隨著台灣社會邁入高齡化、少子化以及勞動力逐漸短缺的結構性轉變，交通運輸系統的永續發展與營運效能正面臨嚴峻挑戰。自駕巴士作為新一代公共運輸解決方案，兼具提升行車安全、降低人為疏失、強化運輸效率與減緩交通壅塞等潛在優勢，已被多國視為推動都市交通現代化與永續發展的關鍵策略工具。特別是在駕駛人力短缺與高風險環境下，自駕技術可有效補足人力資源不足，並強化營運系統的穩定性與成本效益。儘管自駕巴士技術日益成熟，台灣目前仍處於試驗與示範階段，整體推動進程面臨諸多挑戰，包含技術成熟度不足、法規與政策尚未完備、道路基礎設施需同步升級，以及社會大眾對安全性與信任度的疑慮等，因此極需建構一套系統性評估架構，以釐清關鍵成功因素，做為政策規劃與實務推動的參考依據。
本研究發展一套混合多準則決策模型，建構具整體性與操作性的自駕巴士推動關鍵因素評估架構。有別於傳統僅以權重排序為主的評估方法，本研究不僅探討因素之重要性，亦分析其相互依賴性與推動邏輯。本研究透過系統性文獻回顧與修正式德爾菲法，彙整並篩選出台灣推動自駕巴士之關鍵影響因素；引入Z-numbers處理語意模糊與信心水準，排除專家意見在填答過程中的不確定，並應用Rough-Dombi加權幾何平均法 (R-DWGA) 融合不同背景專家的意見，以保留認知差異與資訊多樣性。本研究亦結合決策實驗室分析法 (DEMATEL) 與詮釋結構模型 (ISM) ，深入剖析各關鍵因素間的因果關係與層級結構，進一步釐清推動順序與邏輯架構。本研究建構一套適用於台灣情境之自駕巴士推動關鍵影響因素評估架構，整體架構包含四大構面與十六項評估準則，涵蓋技術層面、法規政策、社會接受度及基礎設施等多元面向。本研究透過決策實驗室分析法分析結果顯示，「感知安全」、「自動駕駛系統可靠度與穩定性」及「建立風險評估指標」為整體系統中最具關鍵性的因素，對其他準則具有顯著影響力，應作為重點強化的核心項目。詮釋結構模型建構因素間的層級架構，結果顯示「社會接受度」與「使用者體驗」是整體推動的根本成因，具高度引導性，建議透過提升民眾信任與乘車意願作為政策推動的第一步；並且需同時加強「法規制度建置」與「資通安全防護」，代表制度完善與風險管理機制作為支撐技術落地的重要條件；「基礎建設完善程度」與「訂定適合之許可運行環境」能有效反映整體推動的成果，因此我們建議將其作為績效評估與成效追蹤的重要依據，需穩定基礎環境建，並設定明確的運行條件，以確保自駕巴士安全且穩定地運行。綜整本研究之結果，建議我國在推動自駕巴士發展時，應優先透過政策工具推動城市示範計畫，強化公共溝通與使用者體驗改善，以提升社會大眾的接受度並建立民眾對自駕巴士技術的信任與認同。同時，應完善相關法規與保險制度，明確釐清法律責任歸屬與風險分攤機制，建立完整的技術檢測標準與安全監理體系，以強化政策執行的有效性。在資訊安全層面，需制定嚴謹的資料管理規範並導入先進防護技術，提升系統運作的透明度與可信度，確保個人資料隱私安全。此外，亦需強化營運路線的基礎環境條件，包括完善道路標示系統、提升交通號誌連線性、建構車聯網架構與資安防護機制，並建立明確的許可運行規範，確保自駕巴士運行的穩定性與安全性。

As Taiwan enters an era of demographic shifts characterized by an aging population, declining birthrate, and increasing labor shortages, the sustainable development and operational efficiency of the transportation system are facing severe challenges. Autonomous buses, as a next-generation public transportation solution, offer significant advantages such as enhancing traffic safety, reducing human error, improving transport efficiency, and alleviating congestion. Many countries now regard autonomous buses as a key strategic tool for modernizing urban transportation and achieving sustainable mobility. Particularly under labor shortages and high-risk operational conditions, autonomous driving technology can effectively compensate for manpower deficits and improve system stability and cost-efficiency.
