近年來，極端氣候事件頻繁發生，對交通運輸系統的穩定性構成重大威脅。公路運輸系統為我國關鍵基礎設施重要組成之一，與其他基礎設施具有高度依存性，系統一旦中斷，將可能引發跨部門之連鎖反應。為因應氣候變遷與災害風險升高的挑戰，各國政府已逐步導入「韌性」思維，強調系統於災前預防、災時應變與災後復原之能力。然而，過往國內相關研究多聚焦於單一災害模擬，例如：颱風導致之道路中斷模擬、地震災害下之橋樑損壞評估，或路網可及性分析，於災害發生時替代路線之可行性及服務水準變化等。大多著重於災害當下之影響，較少建立涵蓋災前準備、災時穩固、災後復原等階段，並反映整體系統韌性表現之評估架構，同時，缺乏一套能處理語意模糊性與專家主觀不確定性的研究模型。因此，建立具操作性與解釋力之「公路運輸韌性評估架構」，以因應災害衝擊、指導政策推動與資源配置，有其必要性。本研究結合Z-number與SWARA方法提出Z-SWARA，以量化專家對評估準則之重要性與可靠度，並運用Modified VIKOR方法進行妥協排序，整合多構面評估結果，分析不同地區公路運輸系統之韌性表現與改善優先順序，並以Z-CoCoSo進行驗證。
研究結果指出，「災前準備性」為四大構面中最具影響力者，顯示提升人員訓練、設施維護與災前應變規劃為強化韌性之首要策略。在16項評估準則中，以「人員培訓與災前演練」、「備用的資訊管理系統」及「基礎設施維護」三者權重最高，凸顯制度建置與技術實作兼備之重要性。最後透過 Modified VIKOR 方法進行比較，發現三個直轄市的公路運輸系統在韌性表現上有明顯差異。臺北市整體表現最好，主因其自1967年即升格為直轄市，長期作為行政中樞，累積了完善的交通建設與災害應變經驗。不過因為市區開發密度高，也讓「災後可供工作區域」成為其主要弱點。相較之下，新北市與桃園市較晚升格為直轄市，在改善項目上相當類似，反映出兩地的韌性體系還在發展中。此外，三個都市皆將「基礎設施維護」列為首要改善項目，顯示提升設施穩定性是強化公路韌性的共通關鍵。

In recent years, the increasing frequency of extreme climate events has posed significant threats to the stability of transportation systems. As a critical component of national infrastructure, the highway transportation system in Taiwan is highly interdependent with other infrastructure sectors. Once disrupted, it can trigger cascading effects across multiple departments. To address the challenges posed by climate change and escalating disaster risks, governments worldwide have gradually adopted the concept of "resilience," emphasizing the system’s capabilities in pre-disaster prevention, emergency response during disasters, and post-disaster recovery. However, past domestic studies have primarily focused on simulations of individual disaster events—such as road closures due to typhoons or bridge damage under seismic conditions—or on network accessibility analysis, exploring the feasibility of alternative routes and changes in service levels during disasters. These studies largely concentrated on the immediate impact of disasters and rarely established comprehensive assessment frameworks that encompass pre-disaster preparedness, stability during disasters, and post-disaster recovery stages. Furthermore, few models have been developed to address the issues of semantic ambiguity and expert judgment uncertainty.
Therefore, it is necessary to establish an operational and interpretable “Highway Transportation Resilience Assessment Framework” to respond to disaster impacts, guide policy implementation, and support resource allocation. This study proposes the Z-SWARA method by integrating Z-numbers and the SWARA approach to quantify the importance and credibility of evaluation criteria based on expert opinions. The Modified VIKOR method is then applied to perform compromise ranking, integrating multidimensional assessment results to analyze the resilience performance and improvement priorities of highway systems across different regions. The Z-CoCoSo method is further employed to verify the consistency and robustness of the evaluation results.
The findings reveal that "pre-disaster preparedness" is the most influential of the four major resilience dimensions, highlighting the importance of enhancing personnel training, infrastructure maintenance, and pre-disaster planning as key strategies for strengthening resilience. Among the 16 evaluation criteria, "personnel training and pre-disaster drills," "backup information management systems," and "infrastructure maintenance" received the highest weights, underscoring the significance of combining institutional frameworks with technical measures. The Modified VIKOR analysis shows considerable differences in resilience performance among the three examined municipalities. Taipei City demonstrated the strongest overall performance, attributable to its designation as a special municipality since 1967 and its long-standing role as the administrative center, which has enabled the accumulation of robust transport infrastructure and disaster response experience. However, the city's high urban development density has made "availability of post-disaster working areas" a notable vulnerability. In contrast, New Taipei City and Taoyuan City, having been upgraded to special municipalities more recently, share similar improvement priorities, reflecting the ongoing development of their resilience systems. Notably, all three cities identified "infrastructure maintenance" as the top improvement priority, indicating that enhancing the stability of infrastructure is a common key to improving highway resilience.

