, 59(3), 1205–1214. https://doi.org/10.1021/acs.est.4c08321
El-Geneidy, A., Grimsrud, M., Wasfi, R., Tetreault, P., & Surprenant-Legault, J. (2014). New evidence on walking distances to transit stops: Identifying redundancies and gaps using variable cutoffs. Transportation, 41(1), 193–210. https://doi.org/10.1007/s11116-013-9508-z
Gesteira de Souza, R., da Silva Lessa, T., & Pereira, R. H. M. (2025). Platform mobility and urban labor markets: Evidence from ride-hailing entry in Brazilian cities. Journal of Urban Economics, 145, 103698. https://doi.org/10.1016/j.jue.2024.103698
Hausman, J. A. (1978). Specification tests in econometrics. Econometrica, 46(6), 1251–1271. https://doi.org/10.2307/1913827
Helliwell, J. F., Layard, R., Sachs, J. D., De Neve, J.-E., Aknin, L. B., & Wang, S. (Eds.). (2023). World happiness report 2023. Sustainable Development Solutions Network. https://worldhappiness.report/ed/2023/
Huang, M., & Xu, J. (2023). Does bike-sharing reduce urban traffic congestion? Evidence from city-level panel data in China. Transportation Research Part D: Transport and Environment, 124, 103927. https://doi.org/10.1016/j.trd.2023.103927
Huang, G., Wang, H., Zhang, L., & Xu, D. (2026). The impact of ride-hailing regulations on traffic congestion: Evidence from a staggered implementation across 98 Chinese cities. Transportation Research Part A: Policy and Practice, 203, 104765. https://doi.org/10.1016/j.tra.2026.104765
International Organization for Standardization. (2018). ISO 37120:2018 — Sustainable cities and communities: Indicators for city services and quality of life.
International Transport Forum. (2023). Road safety annual report 2023. OECD/ITF. https://doi.org/10.1787/i-tif-road-safety-2023-en
Kenney, M., & Zysman, J. (2016). The rise of the platform economy. Issues in Science and Technology, 32(3), 61–69.
Lang, W., Tan, Z., & Zhang, H. (2025). Bike-sharing and public transit ridership: City-level panel evidence from the United States. Transportation Research Part A: Policy and Practice, 183, 104079. https://doi.org/10.1016/j.tra.2025.104079
Li, L., Lo, H. K., & Xiao, F. (2016). Electric vehicle penetration and air quality improvement in Taiwan cities. Transportation Research Part D: Transport and Environment, 45, 234–248. https://doi.org/10.1016/j.trd.2016.02.012
Li, Z., Wang, J., & Lu, Y. (2021). Bike-sharing and tourism: Evidence from Chicago. Tourism Management, 83, 104202. https://doi.org/10.1016/j.tourman.2020.104202
Liu, C.-F. (2020). 新型態共享移動與地方脈絡的互動：WeMo機車共享的案例研究 [The interaction of new shared mobility with local context: A case study of WeMo scooter-sharing]. 新聞學研究 [Mass Communication Research], 145, 1–44.
MacKerron, G., & Mourato, S. (2009). Life satisfaction and air quality in London. Ecological Economics, 68(5), 1441–1453. https://doi.org/10.1016/j.ecolecon.2008.10.002
McKenzie, G. (2019). Spatiotemporal comparative analysis of scooter-share and bike-share usage patterns in Washington, D.C. Journal of Transport Geography, 78, 19–28. https://doi.org/10.1016/j.jtrangeo.2019.05.007
Médard de Chardon, C., Caruso, G., & Thomas, I. (2017). Bike-share rebalancing strategies, patterns, and purpose. Journal of Transport Geography, 55, 22–39. https://doi.org/10.1016/j.jtrangeo.2016.07.003
Nazif-Muñoz, J. I., Saarinen, N., & Pulgar-Vidal, M. (2021). The effects of bike-sharing on traffic crash rates: Evidence from the United States and Canada. Transportation Research Part D: Transport and Environment, 97, 102942. https://doi.org/10.1016/j.trd.2021.102942
Ortuño-García, J., Flor, N., & Guirao, B. (2023). Shared mobility and private vehicle dependency: Evidence from moped-sharing in Madrid. Transportation Research Part A: Policy and Practice, 176, 103820. https://doi.org/10.1016/j.tra.2023.103820
Parker, G. G., Van Alstyne, M. W., & Choudary, S. P. (2016). Platform revolution: How networked markets are transforming the economy and how to make them work for you. W. W. Norton & Company.
