應用PLS-SEM MXL模型分析TPASS通勤月票運具選擇行為 — 以桃園市端點為例

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：17

、訪客IP：18.119.248.54

姓名

曾伊璉(Yi-Lian Tseng) 查詢紙本館藏

畢業系所

土木工程學系

論文名稱

應用PLS-SEM MXL模型分析TPASS通勤月票運具選擇行為 — 以桃園市端點為例
(Application of the PLS-SEM-MXL Model on Transportation Mode Choice Behavior of Commuters w/o TPASS Monthly Tickets: A Case Study of Taipei Metropolitan Area with One Trip End in Taoyuan City)

相關論文

★ 圖書館系統通閱移送書籍之車輛途程問題	★ 起迄對旅行時間目標下高速公路匝道儀控之研究
★ 結合限制規劃法與螞蟻演算法求解運動排程問題	★ 共同邊界資料包絡分析法在運輸業之應用-以國內航線之經營效率為例
★ 雙北市公車乘客知覺服務品質、知覺價值、滿意度、行為意向路線與乘客之跨層次中介效果與調節式中介效果	★ Investigating the influential factors of public bicycle system and cyclist heterogeneity
★ A Mixed Integer Programming Formulation for the Three-Dimensional Unit Load Device Packing Problem	★ 高速公路旅行時間預測之研究--函數資料分析之應用
★ Behavior Intention and its Influential Factors for Motorcycle Express Service	★ Inferring transportation modes (bus or vehicle) from mobile phone data using support vector machine and deep neural network.
★ 混合羅吉特模型於運具選擇之應用-以中央大學到桃園高鐵站為例	★ Preprocessing of mobile phone signal data for vehicle mode identification using map-matching technique
★ 含額外限制式動態用路人均衡模型之研究	★ 動態起迄旅次矩陣推估模型之研究
★ 動態號誌時制控制模型求解演算法之研究	★ 不同決策變數下動態用路人均衡路徑選擇模型之研究

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

至系統瀏覽論文 (2025-10-31以後開放)

摘要(中)

本研究旨在分析 TPASS 通勤月票政策對桃園市跨城際通勤者運具選擇行為的影響，並採用 PLS-SEM MXL 整合模型進行分析。該模型結合偏最小平方結構方程模式（PLS-SEM）與混合羅吉特模式（MXL），其中 PLS-SEM 用於量化旅運選擇行為中心理潛在變項之間的因果關係，而 MXL 模型則作為運具選擇行為的分析工具，用以捕捉旅運者在選擇不同交通工具時的隨機效用異質性，透過對已購買及未購買 TPASS 之不同通勤族群進行比較分析。
研究結果顯示，TPASS 月票政策顯著提升了高頻率跨城際通勤者對公共運輸的依賴性，並降低其對票價變動的敏感度，使得服務水準滿意度在通勤選擇決策中的影響力更加顯著。針對已購買 TPASS 的通勤族群，研究發現其對服務便利性及可靠度尤為突出。因此，建議管理者應優先提升桃園機場捷運及國道客運於主要樞紐站點之轉乘便利性與班次可靠度，以提升整體旅運體驗，進一步穩固大眾運具此對高頻通勤族群之長期吸引力。
此外，對於未購買 TPASS 的通勤者群體，價格仍為其運具選擇行為中的主要考量因素，尤其在低收入族群中，對票價優惠的需求更為顯著。未來政策制定者應考量針對該類群體實施更具彈性的票價策略，透過調整票價結構與引入分層式服務層級，以滿足不同經濟條件乘客之多樣化需求。另一方面，研究結果亦指出，擁有私有交通工具的通勤者相對較少選擇公共運輸工具，表明交通政策在誘導私有運具使用者轉移至公共運輸系統時，應考慮採取提高私有運具使用成本之策略，藉此促進跨城際旅運者向公共運輸的轉移，減少私有運具對道路系統的負擔。
研究結果可作為未來交通管理者在優化公共運輸服務及制定跨城際交通政策時之實證參考，並有助於提升桃園市及其周邊地區公共運輸系統之整體使用率與可持續發展。

摘要(英)

