近年來自然災害頻發，產生之衝擊影響著交通運輸系統的穩定性，交通運輸設施係為我國國家關鍵基礎設施之一，與其他關鍵基礎設施存在相互依存性，在國家運作中扮演重要角色，因此提升交通運輸系統抗災能力已成為重要研究課題。為應對氣候變遷帶來之衝擊，各國嘗試將韌性思維導入防災政策中，相較於過往風險評估之觀點，以韌性思維探討將更為嚴謹。然過往研究軌道運輸系統安全多以風險評估、脆弱性之角度探討，較少基於韌性概念進行研究，且我國目前仍未有基於交通韌性特徵的軌道運輸韌性評估模型，因此建立一套完整的軌道運輸韌性評估架構實有必要。
傳統評估架構多以多評準決策方法配合績效評估進行分析，往往受限於數據的本質、決策者的主觀判斷或外部環境的變化導致研究數據存在不確定性和模糊性，因此本研究應用模糊推論，除了能夠完善地處理決策過程中數據造成的不確定性和模糊性之外，模糊推論系統中的隸屬函數和模糊規則，亦增加了決策過程的靈活性和適應性，使研究在實際應用中更加實用且更適合處理多變環境的決策問題。為了實際應用此評估架構，本研究以國內軌道運輸中，五座大眾捷運系統系統進行實證分析，邀請專家學者對系統軌道運輸韌性表現填答，再以SWARA搭配modified VIKOR進行績效評估，依據評估結果了解軌道運輸兩系統距離理想解之差距，並提出各系統需改善之項目。
本研究建構軌道運輸系統韌性評估架構，包含三大構面（災前準備性、災時穩固性及災後備援性）和九項評估準則，根據模糊推論結果顯示，災後備援性為最具影響軌道運輸系統韌性之構面，代表充沛的備援機具、零組件以及迅速的支援修復的能力是提升軌道運輸系統韌性的基礎，接著，透過逐步權重比率分析法計算準則權重，可得各構面下權重最高的準則分別為：「基礎設施可靠性及妥善率」、「基礎設施防護及備用的電訊系統」、「維生設備及搶修機具與零組件」，最後，透過修正式折衷排序法判斷臺灣五座大眾捷運系統須優先改善之準則項目，發現不論哪座系統皆應優先改善「基礎設施防護」及「備用的電訊系統、維生設備及搶修機具與零組件」兩準則項目，顯示有效的基礎設施防護可以減少破壞性事件對系統的影響，並可在災害發生時減少所受之損害，以及軌道運輸系統應先以準備各類軌道運輸設施相關零組件及子系統等設備機具為主，以供災時若主要系統或部分系統失效時，有各類備援系統或零組件供軌道運輸系統盡速恢復或維持基本服務水準。

In recent years, the frequent occurrence of natural disasters has impacted the stability of transportation systems. Transportation infrastructure is one of the nation's critical infrastructures and is interdependent with other key infrastructures, playing a crucial role in national operations. Therefore, enhancing the disaster resilience of transportation systems has become an important research topic. To address the impacts of climate change, countries are attempting to incorporate resilience thinking into disaster prevention policies. Compared to past risk assessment perspectives, resilience thinking provides a more rigorous exploration. However, past research has often focused on risk assessment and vulnerability, with limited studies based on resilience concepts. Furthermore, there is currently no rail transportation resilience assessment model based on transportation resilience in our country. Therefore, it is essential to establish a comprehensive rail transportation resilience assessment framework.
Traditional assessment frameworks often employ multi-criteria decision-making methods in conjunction with performance evaluation, but they are often limited by the nature of the data, decision-makers' subjective judgments, or changes in the external environment, leading to uncertainty and fuzziness in research data. To address these limitations, this study applies fuzzy Inference. Fuzzy Inference can effectively handle the uncertainty and fuzziness caused by data in the decision-making process. Furthermore, the membership functions and fuzzy rules in fuzzy Inference systems enhance the flexibility and adaptability of the decision-making process, making the research more practical and suitable for handling decision problems in dynamic environments. To validate this assessment framework, this study conducts an empirical analysis of five metro systems in Taiwan. Experts and scholars were invited to evaluate the resilience performance of these systems. Subsequently, the Stepwise Weight Assessment Ratio Analysis (SWARA) method, combined with modified VIKOR, was used for performance evaluation. Based on the evaluation results, the gaps between each rail transportation system and the ideal solution were identified, and improvement items for each system were proposed.
This study constructs a resilience assessment framework for rail transportation systems, encompassing three dimensions (pre-disaster preparedness, during-disaster robustness, and Post-Disaster Redundancy) and nine evaluation criteria. Based on Fuzzy Inference analysis, post-disaster Redundancy is identified as the most influential dimension affecting the resilience of rail transportation systems, suggesting that sufficient backup equipment, components, and rapid support and repair capabilities are fundamental to enhancing the resilience of rail transportation systems.
Subsequently, the SWARA method was employed to calculate the criterion weights, revealing that the highest-weighted criteria within each dimension are: "Reliability and Availability of Infrastructure." and "Infrastructure Protection" and "Backup Telecommunication Systems, Equipment, Repair Equipment, And Spare Parts.” 
Finally, the modified VIKOR method was utilized to determine the priority improvement items for five metro systems in Taiwan. The results show that all systems should prioritize improvements in "Infrastructure Protection" and "Backup Telecommunication Systems, Equipment, Repair Equipment, And Spare Parts." These findings highlight the importance of effective infrastructure protection in mitigating the impact of disruptive events and reducing damage during disasters. Additionally, rail transportation systems should prioritize the preparation of various rail transportation facilities, components, and subsystems to ensure the availability of backup systems or components for rapid restoration or maintenance of basic service levels in the event of system failures.

