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  1. NTU Theses and Dissertations Repository
  2. 電機資訊學院
  3. 生醫電子與資訊學研究所
請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/97397
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dc.contributor.advisor歐陽彥正zh_TW
dc.contributor.advisorYen-Jen Oyangen
dc.contributor.author陳雨彤zh_TW
dc.contributor.authorYu-Tung Chenen
dc.date.accessioned2025-05-22T16:13:06Z-
dc.date.available2025-05-23-
dc.date.copyright2025-05-22-
dc.date.issued2024-
dc.date.submitted2025-04-08-
dc.identifier.citationReferences

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[17] Artur Gramacki. *Nonparametric Kernel Density Estimation and Its Computational Aspects*, volume 37 of *Studies in Big Data*. Springer, 2018. Chapter 3.

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[19] George Grekousis and Yorgos N. Photis. Analyzing high-risk emergency areas with gis and neural networks: The case of athens, greece. *The Professional Geographer*, 66(1):124–137, 2014.

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[34] Chun-Chieh Yang. Kernel density based probability estimation for data classifiers. Master’s thesis, 2019.

[35] Rou-Jun Liu. A study on optimal bandwidth settings for adaptive kernel density estimation. Master’s thesis, National Taiwan University, 2022.

[36] Wei-Yi Li. Optimizing the deployment of ambulances based on an expectation-maximization derivative algorithm. Master’s thesis, National Taiwan University, 2024.

[37] United States Census Bureau. Marin county, california. [https://www.census.gov/](https://www.census.gov/), 2022.
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dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/97397-
dc.description.abstract本研究旨在通過使用核密度估計(Kernel Density Estimation, KDE)來優化緊急服務位置的覆蓋率並縮短響應時間。我們採用 KDE 方法預測緊急事件的高發區域,並將這些區域作為最大覆蓋位置問題模型(Maximal Covering Location Problem, MCLP)的輸入,以改善資源分配。實驗結果顯示,KDE-MCLP 方法在平均響應時間和十分鐘內的覆蓋率方面顯著優於傳統的 MCLP 方法。具體而言,KDE-MCLP 方法的響應時間更短,且十分鐘內的覆蓋率更高,突顯了其在快速響應和廣泛覆蓋方面的顯著改進。這些結果表明,將 KDE 與 MCLP 結合使用可以顯著提升緊急資源配置的效率和效果,從而提高緊急醫療服務的整體效能。zh_TW
dc.description.abstractThis study aims to optimize the coverage and reduce the response time of emergency service locations using Kernel Density Estimation (KDE). We employed KDE to predict high-probability areas for emergency incidents and used these areas as inputs for the Maximal Covering Location Problem (MCLP) model to enhance resource allocation. The experimental results indicate that the KDE-MCLP method significantly outperforms the traditional MCLP method in terms of average response time and ten-minute coverage rate. Specifically, the KDE-MCLP method consistently shows shorter response times and higher ten-minute coverage rates, highlighting its significant improvements in rapid response and extensive coverage. These findings suggest that integrating KDE with MCLP can significantly enhance the efficiency and effectiveness of emergency resource allocation, ultimately improving the overall performance of emergency medical services.en
dc.description.provenanceSubmitted by admin ntu (admin@lib.ntu.edu.tw) on 2025-05-22T16:13:06Z
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dc.description.provenanceMade available in DSpace on 2025-05-22T16:13:06Z (GMT). No. of bitstreams: 0en
dc.description.tableofcontentsVerification Letter from the Oral Examination Committee i

Acknowledgements ii

摘要 iii

Abstract iv

Contents v

List of Figures vii

List of Tables viii

Denotation ix

Chapter 1 Introduction 1
1.1 Background ........................................... 1
1.2 Aim of the study ..................................... 3

Chapter 2 Literature Review 4
2.1 Kernel Density Estimation .......................... 4
2.2 Static Covering Models ............................ 6
2.2.1 Resource Minimization Problem .......... 7
2.2.2 Coverage Maximization Problem .......... 9
2.2.3 Probabilistic Coverage Problem ........... 12
2.2.4 Maximal Survival Models ................... 17
2.2.5 Equity Models ................................. 18

Chapter 3 Method 21
3.1 Implementation of the conventional MCLP algorithm .................................... 21
3.2 Incorporation of the ERAKDE algorithm .......... 22
3.3 Evaluation of deployment schemes ................. 24

Chapter 4 Experiment 26
4.1 Study Area ............................................. 26
4.2 Result .................................................... 27

Chapter 5 Conclusion 34
5.1 Conclusion ............................................. 34
5.2 Future Work ............................................ 35

References 37

Appendix A — MISE 42
-
dc.language.isoen-
dc.subject最大覆蓋位置問題zh_TW
dc.subject覆蓋率zh_TW
dc.subject響應時間zh_TW
dc.subject資源分配zh_TW
dc.subject緊急醫療服務zh_TW
dc.subject核密度估計zh_TW
dc.subjectEMSen
dc.subjectMCLPen
dc.subjectKDEen
dc.subjectCoverage Rateen
dc.subjectResponse Timeen
dc.subjectResource Allocationen
dc.title運用核心密度估計最佳化緊急醫療服務資源的配置zh_TW
dc.titleExploiting Kernel Density Estimation to Optimize Resource Deployment of Emergency Medical Servicesen
dc.typeThesis-
dc.date.schoolyear113-2-
dc.description.degree碩士-
dc.contributor.oralexamcommittee黃乾綱;孫維仁;楊孟翰zh_TW
dc.contributor.oralexamcommitteeChien-Kang Huang;Wei-Zen Sun ;Meng-Han Yangen
dc.subject.keyword核密度估計,最大覆蓋位置問題,緊急醫療服務,資源分配,響應時間,覆蓋率,zh_TW
dc.subject.keywordKDE,MCLP,EMS,Resource Allocation,Response Time,Coverage Rate,en
dc.relation.page44-
dc.identifier.doi10.6342/NTU202404329-
dc.rights.note未授權-
dc.date.accepted2025-04-08-
dc.contributor.author-college電機資訊學院-
dc.contributor.author-dept生醫電子與資訊學研究所-
dc.date.embargo-liftN/A-
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