This study aims to analyze the impact of the TPASS commuter monthly pass policy on the mode choice behavior of intercity commuters in Taoyuan City, using an integrated PLS-SEM and MXL model. The integrated model combines Partial Least Squares Structural Equation Modeling (PLS-SEM) and the Mixed Logit Model (MXL), where PLS-SEM is employed to quantify the causal relationships among latent variables in travel mode choice behavior, while the MXL model serves as an analytical tool to capture random utility heterogeneity in the mode choice behavior of travelers. A comparative analysis was conducted between commuters who have purchased TPASS and those who have not.
The results indicate that the TPASS policy significantly increases the dependence of high-frequency intercity commuters on public transportation and reduces their sensitivity to fare changes, thereby making service quality a more significant factor in their mode choice decisions. For TPASS users, service convenience and reliability were found to be particularly prominent factors. Hence, it is recommended that transportation authorities prioritize enhancing the transfer convenience and service reliability of Taoyuan Airport MRT and highway buses at major hub stations to improve the overall travel experience and strengthen the long-term attractiveness of public transportation for high-frequency commuters.
Additionally, for non-TPASS commuters, fare remains the primary consideration in their mode choice behavior, especially for low-income groups who show a more significant demand for fare discounts. Future policy makers should consider implementing more flexible fare strategies for this group, adjusting the fare structure, and introducing tiered service levels to meet the diverse needs of passengers with varying economic conditions. Furthermore, the findings reveal that commuters who own private vehicles are less likely to choose public transportation, indicating that transportation policies should consider strategies that increase the cost of private vehicle usage to encourage a modal shift to public transportation, thereby reducing the burden on the road system.
The proposed integrated PLS-SEM and MXL model effectively combines the causal relationships among latent variables with the random utility heterogeneity characteristics, providing a more comprehensive analytical framework for understanding the influence of the TPASS policy on the mode choice behavior of intercity commuters in Taoyuan City. The findings offer empirical evidence for transportation managers to optimize public transportation services and formulate intercity transportation policies, contributing to the overall usage and sustainable development of the public transportation system in Taoyuan City and its surrounding areas.

關鍵字(中)

★ TPASS通勤月票
★ PLS-SEM
★ 運具選擇
★ 混合羅吉特模式

關鍵字(英)

★ TPASS
★ PLS-SEM
★ Mode Choice
★ Mixed Logit Model

論文目次

摘要 i
Abstract ii
致謝 iv
圖目錄 vii
表目錄 viii
第一章　緒論 1
1.1 研究背景與動機 1
1.2 研究目的 2
1.3 研究範圍與對象 3
1.4 研究方法與流程 3
第二章　文獻回顧 4
2.1 計劃行為理論 4
2.2 公共運輸定期票 5
2.3 運具選擇 6
第三章　研究方法 9
3.1 個體選擇模式 9
3.2 混和羅吉特模式 10
3.3. 偏最小平方結構方程模式 11
3.4 PLS-SEM MXL 16
第四章　研究設計 18
4.1 問卷設計 18
4.2 調查計畫 19
4.3 基本統計分析 19
4.3.1 社經特性分析 19
4.3.2 旅運行為分析 22
4.3.3 TPASS 通勤月票政策與使用情況 23
4.4 運具移轉交叉分析 25
第五章　模式校估結果 27
5.1 PLS-SEM模型估算結果與分析 27
5.1.1 模型架構與假說建構 27
5.1.2 PLS-SEM 模型信效度分析 29
5.1.3路徑係數分析 30
5.1.4可觀測異質性分析 34
5.1.5不可觀測異質性分析 36
5.2 考慮心理潛在變項之混合羅吉特模型 37
5.2.1有購買TPASS之PLS-SEM MXL模型 38
5.2.2未購買TPASS之PLS-SEM MXL模型 41
5.3模型收斂結果 43
第六章　結論與建議 44
6.1 結論 44
6.2 建議 46
參考文獻 48
附錄Ａ　問卷 53

