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DC 欄位 | 值 | 語言 |
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dc.contributor.advisor | 朱致遠(James Chu) | |
dc.contributor.author | YU-TING WEI | en |
dc.contributor.author | 魏妤庭 | zh_TW |
dc.date.accessioned | 2021-06-17T09:08:27Z | - |
dc.date.available | 2025-02-13 | |
dc.date.copyright | 2020-02-13 | |
dc.date.issued | 2019 | |
dc.date.submitted | 2019-11-08 | |
dc.identifier.citation | Blue, V. J., & Adler, J. L. (1998). Emergent Fundamental Pedestrian Flows from Cellular Automata Microsimulation. Transportation Research Record: Journal of the Transportation Research Board, 1644(1), 29-36.
Blue, V. J., & Adler, J. L. (2001). Cellular automata microsimulation for modeling bi-directional pedestrian walkways. Transportation Research Part B: Methodological, 35(3), 293-312. Borgers, A., & Timmermans, H. (2010). A Model of Pedestrian Route Choice and Demand for Retail Facilities within Inner-City Shopping Areas. Geographical Analysis, 18(2), 115-128. Borgers, A., & Timmermans, H. J. P. (1986). City Center Entry Points, Store Location Patterns and Pedestrian Route Choice Behavior - a Microlevel Simulation-Model. Socio-Economic Planning Sciences, 20(1), 25-31. Burstedde, C., Klauck, K., Schadschneider, A., & Zittartz, J. (2001). Simulation of pedestrian dynamics using a two-dimensional cellular automaton. Physica A: Statistical Mechanics and its Applications, 295(3-4), 507-525. Cramer, C. E., & Gelenbe, E. (2000). Video quality and traffic QoS in learning-based subsampled and receiver-interpolated video sequences. IEEE Journal on Selected Areas in Communications, 18(2), 150-167. Duives, D. C., Daamen, W., & Hoogendoorn, S. P. (2013). State-of-the-art crowd motion simulation models. Transportation Research Part C: Emerging Technologies, 37, 193-209. Helbing, D., Buzna, L., Johansson, A., & Werner, T. (2005). Self-Organized Pedestrian Crowd Dynamics: Experiments, Simulations, and Design Solutions. Transportation Science, 39(1), 1-24. Hoogendoorn, S. P., P. H. L. Bovy, W. Daamen. (2002). Microscopic pedestrian wayfinding and dynamics modelling. M. Schreckenberg, S. D. Sharma, eds. Pedestrian and Evacuation Dynamics. Springer, Berlin, Germany, 123–154. Hoogendoorn, S.P., Daamen, W., (2009). A novel calibration approach of microscopic pedestrian models. In: Pedestrian Behavior: Models, Data Collection and Applications, pp. 195. Jiang, Y.-q., Zhang, P., Wong, S. C., & Liu, R.-x. (2010). A higher-order macroscopic model for pedestrian flows. Physica A: Statistical Mechanics and its Applications, 389(21), 4623-4635. Liu, J., Song, X., Sun, J., & Xie, Z. (2018). Global A* for Pedestrian Room Evacuation Simulation. Paper presented at the 2018 IEEE International Conference on Big Data and Smart Computing (BigComp). Seyfried, A., Steffen, B., Klingsch, W., & Boltes, M. (2005). The fundamental diagram of pedestrian movement revisited. Journal of Statistical Mechanics: Theory and Experiment, 2005(10), P10002-P10002. Tordeux, A., Lämmel, G., Hänseler, F. S., & Steffen, B. (2018). A mesoscopic model for large-scale simulation of pedestrian dynamics. Transportation Research Part C: Emerging Technologies, 93, 128-147. Twarogowska, M., Goatin, P., & Duvigneau, R. (2014). Macroscopic modeling and simulations of room evacuation. Applied Mathematical Modelling, 38(24), 5781-5795. Weidmann, U., (1992). Transporttechnik der Fußgänger, Schriftenreihe des IVT Nr. 90. Institute for Transport Planning and Systems, ETH Zürich, Switzerland. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/74832 | - |
dc.description.abstract | 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。 | zh_TW |
dc.description.abstract | 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. | en |
dc.description.provenance | Made available in DSpace on 2021-06-17T09:08:27Z (GMT). No. of bitstreams: 1 ntu-108-R06521509-1.pdf: 4718170 bytes, checksum: 7b12f55030687cd8da965ebd0fd3488c (MD5) Previous issue date: 2019 | en |
dc.description.tableofcontents | 口試委員審定書 i
致謝 ii 中文摘要 iii ABSTRACT iv 目錄 vi 圖目錄 viii 表目錄 xi 第一章、 緒論 1 1.1 研究動機 1 1.2 研究目的與內容 2 第二章、 文獻回顧 4 2.1 路網模式(Network models) 4 2.2 微觀行人模式(Microscopic pedestrian model) 4 2.3 巨觀行人模式(Macroscopic pedestrian model) 5 2.4 中觀行人模式 (Mesoscopic pedestrian model) 6 2.5 小結 7 第三章、 中觀模擬動態災害下行人疏散設計方法 9 3.1 模式概念與架構 9 3.1.1 模式設計概念 9 3.1.2 研究流程 10 3.1.3 研究特點 11 3.2 模式設定 12 3.2.1 時間軸設定 12 3.2.2 空間軸設定 13 3.2.3 模式假設 25 3.3 行人疏散模式 25 3.3.1 行人移動方向設定 25 3.3.2 行人移動決策-跳躍率 39 第四章、 案例分析 45 4.1 小型案例分析 45 4.1.1 小型案例參數設定 45 4.1.2 小型案例方向性模擬成果 45 4.1.3 小型案例無方向性模擬成果 48 4.1.4 方向性與無方向性模擬成果比較 51 4.2 實際案例分析 57 4.2.1 實際案例時間軸設定 57 4.2.2 實際案例空間軸設定 58 4.2.3 資料處理流程 60 4.2.4 水庫潰堤資料輸出分析 61 4.2.5 水庫潰堤之影像內插 69 4.2.6 實際案例參數設定 72 4.2.7 實際案例方向性模擬成果 73 4.2.8 實際案成果小結 86 第五章、 結論與建議 88 5.1 中觀維度 88 5.2 動態環境 88 5.3 模擬時間與行人移動選擇改善 88 5.4 大、中網格的優勢 89 5.5 模式適用範圍 90 5.6 未來研究方向建議 90 第六章、 參考文獻 91 | |
dc.language.iso | zh-TW | |
dc.title | 中觀行人模式於大規模疏散模擬之應用 | zh_TW |
dc.title | A mesoscopic pedestrian model for large-scale evacuation simulation | en |
dc.type | Thesis | |
dc.date.schoolyear | 108-1 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 陳柏華(Albert Chen),吳沛儒(Pei-Ju Wu),蔡豐明(Feng-Ming Tsai) | |
dc.subject.keyword | 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, | zh_TW |
dc.subject.keyword | mesoscopic pedestrian simulation,large-scale pedestrian simulation,static floor field,directional factor,dynamic environment factor, | en |
dc.relation.page | 92 | |
dc.identifier.doi | 10.6342/NTU201901912 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2019-11-11 | |
dc.contributor.author-college | 工學院 | zh_TW |
dc.contributor.author-dept | 土木工程學研究所 | zh_TW |
顯示於系所單位: | 土木工程學系 |
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