由於氣候變遷造成許多災害，在過去幾十年許多研究致力於疏散之規劃。疏散為一常見之災害有效緊急應變措施，但過去大部分研究中並未考慮到人們在疏散時的行為特性會對疏散時所做的決策造成影響，尤其在疏散時恐慌的情緒會造成疏散者做出非理性的決定，因此本論文整合細胞自動機模型與細胞傳輸模型以完整表達疏散之行為特性與疏散之交通動態過程。此外，本論文以整合之模型為基礎亦針對常見之疏散規劃策略「車道調撥」做進一步之研究。本論文為一非線性混合整數規劃問題，若以傳統之數學規劃演算法求解會有實行上之難度，故本研究結合禁忌搜尋法與蟻群演算法作為求解本問題之最佳化啟發式演算法。本研究之求解架構應用實際大規模疏散網路於數值分析以探討其適用性與表現，研究證明為使疏散規劃更加準確以及更具有效率應考慮疏散者之行為。

Evacuation is widely used as an effective emergency response and mitigation strategy, and a well-defined and manageable plan is a prerequisite for the successful implementation of a large-scale urban or regional evacuation. While there is a considerable amount of research on evacuation, most of the existing studies do not consider the behavior of evacuees which could affect the evacuation process, despite the fact that people may panic and make irrational decisions in such situations. To incorporate this critical feature in evacuation planning, we integrate the cellular automata (CA) model with the cell transmission model (CTM) to better capture evacuees’ behavior and traffic dynamics during the evacuation process. Further, we investigate one of the common evacuation planning strategies, lane reversal (or contraflow), based on the integrated CA and CTM model. The resulting problem is a non-linear mixed integer program, which faces significant challenges when solved with conventional mathematical programming algorithms. Therefore, a tabu search solution approach that embeds an ant colony optimization heuristic is developed to address this issue. The proposed solution framework is empirically applied to a real network to investigate the applicability and performance of the proposed methodology in large-scale evacuation networks. The results show that an evacuation plan should take evacuees’ behavior into account, so that it can be more accurate and effective.

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