Parry, I. W. H., Walls, M., & Harrington, W. (2007). Automobile externalities and policies. Journal of Economic Literature, 45(2), 373–399. https://doi.org/10.1257/jel.45.2.373
Pigou, A. C. (1920). The economics of welfare. Macmillan.
Qiu, L.-Y., & He, L.-Y. (2018). Bike sharing and the economy, the environment, and health-related externalities. Sustainability, 10(4), 1145. https://doi.org/10.3390/su10041145
Shr, Y.-H., & Chang, H.-H. (2024). Platform efficiency and taxi market competition: Evidence from a digital matching program in Taiwan. Journal of Transport Economics and Policy, 58(1), 45–69.
Small, K. A., & Verhoef, E. T. (2007). The economics of urban transportation. Routledge.
Sobel, M. E. (1982). Asymptotic confidence intervals for indirect effects in structural equation models. Sociological Methodology, 13, 290–312. https://doi.org/10.2307/270723
Stutzer, A., & Frey, B. S. (2008). Stress that doesn't pay: The commuting paradox. Scandinavian Journal of Economics, 110(2), 339–366. https://doi.org/10.1111/j.1467-9442.2008.00542.x
Sundararajan, A. (2016). The sharing economy: The end of employment and the rise of crowd-based capitalism. MIT Press.
Wallace, T. D., & Hussain, A. (1969). The use of error components models in combining cross section with time series data. Econometrica, 37(1), 55–72. https://doi.org/10.2307/1909205
World Health Organization. (2021). WHO global air quality guidelines: Particulate matter (PM2.5 and PM10), ozone, nitrogen dioxide, sulfur dioxide and carbon monoxide. https://www.who.int/publications/i/item/9789240034228
Ye, R., Chen, Z., & Liu, Y. (2024). Shared mobility and urban sustainability: A systematic review of city-level evidence. Sustainable Cities and Society, 103, 105272. https://doi.org/10.1016/j.scs.2024.105272
Yen, B. T. H., Mulley, C., & Yeh, C.-H. (2023). Does YouBike complement or compete with the MRT? Spatial demand-supply analysis of bike-sharing and metro in Taipei. Transport Policy, 133, 100–112. https://doi.org/10.1016/j.tranpol.2023.01.012
Government Reports and Official Statistics
Directorate-General of Budget, Accounting and Statistics. (2016–2024). 家庭收支調查 [Family income and expenditure survey]. Republic of China. https://www.stat.gov.tw
Economic Daily News & Cathay Life Insurance. (2023). 2023縣市幸福指數大調查 [2023 county and city happiness index survey]. https://www.gov.taipei/News_Content.aspx?n=F0DDAF49B89E9413&sms=72544237BBE4C5F6&s=7953CE6CCF5DDBBF
Ministry of Environment. (2015–2024). Taiwan air quality annual statistics. https://airtw.moenv.gov.tw
Ministry of Interior. (2016–2024). Household registration statistics (戶籍人口統計). Republic of China. https://www.moi.gov.tw
Ministry of Transportation and Communications. (2023a). 111年自用小客車使用狀況調查報告 [2022 private car usage survey]. MOTC Statistics Department.
Ministry of Transportation and Communications. (2023b). 111年民眾日常使用運具狀況調查報告 [2022 national personal mobility and urban travel survey]. MOTC Statistics Department.
Ministry of Transportation and Communications. (2021). 109年民眾日常使用運具狀況調查報告 [2020 national personal mobility and urban travel survey]. MOTC Statistics Department.
Ministry of Transportation and Communications. (2017). 105年民眾日常使用運具狀況調查報告 [2016 national personal mobility and urban travel survey]. MOTC Statistics Department.
Ministry of Transportation and Communications, Institute of Transportation. (2021). 運輸部門溫室氣體減量第二階段策略精進研究 [Phase II strategy research on greenhouse gas reduction in the transportation sector]. Institute of Transportation, MOTC.
National Communications Commission. (2023). Communications market report: Broadband and smartphone penetration statistics. Republic of China. https://www.ncc.gov.tw
OECD. (2024). Transport outlook 2024: Sustainable mobility for all. OECD Publishing. https://doi.org/10.1787/oecd-transport-outlook-2024
Taiwan Highway Bureau. (2016–2024). Annual vehicle registration statistics (機動車輛登記數). Ministry of Transportation and Communications. https://data.gov.tw
Tourism Administration, Ministry of Transportation and Communications. (2016–2024). Annual statistics of tourist recreation areas by city (主要觀光遊憩區遊客人次). https://admin.taiwan.net.tw
Transport Data eXchange Platform. (2016–2024). YouBike historical trip and station data. Ministry of Transportation and Communications. https://tdx.transportdata.tw
全文公開日期 2031/08/18