近年來受到全球氣候變遷的影響，溫室效應促使氣候急遽變化，使得災害發生頻率和強度逐年上升。天然災害有著不可預測性且造成的破壞程度難以估計，然而災害發生後會因時間性及地域性來衍生出其他災難浩劫。隨著政府著手開放災害資料，於過往的災害歷史資料裡尋找經常發生災害的關聯序列，將可以幫助民眾對於二次災害的發生提前做準備。傳統上對於避難路徑的設計，採用災害潛勢地圖和使用問卷調查當地人的用路習慣來規劃事前最短逃生路徑，但是用事前規劃出的最短逃生路徑並未考量到災難發生後引發的道路損毀和二次災害的發生可能造成的損傷，而且傳統上對於單一災難目標規劃路徑已經不敷使用。
為了於災害發生時提升疏散的速度，且迅速地提供民眾前往安全的逃生避難路徑，和有效降低二次災害所造成的傷亡。本論文針對複合性災害進行分析，由於災害存在時序性的特徵，運用PrefixSpan演算法，以更具有系統性的方法尋找頻繁災害序列。本論文以A Star演算法做為本研究路徑演算法之求解模式，並與路網資訊和災害頻繁序列做結合，透過本研究所設計的時序複合性災害資料之避難路徑規劃系統可以預先避開疑似危險路段，規劃出安全的逃生避難路徑，協助使用者爭取更多避難和救災的黃金時間。

Climate change is already affecting the planet in recent years. The greenhouse effect triggers such drastic climate changes which causes the frequency and intensity of disasters increasingly year by year. Although natural disasters are unpredictable and could result in massive damages, it will derive other multiple disasters depend on its time relation and regional characteristic. As the government gradually promote open disaster data, discovering frequent disaster-related sequences from disasters historical data can assist victims to get ready for secondary disasters beforehand. The traditional evacuation path uses disaster potential maps and survey the evacuation habits of the locals to plan shortest evacuation path before disasters happened. However, the pre-planned shortest path considers neither the consequences after the disasters which could possibly destroy the road, nor the damages caused by secondary disasters. Besides, single disaster-based processing is not enough for composited disaster events.
In order to accelerate the evacuation speed, provide a safer evacuation path to victims, and against the damages caused by secondary disasters when disasters occur. This research proposes to adopt a PrefixSpan method with composited disaster data and systematically search frequent disaster-related sequences. Moreover, the path finding is based on A-Star method which can effectively find a shortest path. This research also combines path finding with GIS information and frequent disaster-related sequences. In the end, this research can previously avoid the potential dangerous area and provide a safer route to user for suggestion, so that they can get more rescue and evacuation time.

[1] Nicole Laframboise and Boileau Loko, “Natural Disasters: Mitigating Impact, Managing Risks”, IMF Working Paper, Oct. 2012.
[2] 災害防救法, http://law.moj.gov.tw/LawClass/LawAll.aspx?PCode=D0120014
[3] NCDR災害示警公開資料平台, https://alerts.ncdr.nat.gov.tw/
[4] 李威儀,陳志勇,簡妤珊,許慈君, “台北市內湖地區都市防災空間系統規劃”, 內政部建築研究所, 2007
[5] 蔡柏全, “都市災害防救管理體系及避難圈域適宜規模之探究－以嘉義市為例”, 國立成功大學, 2002
[6] 包昇平, “都市防災避難據點適宜性評估之研究-以嘉義市為例”, 國立成功大學, 2004
[7] 黃定國, “都市與建築防災整體研究架構之規劃”, 內政部建築研究所, 1995
[8] 三船康道, “地域、地區防災計畫”, 1995
[9] 蕭素月, “地震災害避難疏散最適路徑之研究”, 國立台灣大學, 2003
[10] 周孟宏, “動態性避難道路路線規劃”, 國立台灣師範大學, 2005
[11] 張明輝, “地震災害發生時避難路徑選擇與其效能分析之研究”, 國立台灣科技大學, 2006
[12] 張恆維, “以多屬性決策選擇都市最適避難路徑之研究”, 逢甲大學, 2007
[13] 楊毓哲, “都市避難據點容納量及人員避難路徑之研究”, 朝陽科技大學, 2009
[14] 劉書綺, “都市地區地震災害避難據點與避難路徑擇定之研究”, 國立交通大學, 2011
[15] 蘇益平, “都會地區水災災害最適避難路徑之研究”, 東南科技大學, 2012
[16] 游崇韋, “極端氣候下避難疏散路徑規劃”, 國立台灣科技大學, 2012
[17] 賴懋頡, “應用防災地圖於避難路徑規劃之研究”, 國立中興大學, 2014
[18] 林皇慧, “建置三維數位智慧城市之防災避難功能與路線規劃”, 國立中興大學, 2014
[19] R. Srikant and R. Agrawal, “Fast algorithms for mining association rules”, Proceedings of the 20th International Conference on Very Large Data Bases, VLDB, pages 487-499, Santiago, Chile, Sept. 1994.
[20] Jian Pei and Jiawei Han, “PrefixSpan: Mining Sequential Patterns Efficiently by Prefix-Projected Pattern Growth”, Proc. 2001 Int’l Conf. Data Eng, Apr. 2001
[21] R. Srikant and R. Agrawal, “Mining sequential patterns: Generalizations and performance improvements”, In Proc. 5th Int. Conf. Extending Database Technology, Mar. 1996.
[22] E. W. Dijkstra, “A note on two problems in connexion with graphs”, Numerische Mathematik, 1959
[23] P. Hart, N. Nilsson and B. Raphael, “A Formal Basis for the Heuristic Determination of Minimum Cost Paths”, IEEE Transactions on Systems Science and Cybernetics SSC4, 1968
[24] Amit’s Thoughts on Pathfinding, http://theory.stanford.edu/~amitp/GameProgramming/AStarComparison.html
[25] 共通示警協議說明, https://alerts.ncdr.nat.gov.tw/Document/CAP%E8%B3%87%E6%96%99%E8%AA%AA%E6%98%8E.pdf
[26] 台灣共通示警協議, https://alerts.ncdr.nat.gov.tw/Document/CAP%20Taiwan%20Profile.pdf
[27] Open Street Map, https://www.openstreetmap.org/
[28] Open Street Map Key: highway wiki, http://wiki.openstreetmap.org/wiki/Key:highway
[29] Great-circle distance, https://en.wikipedia.org/wiki/Great-circle_distance
[30] Haversine formula, https://en.wikipedia.org/wiki/Haversine_formula
[31] Darrin Ward, https://www.darrinward.com/lat-long/
[32] 戶川喜久二, “群集流觀測基避難設施研究”, 1955
[33] Yi-zhou Chen, Shi-fei Shen, Tao Chen, Rui Yang, “Path Optimization Study for Vehicles Evacuation based on Dijkstra Algorithm”, 2014