目錄
目錄	I
圖目錄	IV
表目錄	V
第一章 緒論	1
1.1 研究背景與研究動機	1
1.2 研究目的	3
1.3 研究範圍與研究限制	4
1.4 研究流程	5
第二章 文獻回顧	7
2.1 關鍵基礎設施	7
2.1.1 關鍵基礎設施發展與定義	7
2.1.2 我國關鍵基礎設施發展	10
2.1.3 關鍵基礎設施相互依存性	12
2.2 我國災害防救體系	13
2.3 韌性的定義	16
2.4 交通運輸韌性定義	18
2.4.1 公路運輸韌性定義	23
2.4.2 公路運輸韌性特徵與評估構面	24
2.5 多準則決策方法	31
2.5.1 多準則決策	31
2.5.2 多準則決策方法應用於韌性評估	36
2.5.3 德爾菲法	36
2.5.4 Z-numbers	40
2.5.5 SWARA方法	41
2.5.6 Modified VIKOR方法	43
2.5.7 CoCoSo方法	45
2.6 小結	48
第三章 研究方法	51
3.1 研究架構	51
3.2 修正式德爾菲法	52
3.3 Z-numbers	53
3.4 SWARA	53
3.5 VIKOR	54
3.6 Modified VIKOR	56
3.7 Z-SWARA	57
3.8 CoCoSo	60
3.9 Z-CoCoSo	61
第四章 實證分析	65
4.1 修正式德爾菲法之準則篩選	65
4.2 Z-SWARA	73
4.3 Modified VIKOR	84
4.4 Z-CoCoSo分析	96
4.5 小結	98
第五章 結論與建議	100
5.1 結論	101
5.2 建議	103
參考文獻	104
附錄	129
附錄A 第一次修正式德爾菲法問卷	129
附錄B第二次修正式德爾菲法問卷	136
附錄C 自然災害下公路運輸韌性績效評估問卷	143
附錄D自然災害下公路運輸韌性績效評估問卷	152

圖目錄
圖 1.1研究流程圖	6
圖 2.1關鍵基礎設施相互依存層級與關係示意圖	13
圖 2.2中央災害防救體系組織架構	14
圖 2.3陸上交通事故災害防救體系示意圖	15
圖 2.4關鍵基礎設施災前至災後階段之韌性流程圖	17
圖 2.5本研究初擬公路運輸韌性評估架構	31
圖 2.6 VIKOR方法之理想解與妥協解示意圖	44
圖 3.1研究流程圖	51
圖 4.1本研究建立之公路運輸韌性評估模型	72

表目錄
表 2 1美國定義關鍵基礎設施或產業	9
表 2 2各國政府對關鍵基礎設施之定義	10
表 2 3 臺灣國家關鍵基礎設施領域分類	11
表 2 4國內外文獻常見之運輸韌性定義	18
表 2 5交通運輸系統韌性的主要特徵	20
表 2 6交通運輸系統韌性能力	23
表 2 7公路系統韌性意涵及其能力描述	24
表 2 8韌性評估構面之建立	24
表 2 9公路運輸韌性評估準則彙整表	27
表 2 10常見MCDM方法比較	34
表 2 11 修正式德爾菲法相關應用	38
表 2 12 Z-numbers相關應用	40
表 2 13 SWARA相關應用	42
表 2 14 VIKOR相關應用	45
表 2 15 CoCoSo相關應用	46
表 3 1 傳統VIKOR方法之限制與對應修正策略	56
表 3 2評估準則重要性程度語意變數	58
表 3 3可靠度語意變數	58
表 3 4 Z-numbers語意變數轉換為模糊數之規則	58
表 3 5備選方案排序所使用的語意變數	62
表 3 6排序備選方案的Z-numbers語意變數轉換為模糊數之規則	62
表 4 1修正式德爾菲問卷之專家學者背景簡介	65
表 4 2彙整文獻回顧之公路韌性準則與定義	66
表 4 3第一輪修正式德爾菲法修改後之公路運輸韌性評估準則與定義	68
表 4 4第二輪修正式德爾菲法之公路運輸韌性評估準則篩選結果	71
表 4 5 Z-SWARA問卷之專家統計資料	73
表 4 6評估準則重要性程度語意變數	74
表 4 7可靠度語意變數	74
表 4 8評估準則重要性程度及可靠度語意變數	74
表 4 9以專家一為例各評估準則之重要性比較係數值(s值)彙整表	75
表 4 10以專家一為例各評估準則之調整係數(k值)彙整表	76
表 4 11以專家一為例各評估準則之累積反比權重(q值)計算表	77
表 4 12以專家一為例各評估準則之最終正規化模糊權重(w值)彙整表	78
表 4 13各評估準則解模糊後權重彙整表	79
表 4 14各評估準則加權後權重彙整表	82
表 4 15專家群績效評估表	84
表 4 16加權正規化計算	85
表 4 17專家群方案群體效益(Sj)與最大個體遺憾(Rj)	86
表 4 18方案綜合效益排名(Qj)計算	86
表 4 19各系統改善準則項目排序	87
表 4 20首要改善類	92
表 4 21次要改善類	95
表 4 22基本改善類	96
表 4 23以專家一為例對各城市在各準則下的語意績效評估	96
表 4 24各城市方案之Z-CoCoSo綜合績效評估結果	97
表 4 25各城市方案之Z-CoCoSo評估指標與最終排序	97

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