||A Modified Entropy-based Dynamic Gravity Model for the Evacuation Trip Distribution Problem under the Impact of Typhoons
||Department of Transportation & Communication Management Science
Support vector regression
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.
TABLE OF CONTENTS
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
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