參考文獻

[1] 中華民國內政部戶政司全球資訊網，2023，臺北市。
[2] 中華民國行政院，2023，TPASS通勤月票一卡通，臺北市。擷自：https://www.i-pass.com.tw/Page/TPASScard
[3] 桃園市政府，2011，桃園都會區大眾捷運系統綠線暨土地整合發展可行性研究。
[4] 陳惠國，2021，研究分析方法講義，國立中央大學土木工程學系，桃園市。
[5] 陳惠國，2023，運輸規劃-基礎與進階，五南圖書出版公司，臺北市。
[6] 陳敦基，陳其華，劉仲潔，張學孔，2023，交通行動服務 (MaaS) 之發展理念及營運構想，土木水利，45(2), pp. 60-65.
[7] 江曉倩，2015，差別費率策略下高速公路通勤旅次之旅運選擇行為，國立交通大學運輸與物流管理學系，碩士論文，新竹市。
[8] 吳招億，2019，公共運輸定期票購買意願之影響因素分析，國立交通大學管理學院運輸物流學程，碩士論文，新竹市。
[9] 邱裕鈞，溫傑華，陳韋穎，王穆衡，傅強，曾幸敏，林育瑄，2014，旅運者運具選擇行為異質性之研析：混合羅吉特模式之應用，運輸計劃季刊，43(2), pp. 143-171.
[10] 張恩睿，2021，論雙北公共運輸定期票政策之效果與影響，國立政治大學經濟學系，碩士論文，臺北市。
[11] 郭銘倫，2021，大數據分析應用於交通行動服務高雄 MeNGo 月票之顧客分群研究，私立淡江大學運輸管理學系，碩士論文，新北市。
[12] 中華民國行政院主計總處，黃于玲，楊嘉琳，2020，台灣地區通勤通學情形研析。
[13] 楊志文，林佳宏，詹仕偉。2013，探討運具分群與旅客區隔之運具選擇模式，中國土木水利工程學刊, 25(4), pp. 283-292.
[14] 葉文雅，2009，高鐵價格促銷方案對小汽車駕駛人運具選擇行為之影響，國立交通大學交通運輸研究所，碩士論文，新竹市。
[15] 趙韋翔，2009，結合計畫行為理論科技接受模式與慣性行為探討運具轉移行為：以涉入程度為干擾變數，國立成功大學交通管理學系，碩士論文，台南市。
[16] 蔡維倫，2023，探討交通行動服務方案服務體驗，滿意度，忠誠度關係之研究：以 MeNGO 為例，國立成功大學交通管理學系，碩士論文，台南市。
[17] 賴文泰，2011，不同大眾運輸供給地區旅運者之大眾運具使用行為分析，運輸計劃季刊, 40(3), pp. 287-308.
[18] 賴文泰，呂錦隆，2008，應用涉入理論於運具選擇行為之研究，運輸計劃季刊, 37(2), pp. 237-262.
[19] Alhassan, I., Matthews, B., Toner, J., Susilo, Y. (2023). Examining the Effect of Integrated Ticketing on Mode Choice for Interregional Commuting: Studies Among Car Commuters. International Journal of Sustainable Transportation, 17(3), pp. 245-257, doi: 10.1080/15568318.2022.2029634.
[20] Aman, J. J. C., Smith-Colin, J. (2022). Application of Crowdsourced Data to Infer User Satisfaction with Mobility as a Service (MaaS). Transportation Research Interdisciplinary Perspectives, 15, doi: https://doi.org/10.1016/j.trip.2022.100672.
[21] Bahamonde-Birke, F. J., Frowijn, L., van Gils, C., Helmink, R. D., Henkus, S., van der Hoeven, S., Kolkman, O. M., van Onzen, T., Ronteltap, L.,Wehl, D. E. (2023) Am I Willing to Replace My Car with a MaaS Subscription: An Analysis of the Willingness of Dutch Citizens to Adopt MaaS and the Triggers AffectingTheir Choices. Transportation Research Part A: Policy and Practice, 176, doi: https://doi.org/10.1016/j.tra.2023.103816.
[22] Behnood, A., Haghani, M., Golafshani, E. M. (2022). Determinants of Purchase Likelihood for Partially and Fully Automated Vehicles: Insights from Mixed Logit Model with Heterogeneity in Means and Variances. Transportation Research Part A: Policy and Practice, 159, pp. 119-139, doi: 10.1016/j.tra.2022.03.017.
[23] Chen, C.-F., Fu, C., Chen, Y.-C. (2023). Exploring Tourist Preference for Mobility-as-a-Service (MaaS)–A Latent Class Choice Approach. Transportation Research Part A: Policy and Practice, 174, doi: https://doi.org/10.1016/j.tra.2023.103750.
[24] Chen, H.-K., Yan, D.-W. (2019). Interrelationships Between Influential Factors and Behavioral Intention with Regard to Autonomous Vehicles. International Journal of Sustainable Transportation, 13(7), pp. 511-527, doi: 10.1080/15568318.2018.1488021.
[25] Chiou, Y.-C. (2014). Analysis of Heterogeneity in Commuter Vehicle Choice Behavior: Application of Mixed Logit Model. Transportation Planning Quarterly, doi:https://doi.org/10.1016/j.tra.2023.103610.
[26] Cui, H., Li, M., Zhu, M., Ma, X. (2023). Investigating the Impacts of Urban–rural Bus Service Quality on Rural Residents’ Travel hoices Using an SEM–MNL Integration Model. Sustainability, 15(15), doi: https://doi.org/10.3390/su151511950.
[27] Dash, G., Paul, J. (2021). CB-SEM v.s. PLS-SEM Methods for Research in Social Sciences and Technology Forecasting. Technological Forecasting and Social Change, 173, doi: 10.1016/j.techfore.2021.121092.
[28] Han, Y., Li, W., Wei, S., Zhang, T. , 2018. Research on Passenger’s Travel Mode Choice Behavior Waiting at Bus Station Based on SEM-logit Integration Model. Sustainability, 10(6), doi: https://doi.org/10.3390/su10061996.
[29] Jia, T.-W. (2020). A Study on Transportation Mobility Services (MaaS) on the Transportation Choice Behavior of Commuters—Taking the Shuangbei Region as an Example, National Central University, Taoyuan City, Taiwan.