Although autonomous bus technology is steadily maturing, Taiwan remains in the pilot and demonstration stage, facing multiple challenges including technological readiness, regulatory and policy gaps, infrastructure limitations, and public concerns over safety and trust. Therefore, it is urgent to establish a systematic evaluation framework to identify critical success factors, which can serve as a reference for policy planning and practical implementation.
This study develops a hybrid Multi-criteria decision-making (MCDM) model to construct a comprehensive and operable evaluation framework for identifying the key factors influencing the promotion of autonomous buses. Unlike traditional evaluation approaches that primarily focus on weight ranking, this study explores not only the importance of individual factors but also their interdependencies and logical promotion sequence. The research first applies a systematic literature review and a modified Delphi method to extract and screen the key influencing factors relevant to Taiwan. It then introduces Z-numbers to handle linguistic ambiguity and confidence levels in expert judgments, eliminating uncertainties in the evaluation process. Furthermore, the Rough-Dombi Weighted Geometric Averaging (R-DWGA) method is employed to aggregate expert opinions from diverse professional backgrounds, preserving cognitive diversity and information integrity.
Additionally, this study integrates the Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Interpretive Structural Modeling (ISM) methods to analyze the causal relationships and hierarchical structure among key factors, clarifying their promotion priorities and logical sequencing.
The resulting evaluation framework consists of four major dimensions and sixteen assessment criteria, encompassing technological, regulatory, societal, and infrastructural aspects. According to the DEMATEL results, “Perceived Safety,” “Reliability and Stability of the Autonomous Driving System,” and “Establishment of Risk Assessment Indicators” are identified as the most influential core factors, warranting prioritized reinforcement. The ISM model further reveals that “Social Acceptance” and “User Experience” are primary driving factors, highlighting the importance of enhancing public trust and willingness to ride. “Regulatory Framework” and “Information Security” are identified as secondary factors, representing essential support conditions for technological implementation. Lastly, “Infrastructure Maturity” and “Establishing Suitable Operational Permitting Conditions” serve as foundational factors, emphasizing the need for comprehensive infrastructure planning and clear operational criteria to ensure safe and stable deployment of autonomous buses.
Based on the findings of this study, it is recommended that Taiwan prioritize enhancing public acceptance when promoting autonomous bus development by utilizing policy instruments to advance urban demonstration projects, strengthening public communication and improving user experience to build public trust and recognition of autonomous bus technology. Simultaneously, relevant regulations and insurance systems should be refined to clearly define legal responsibility attribution and risk-sharing mechanisms, while establishing comprehensive technical testing standards and safety supervision systems to strengthen policy implementation effectiveness. In terms of information security, stringent data management regulations must be formulated and advanced protection technologies introduced to enhance system operational transparency and credibility, ensuring personal data privacy protection. Furthermore, it is essential to strengthen the infrastructure and environmental conditions of operational routes, including improving road signage systems, enhancing traffic signal connectivity, constructing vehicle-to-everything (V2X) networks and cybersecurity protection mechanisms, and establishing clear operational licensing standards to ensure the stability and safety of autonomous bus operations. Through systematic advancement across these multiple dimensions, a solid foundation can be established for the healthy development of autonomous buses in Taiwan.

