中觀行人模式於大規模疏散模擬之應用

A mesoscopic pedestrian model for large-scale evacuation simulation

魏妤庭(YU-TING WEI)

指導教授 ： 朱致遠

國立臺灣大學/工學院/土木工程學研究所/碩士(2019年)

https://doi.org/10.6342/NTU201901912

摘要

近年來，各界為瞭解自然與人為災害下之行人疏散效率，發展出許多行人疏散之電腦模擬模式。大多數的行人模擬模式皆為針對每位行人移動之微觀模式，適用於小規模問題，當模擬範圍擴大，疏散行人增多時，將花費極長的時間計算，因此本研究建構中觀行人模擬模式以提升大規模模擬之效率。由於災害的範圍可能隨時間遞進而改變，因此本研究也將納入災害範圍隨時間變動的動態環境因子，讓行人能依據更新後的環境狀態重新規劃路徑，期望能更加真實的反映環境與行人二者間相互變化。

本研究提出之中觀模式主要利用「靜態地面場值」、「大型與中型網格」以及「方向性因子」三項特點來提升模擬效率又保持模擬的準確性。案例測試的結果顯示，本模式可大幅減少模擬時間，並提升行人在大規模疏散起訖點間無障礙物下移動路徑的合理性，且更加真實反映障礙物與行人間之相互反饋變化。

關鍵字

中觀行人模擬 ； 大規模行人模擬 ； 靜態地面場 ； 方向性 ； 動態環境因子

並列摘要

In recent years, many computer models for pedestrian simulation have been developed to understand the efficiency of pedestrian evacuation under disasters. Most of these model are microscopic that focus on each pedestrian's movement, which are appropriate for small scale problems. When the problem scope and number of pedestrians increase, the simulation takes extremely long time to calculate. Therefore, this study constructs a mesoscopic pedestrian model to improve the efficiency of large-scale pedestrian evacuation simulation. In addition, hazardous areas in a disaster may change over time. Therefore, this study also considers the dynamic environment, that is, the hazardous areas may vary over time and pedestrians can re-plan the path according to the updated environment. It is expected that pedestrians will be realistically respond to the environment.

The developed mesoscopic model has three major features that improves simulation efficiency and maintains the accuracy of pedestrian simulation, which are static floor field, large and medium grid, and directional factor. Through the above features, this study drastically reduces the simulation time, enhances the accuracy of pedestrians' movement paths, and reflects the interactions between hazardous areas and pedestrians.

並列關鍵字

mesoscopic pedestrian simulation ； large-scale pedestrian simulation ； static floor field ； directional factor ； dynamic environment factor

參考文獻

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