[30] Kriswardhana, W., Esztergar-Kiss, D. (2023). Exploring the Aspects of MaaS Adoption Based on College Students’ Preferences. Transport Policy, 136, pp. 113-125, doi: https://doi.org/10.1016/j.tranpol.2023.03.018.
[31] Mandhani, J., Nayak, J. K., Parida, M. (2020). Interrelationships Among Service Quality Factors of Metro Rail Transit System: An Integrated Bayesian Networks and PLS-SEM Approach. TransportationResearch part A: Policy and Practice, 140, pp. 320-336, doi: 10.1016/j.tra.2020.08.014.
[32] Meena, S., Patil, G. R., Mondal, A. (2019). Understanding Mode Choice Decisions for Shopping Mall Trips in Metro Cities of Developing Countries. Transportation Research Part F: Traffic Psychology and Behaviour, 64, pp. 133-146, doi: 10.1016/j.trf.2019.05.002
[33] Fan, M., Qi, J., Zheng, X., Shang, H., Kan, J. (2024). A Study on Commuters’ Public Transportation Mode Choice Behavior in River Valley Type Cities Considering Terrain Spatial Perception: Evidence From Lanzhou, China. Scientific Reports, 14(1).doi: 10.1038/s41598-024-64062-z.
[34] Paschal Sheeran. (2011). Intention—Behavior Relations: A Conceptual and Empirical Review. European Review of Social Psychology, 12, doi: https://doi.org/10.1080/14792772143000003.
[35] Paulssen, M., Temme, D., Vij, A., Walker, J. L. (2014). Values, Attitudes and Travel Behavior: A Hierarchical Latent Variable Mixed Logit Model of Travel Mode Choice. Transportation, 41, pp. 873-888, doi: 10.1007/s11116-013-9504-3.
[36] Saeidi, S. (2024). Factors affecting Public Transportation Use During Pandemic: An Integrated Approach of Technology Acceptance Model and Theory of Planned Behavior. Tehni?ki glasnik, 18(3), doi:https://doi.org/10.31803/tg-20230601145322.
[37] Shah, B. A., Zala, L., Desai, N. A. (2023). An Integrated Estimation Approach to Incorporate Latent Variables Through SEM into Discrete Mode Choice Models to Analyze Mode Choice Attitude of a Rider. Transportation Research Interdisciplinary Perspectives, 19, doi: 10.1016/j.trip.2023.100819.
[38] Si, H., Shi, J., Tang, D., Wu, G., J, Lan. (2020). Understanding Intention and Behavior Toward Sustainable Usage of bike Sharing by Extending the Theory of Planned Behavior. Resources, Conservation and Recycling, 152, pp.5-7, doi: https://doi.org/10.1016/j.resconrec.2019.104513.
[39] Si, Y., Guan, H., Cui, Y. (2019). Research on the Choice Behavior of Taxis and Express Srvices Based on the SEM-logit Model. Sustainability, 11(10), pp.3-8, doi: https://doi.org/10.3390/su11102974.
[40] Tung, P.L. (2013). Vehicle Selection Behavior Considering Observable and Potential Variables - Taking Medium and Long-distance Intercity Transport in the West as an Example, National Central University, Taoyuan City, Taiwan.
[41] Wang, C.Y. (2018). Using the AHP Method to Explore the Factors Influencing Passengers′ Choice of Public Transportation - Taking the Taoyuan International Airport-Taipei urban Area as an Example, National Central University, Taoyuan City, Taiwan.
[42] Yan, D.-W. (2018). Interrelationships Between Influential Factors and Behavioral Intention with Regard to Autonomous Vehicles. International Journal of Sustainable Transportation, 13(7), doi:https://doi.org/10.1080/15568318.2018.1488021.
[43] Yanez, M. F., Raveau, S., Ortuzar, J. d. D. (2010). Inclusion of Latent Variables in Mixed Logit Models: Modelling and Forecasting. Transportation Research Part A: Policy and Practice, 44(9), pp. 744-753, doi:https://doi.org/10.1016/j.tra.2010.07.007.
[44] Ye, X., Sui, X., Wang, T., Yan, X., Chen, J. (2022). Research on Parking Choice Behavior of Shared Autonomous Vehicle Services by Measuring Users’ Intention of Usage. Transportation Research Part F: Traffic Psychology and Behaviour, 88, pp. 81-98, doi:10.1016/j.trf.2022.05.012.

指導教授

陳惠國(Huey-Kuo Chen)

審核日期

2024-10-15

推文