目錄	i
圖目錄	iii
表目錄	iv
第一章 緒論	1
1.1研究背景與動機	1
1.2研究目的	6
1.3研究範圍	6
1.4研究流程圖	7
1.5研究貢獻	9
第二章 文獻回顧	10
2.1自駕車之概念與研究	10
2.1.1自動駕駛輔助系統發展	10
2.1.2自駕車相關定義	12
2.1.3自駕車與自駕巴士	15
2.2自駕車國內外相關案例	16
2.2.1國外相關案例	16
2.2.2國外相關法規	25
2.2.3國內相關法規	33
2.3推動自駕巴士關鍵影響因素	34
2.4德爾菲法	52
2.4.1德爾菲法起源	52
2.4.2應用修正式德爾菲法之相關研究	52
2.5多準則決策方法	53
2.5.1多準則決策	53
2.5.2 MCDM方法基本概述	55
2.5.3 Z-Numbers	59
2.5.4 Rough-Dombi	60
2.5.5 決策實驗室分析法 (DEMATEL)	61
2.5.6 詮釋結構模型 (ISM)	63
2.6小結	65
第三章 研究方法	67
3.1研究流程	67
3.2修正式德爾菲法	68
3.3 Z-Number	69
3.4 Rough-DWGA	70
3.5 DEMATEL	71
3.6 ISM	73
第四章 實證分析	75
4.1修正式德爾菲之準則篩選	75
4.2 Rough-ZDWGA-DEMATEL-ISM	82
4.3研究小結	101
第五章 結論與建議	104
5.1結論與建議	105
5.2後續研究	114
參考文獻	115
附錄	129
附錄一、修正式德爾菲法問卷	129
附錄二、Rough-ZDWGA-DEMATEL-ISM	137
附錄三、填答範例	147
附錄四、10位專家之評估結果	157
圖1.1研究流程圖	8
圖2.1初擬推動自駕巴士之關鍵影響因素	51
圖2.2 MCDM決策流程	55
圖3.1研究流程圖	67
圖4.1推動自駕巴士關鍵因素評估之層級架構	81
圖4.2影響關係圖	91
圖4.3系統與車輛(D1)影響關係圖	92
圖4.4法規與政策(D2)影響關係圖	93
圖4.5運行環境(D3)影響關係圖	93
圖4.6社會經濟(D4)影響關係圖	93
圖4.7層級結構圖	99
表2.1自駕車與自駕巴士差異	15
表2. 2國際自動駕駛案例彙整	24
表2.3《自動駕駛系統2.0：安全願景》安全性12項規範概要	25
表2.4《自動與聯網駕駛策略方案》規範概要	28
表2.5《自動與聯網駕駛策略方案》規範概要	29
表2.6《自動駕駛制度整備大綱》規範概要	30
表2.7 《UN ECE R157》規範概要	31
表2.8國際自動駕駛安全規範彙整	32
表2.9自駕公車實驗運行安全指引彙整	33
表2.10 Bakioglu & Atahan (2021) 自動駕駛汽車接受度相關因素	35
表2.11 Abdel-Basset et al. (2021) 自動駕駛車輛風險評估	36
表2.12 Deveci et al. (2023) 自動駕駛公車替代方案	40
表2.13 Greifenstein (2024) 影響SAV使用者行為的因素摘要	41
表2.14相似概念之評估準則彙整 (1/2)	44
表2.15推動自駕巴士之關鍵影響因素彙整表	46
表2.16推動自駕巴士之關鍵影響因素彙整表	47
表2.17過往MCDM相關文獻方法彙整	50
表2.18常見MCDM方法缺點比較 (1/3)	56
表4. 1修正式德爾菲專家小組之背景與簡介	75
表4.2修正後之推動自駕巴士之關鍵影響因素準則與定義	76
表4.3修正式德爾菲法之推動自駕巴士關鍵影響因素評估準則之篩選結果	80
表4.4 Rough-ZDWGA-DEMATEL-ISM之專家學者背景	82
表4.5影響評估和信心度評估之語義變數	82
表4.6專家一成對比較之評估結果	83
表4.7專家一的初始影響矩陣	85
表4.8 10位專家對各影響因素之評估結果	85
表4.9初始直接影響矩陣(D)	88
表4.10總影響力和被影響力的粗略模糊數	89
表4.11總影響力和被影響力的清晰值及排序	90
表4.12關鍵評估因素彙整	91
表4.13可達影響矩陣 (Ｈ)	94
表4.14最終可達影響矩陣 (Ｍ)	95
表4.15可達集合R (Ci) 、先行集合A (Ci) 、交集集合C (Ci) 及第一階級	96
表4.16可達集合R (Ci) 、先行集合A (Ci) 、交集集合C (Ci) 及第二階級	97
表4.17可達集合R (Ci) 、先行集合A (Ci) 、交集集合C (Ci) 及第三階級	97
表4.18可達集合R (Ci) 、先行集合A (Ci) 、交集集合C (Ci) 及第四階級	98
表4.19可達集合R (Ci) 、先行集合A (Ci) 、交集集合C (Ci) 及第五階級	98
表4.20可達集合R (Ci) 、先行集合A (Ci) 、交集集合C (Ci) 及第六階級	98
表4.21可達集合R (Ci) 、先行集合A (Ci) 、交集集合C (Ci) 及第七階級	99

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