發展本體論與多代理人模式於高速鐵路緊急調度之研究

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研究生：	何庭武 Ting-Wu Ho
論文名稱：	發展本體論與多代理人模式於高速鐵路緊急調度之研究 Development of an Ontology-based, Multi-Agent Model for Emergency Dispatching for High-Speed Rail System
指導教授：	周建成 Chien-Cheng Chou
口試委員:
學位類別：	博士 Doctor
系所名稱：	工學院 - 土木工程學系 Department of Civil Engineering
論文出版年：	2015
畢業學年度：	103
語文別：	英文
論文頁數：	132
中文關鍵詞：	多代理人系統、知識本體論、高速鐵路、列車調度
外文關鍵詞：	Multi-Agent System, Ontology, High-Speed Rail, Train Dispatching
相關次數：	點閱：7 下載：0
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高速鐵路與其他軌道運輸系統在設計上最大不同處為具有嚴格的定時運行特性，亦即列車運行、號誌控制，以至於旅客或票務資訊均必須依照時刻表來運作。當高鐵系統遇到災害等緊急事故時，傳統上各調度人員必須互相溝通協調，根據目前系統狀況構思出適合的調度程序，產生對應的列車時刻表。此過程非常仰賴調度人員的溝通效率與專業經驗，耗時且易出錯，不但耽擱旅客時間與減少載客量，亦可能因調度失當造成更多人員傷亡造成；此外，在緊急事故時，高鐵系統的電力供應常成為另一不穩定來源，過去研究甚少探討此因素對於整體調度的影響。經由技術文獻回顧可知，智慧型代理人技術非常適合用於多人協調問題上，故本研究提出以本體論與智慧型代理人技術為基礎之高鐵列車緊急事故調度模式，讓智慧型代理人具備列車調度知識，能夠即刻做出最適決策。本研究首先以本體論設計知識模型來表達包含行控中心控制員、列車駕駛員、基地管理員與電力控制員等，其次，設計列車調度的推論規則來模擬各調度員之間的溝通過程。最後並以高鐵實際案例作為模型測試驗證用，驗證結果顯示此模型能產生與人工調度最佳結果一致的調度班表，且大幅減少人工調度所需時間。應用本研究模型將有助於高鐵在緊急事故時之應變效率與行車安全的提升。

There is a big difference between HSR and traditional railway system is that HSR has a characteristic of strict on-time. The operations include train scheduling, sign control and passengers ticketing information must follow the train timetable. When an incident occurred in the HSR system, each dispatcher has to communicate and coordinate with each other. They cooperate to get the suitable dispatching procedure according to the current situation and generate a corresponding train timetable. This process is dependent on the efficiency of communication and professional experience for dispatchers, it is time-consuming and error-generating. In this way, it not only delayed the passenger time and reduced the passenger capacity but also caused more casualties due to dispatching error. In addition, the power supply often be another source of instability during incident, and the few studies over the past investigated this factor that has an effect on the overall dispatching. In the technical literature review shows that intelligent agent technology is ideally suited for the issue of people coordination. Therefore, this study proposed an ontology-based multi-agent model for emergency dispatching for HSR system. The intelligent agents have the train dispatching knowledge and can instantly make optimal decisions. Firstly, the ontology knowledge model is designed to express each components of the railway system include control controller, train driver, depot manager and power controller. Then, the train dispatching inference rules are designed to simulate the communicated process between each dispatcher. Finally, the HSR real cases are used to testing in verification and validation steps. The experimental results show that proposed model can generated the dispatching procedure is the same as the best human scheduling. Application of proposed model will contribute to increase the response efficiency and train operating safety during emergency for HSR system.

Abstract .. i
摘要 .. ii
誌謝 . iii
Contents  iv
List of Figures . vii
List of Tables . ix
Chapter 1 Introduction  1
1.1 Research Background and Motivation . 1
1.2 Research Question and Objectives . 4
1.3 Research Steps .. 5
1.4 Dissertation Structure  6
Chapter 2 Literature Review  7
2.1 Train Scheduling .. 7
2.2 Train Dispatching  9
2.3 Power Failure .. 11
2.4 Ontology-based Multi-Agent System . 12
2.4.1 Multi-Agent System . 12
2.4.2 Ontology . 16
2.4.3 Ontology-based Multi-Agent  17
2.5 Summary  17
Chapter 3 Design of OMAED-HSR .. 19
3.1 Design of MAS Model .. 19
3.2 Design of Ontology . 23
3.3 Inference Rules .. 27
3.3.1 Rule 1: Train Path Existence  27
3.3.2 Rule 2: Node Path Existence  28
3.3.3 Rule 3: Platform Checking  30
3.3.4 Rule 4: Stabling Yard Checking . 31
3.3.5 Rule 5: Spare Train Assignment for Train . 32
3.3.6 Rule 6: Spare Train Assignment for Node . 34
3.3.7 Rule 7: Trailer Assignment  35
3.3.8 Rule 8: Inspection Car Assignment .. 36
3.3.9 Rule 9: Train Movement for Station . 38
3.3.10 Rule 10: Train Movement for Depot . 39
3.3.11 Rule 11: Train Towing .. 41
Chapter 4 Verification and Validation . 43
4.1 Initiating the Platform . 43
4.2 Introduction of Taiwan HSR  46
4.3 Verification .. 48
4.3.1 Damage to Facility  48
4.3.2 Damage to Train . 52
4.3.3 Damage to Train Requiring Tow  55
4.4 Validation .. 58
4.4.1 Case of Study 1  60
4.4.2 Case of Study 2  64
4.4.3 Case of Study 3  68
4.5 Summary  74
Chapter 5 Conclusions .. 77
5.1 Conclusions . 77
5.2 Contributions .. 79
5.3 Suggestions .. 80
Appendix A: DM Increases Effectiveness of OR: Application to High-Speed Rail
Re-scheduling During Disaster . 82
A.1 Introduction  82
A.2 Literature Review  86
A.3 Development of Methodology .. 90
A.3.1 Procedures  90
A.3.2 Re-Scheduling Formulation . 93
A.3.3 Application to THSR .. 96
A.3.4 Scenarios Generation .. 98
A.3.5 Data Pre-processing .. 103
A.3.6 Data Mining Methodology  105
A.3.7 Data Post-processing  109
A.4 Conclusions . 110
Reference . 113
                                

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