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系統識別號 U0026-0102201521353200
論文名稱(中文) 颱風影響下修正熵為基礎之動態重力模式於疏散旅次分佈問題之研究
論文名稱(英文) A Modified Entropy-based Dynamic Gravity Model for the Evacuation Trip Distribution Problem under the Impact of Typhoons
校院名稱 成功大學
系所名稱(中) 交通管理科學系
系所名稱(英) Department of Transportation & Communication Management Science
學年度 103
學期 1
出版年 104
研究生(中文) 何偉銘
研究生(英文) Wei-Ming Ho
電子信箱 wmho.jui@gmail.com
學號 R58971056
學位類別 博士
語文別 英文
論文頁數 115頁
口試委員 口試委員-李治綱
口試委員-魏健宏
口試委員-胡守任
口試委員-黃國平
口試委員-盧宗成
口試委員-董啟崇
指導教授-胡大瀛
中文關鍵字 旅次分佈  重力模式    支援向量回歸  颱風 
英文關鍵字 Trip distribution  Gravity model  Entropy  Support vector regression  Typhoon 
學科別分類
中文摘要 為了避免颱風影響下道路損壞所導致的嚴重延滯,預測颱風對於運輸路網的影響是重要的議題,可減少威脅生命的風險。颱風莫拉克,於西元2009年8月2日形成,是台灣史上導致最嚴重災情的颱風,造成超過700人死亡。在颱風疏散的過程,其中一個重要的議題,是如何在有限與不同的的庇護所之下,考量空間與依時性特性,有效地進行疏散旅次之分佈。本研究主要建構支援向量回歸進行颱風對於運輸路網影響之預測,以及提出一以修正熵為基礎之動態重力模式來反映空間與依時性特性下,疏散者與庇護所之間旅次分佈的關係。本研究利用支援向量回歸模式求解非線性問題,校估後的支援向量回歸模式,進行颱風對於運輸路網影響之預測,其結果並加以驗證應用於旅次分佈中。
在以修正熵為基礎之動態重力模式中,主要特色為將熵整合於旅行成本限制中,旅行成本限制可與熵做一連結,並間接影響旅次分布,增加疏散下的可及性與效率。預期相較於傳統熵為基礎之動態重力模式中,能具較低的成本值。模式之空間分佈,主要以阻抗方程式加以表示,模式之時間分佈,主要利用依時性離散型時間區段表示。並利用模擬指派模式產生區到區之旅行時間。模試驗證、求解與校估分析主要應用於受莫拉克颱風影響的高雄甲仙路網上,並分析支援向量回歸與修正熵為基礎之動態重力模式之績效。結果顯示,支援向量回歸預測絕對值相對誤差為9.7%。而校估結果顯示,修正熵為基礎之動態重力模式比傳統熵為基礎之動態重力模式具較好的熵收斂結果、較高的旅行成本係數與較低的一般化旅行成本,可適用於颱風緊急疏散下旅次分佈規劃之應用。
英文摘要 The ability to predict the impact of typhoons on the transportation infrastructure is important, as it can help to avoid serious delays when roads are closed due to such events. Typhoon Morakot, which formed on August 2, 2009, was the deadliest typhoon in Taiwan’s history, responsible for over 700 deaths on the island. During the typhoon evacuation process, one critical issue is how to efficiently distribute evacuation trips to a limited number of shelters based on both temporal and spatial considerations. This research focuses on the prediction of typhoon impact on transportation networks with support vector regression (SVR) and proposes a dynamic modified entropy-based gravity model to reflect the spatial and temporal distribution of the evacuees and shelters. An SVR model is constructed to solve a non-linear prediction problem. The SVR model is calibrated and validated by a heuristic process. The calibrated and validated results are then applied to predict closed roads for trip distributions.
In the modified dynamic entropy-based gravity model, entropy is explicitly incorporated within the travel cost constraints. The travel cost constraints can thus be directly linked with the entropy values, and these values can then indirectly affect the trip distribution for evacuation operations to improve efficiency and accessibility. The spatial and temporal relationships between evacuees and shelters can be reflected through the impedance functions and the discretized time intervals with better performances than the traditional model. A simulation-assignment model is applied to generate zone to zone travel time. A calibration analysis based on the solution procedure is conducted for the Jiasian network, which was heavily affected by the Typhoon Morakot. The results show that the mean absolute percentage error (MAPE) of SVR prediction is 9.7%. The calibration results show that the modified entropy-based dynamic gravity model leads to better convergence patterns in the entropy values, higher travel cost coefficients and lower average generalized trip costs than the traditional model, and is suitable for use with the evacuation plan under typhoons.
論文目次 TABLE OF CONTENTS