目錄
目錄	I
圖目錄	III
表目錄	IV
第一章 緒論	1
1.1研究背景與研究動機	1
1.2研究目的	5
1.3研究範圍與研究限制	6
1.4研究流程	7
1.5研究貢獻	8
第二章 文獻回顧	9
2.1關鍵基礎設施	9
2.2交通運輸韌性	16
2.3軌道運輸型態之韌性	25
2.4德爾菲法	35
2.5模糊推論	37
2.6 逐步權重比率分析法(SWARA)	40
2.7 modified VIKOR	40
2.8小結	43
第三章 研究方法	45
3.1研究架構	45
3.2修正式德爾菲法分析程序	46
3.3 模糊推論	47
3.4逐步權重比率分析法(SWARA)	50
3.5 modified VIKOR分析程序	52
第四章 實證分析	54
4.1修正式德爾菲法之準則篩選	54
4.2模糊推論分析	60
4.3逐步權重比率分析法(SWARA)分析	69
4.4 modified VIKOR分析	71
第五章 結論與建議	77
5.1結論	78
5.2建議	82
參考文獻	85
附錄	103
附錄A修正式德爾菲法問卷	103
附錄B第二次修正式德爾菲法問卷	109
附錄C自然災害下軌道韌性評估問卷	115
附錄D自然災害下軌道運輸韌性評估準則重要程度問卷	131
附錄E 自然災害下軌道運輸韌性績效評估問卷	137

圖目錄
圖1.1研究流程圖	7
圖2.1關鍵基礎設施相互依存層級與關係示意圖	16
圖2.2本研究初擬軌道韌性評估架構	34
圖3.1研究流程圖	45
圖3.2模糊推論流程	48
圖4.1軌道運輸韌性評估構面、準則之層級架構	59
圖4.2模糊推論系統轉換過程	61
圖4.3模糊規則三維曲面圖（依軌道運輸韌性構面區分）	64
圖4.4模糊規則三維曲面圖（依災前準備性區分）	64
圖4.5模糊規則三維曲面圖（依災時穩固性區分）	65
圖4.6模糊規則三維曲面圖（依災後備援性區分）	65
圖4.7輸出變量（以災前準備性為例）	66

表目錄
表2-1美國定義關鍵基礎設施或產業	10
表2-2國外文獻對關鍵基礎設施之定義	11
表2-3我國現行關鍵基礎設施領域分類	13
表2-4國內文獻對關鍵基礎設施之定義	14
表2-5國內文獻常見之韌性定義	17
表2-6 運輸系統韌性定義整理	19
表2-7交通運輸系統韌性的關鍵能力	21
表2-8 韌性交通運輸系統的主要特徵	23
表2-9韌性評估構面之建立	25
表2-10鐵道運輸系統中常見之韌性定義	26
表2-11軌道運輸韌性構面初擬	28
表2-12軌道系統韌性評估準則彙整表	30
表2-13軌道韌性評估準則整合結果	31
表2-14模糊推論相關應用文獻	39
表2-15 modified VIKOR相關應用文獻	42
表4-1修正式德爾菲問卷填答之專家學者背景簡介	54
表4-2文獻回顧彙整之軌道韌性準則與定義	55
表4-3第一輪修正式德爾菲法修改後之軌道運輸韌性評估準則及定義	57
表4-4第二輪修正式德爾菲法之軌道運輸韌性評估準則篩選結果	59
表4-5模糊推論問卷之專家學者統計資料	60
表4-6輸入語意變量表	60
表4-7輸出語意變量表	60
表4-8軌道運輸韌性構面之模糊規則	62
表4-8災前準備性構面下評估準則之模糊規則	62
表4-9災時穩固性構面下評估準則之模糊規則	62
表4-10災後備援性構面下評估準則之模糊規則	62
表4-11本研究大眾捷運系統編碼與其描述	66
表4-12績效評估問卷之專家學者統計資料	67
表4-13專家評估值正規化之整合評估表	68
表4-14模糊推論韌性評估結果	68
表4-15 SWARA問卷之專家學者統計資料	69
表4-16各構面下評估準則平均重要度排名	69
表4-17 SWARA分析結果	70
表4-18專家群績效評估表	71
表4-19 w_j  ((f_j^*-f_ij ))⁄((f_j^*-f_j^- ) )計算(1/2)	72
表4-20 w_j  ((f_j^*-f_ij ))⁄((f_j^*-f_j^- ) )計算(2/2)	72
表4-21專家群方案群體效益（Si）與最大個體遺憾（Ri）	72
表4-22方案綜合效益（Qi）計算	73
表4-23各系統改善準則項目排序	74
表4-24首要改善類	75
表4-25次要改善類	76
表4-26基本改善類	76
表5-1臺灣軌道運輸系統韌性之優先改善項目	80

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