ABSTRACT I
摘要 II
ACKNOWLEDGEMENT IV
誌謝 V
TABLE OF CONTENTS VI
LIST OF TABLES IX
LIST OF FIGURES X

CHAPTER 1 INTRODUCTION 1
1.1 Background and Motivation 1
1.2 Objectives 4
1.3 Research Flowchart 4
1.4 Overview 6

CHAPTER 2 LITERATURE REVIEW 8
2.1 Impact of Nature Disasters and Adverse Weather on Transportation 8
2.2 Predicting the Impact of Typhoons on Transportation Networks 14
2.3 SVR Model 16
2.4 Transportation planning under Climate change 18
2.5 Category of Evacuations 21
2.5.1 Flood evacuation 22
2.5.2 Hurricane evacuation 23
2.5.3 Other evacuations 25
2.6 Evacuation Trip distribution Models 26
2.6.1 Static Trip distribution Models 26
2.6.2 Dynamic Trip distribution Models 28
2.6.3 Entropy and Simulation Models for Evacuation Trip Distribution 28
2.7 Calibration Index for Evacuation Trip distribution Models 29
2.8 Network Reliability 29
2.9 Simulation Assignment Model: DynaTAIWAN 31
2.10 Summary 33

CHAPTER 3 RESEARCH METHODOLOGY 35
3.1 Framework of Transportation Planning under Climate Change 35
3.2 Overall Framework 37
3.3 SVR Model 40
3.3.1 Problem Statement of the SVR Model 40
3.3.2 Research Framework of the SVR Model 42
3.3.3 Model Formulation of the SVR Model 46
3.3.4 Measurement Criteria 48
3.4 Modified Entropy-based Gravity Model 48
3.4.1 Problem Statement of the Modified Entropy-based Gravity Model 49
3.4.2 Research Framework of the Modified Entropy-based Gravity Model 49
3.4.3 Model Formulation of the Modified Entropy-based Gravity Model 52
3.4.4 Solution Procedure of the Modified Entropy-based Gravity Model 54
3.5 Summary 57

CHAPTER 4 NUMERICAL EXPERIMENT AND RESULTS ANAYLSIS OF THE SVR MODEL 59
4.1 Input and Output Data in the SVR Model 59
4.2 Calibration and Model Validation 61
4.2.1 Calibration Process 61
4.2.2 Calibration Results 62
4.2.3 Result Validation 63
4.3 Numerical Experiments and Analysis of the Results 67
4.3.1 Network Characteristics and Simulated Scenarios 67
4.3.2 Analysis of the Results 73
4.4 Summary 76

CHAPTER 5 NUMERICAL EXPERIMENT AND RESULTS ANAYLSIS OF THE MODIFIED ENTROPY-BASED GRAVITY MODEL 77
5.1 Numerical Experiment in Jiasian network 77
5.1.1 Calibration Description 79
5.1.2 Data Description and Research Assumptions in Jiasian network 79
5.1.3 Comparison Results in Jiasian network 80
5.2 Sensitivity Analysis: Different Time Intervals 84
5.3 Numerical Experiment in Kaohsiung City sub-network 85
5.3.1 Data Description and Research Assumptions in Kaohsiung City sub-Network 85
5.3.2 Comparison Results in Kaohsiung City sub-Network 87
5.4 Traffic Strategy in Kaohsiung City sub-Network 91
5.5 Summary 92

CHAPTER 6 CONCLUSIONS 93
6.1 Overall Conclusions 93
6.2 Research Contributions 94
6.3 Research Limitation and Future Research 94

REFERENCES 96

VITA 111
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