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系統識別號 U0026-1808202021283200
論文名稱(中文) 需求導向下之自動駕駛車隊管理
論文名稱(英文) A study on Autonomous Mobility-on-Demand fleet management
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 108
學期 2
出版年 109
研究生(中文) 羅元亭
研究生(英文) Yuan-Ting Lo
學號 R56071127
學位類別 碩士
語文別 英文
論文頁數 89頁
口試委員 指導教授-胡大瀛
口試委員-沈宗緯
口試委員-董啟崇
口試委員-朱致遠
口試委員-陳麗雯
中文關鍵字 自動駕駛即時行動服務  車隊管理  代理人基模擬 
英文關鍵字 Autonomous Mobility-on-Demand  Fleet management  Agent-based simulation 
學科別分類
中文摘要 2018年聯合國經濟和社會事務部人口司對世界城市化前景指出,目前在世界上有55%的人口居住在各國主要的大都市地區,未來將有更多的人口往都市地區移動,預計在2050年都市人口將會提升至68%。然而,這些城市是主導世界經濟脈動的重要地區,其中將近90%的城市集中在亞洲和非洲地區,都市人口集中導致城市道路和住宅空間短缺等問題出現。為了滿足人口不斷成長和扶持工商業發展,人們的移動行為必須邁向智慧化發展,才能有效利用都市空間。
由於自動駕駛技術日漸成熟,利用自動駕駛技術的新型運輸服務如:聯網自動駕駛車輛和自動駕駛即時行動服務,都在朝向智慧移動發展的進程中占有一席之地。其中,自動駕駛即時行動服務透過自動駕駛車輛,在任何時間、任何地點提供載客服務,將旅客接送至目的地。基於自動駕駛車輛的靈活度和高效率等優點,自動駕駛即時行動服務對於需求端的乘客、或是供給端的運營商皆具有相當大的吸引力,未來也將能降低交通肇事發生率及減少都市停車需求。
商業模式為落實自動駕駛即時行動服務的關鍵要素之一,運營商必須採取適當的運營策略來提高營運績效並且獲取利潤。根據過去的研究,在AMoD服務的營運策略方面,本研究主要探討自動駕駛車輛派遣問題。
本研究主要解決需求導向下的自動駕駛車隊派遣問題,車隊的營運架構需要將客戶的乘車需求與可用的車輛配對。在任何時間點,派遣中心必須將可調度的自駕車輛派遣給指定旅客,最終所有旅客都必須被服務完成,並且透過演算法最小化車隊公里和等待時間。
為了解決車輛派遣問題,透過使用代理人基模擬之架構建構出自動駕駛即時行動服務之系統。最後,我們選擇高雄市三民區作為研究區域,並在該地區測試不同的最佳化派遣策略,觀察運輸供需之間的交互作用。研究模擬結果將提供未來台灣自動駕駛即時行動服務的整體系統設計及決策參考依據。
英文摘要 The 2018 revision of the World Urbanization Prospects by UN DESA’s Population Division notes that 55% of the world’s population lives in major metropolitan areas and more people are coming: by 2050, that number is expected to 68%. These cities are the economic powers of the world, with close to 90% of the increase concentrated in Asia and Africa. Cities are already short on roads and housing. To accommodate population growth and support business, mobility will need to get smarter.
Owing to the maturity of self-driving technology, new transportation services including autonomous ones can play an important role in enabling smarter mobility such as connected autonomous vehicles (CAVs) and autonomous Mobility-on-Demand (AMoD), an AMoD system can pick up travelers at any time and any location, then send them to the destination they want, the whole procedure is provided by autonomous vehicles (AVs). The AMoD systems are attractive to passengers and operators because of the flexibility and efficiency, also it will decrease the traffic accidents and free up lanes on many urban roads by eliminating parking cars in the future.
However, the business model dominates one of the key elements of the implementation of AMoD systems. The operators must apply a proper operational strategy to improve the system performance and further make the profit. Following past studies, we mainly discuss the assignment problem for AMoD service.
This research addresses the issue of fleet dispatching. The operational policy needs to match travelers’ immediate requests with available vehicles. At any time, the dispatcher can assign an available vehicle to a specific traveler. The dispatching kilometers must be covered to serve the traveler and the dispatching distance and the waiting time can be minimized by the algorithm.
The objective of this research is to solve the assignment problem. By using an agent-based framework to model the AMoD operation system. Finally, we test the proposed model and simulate it in the San-min district of Kaohsiung City. This research embodies the interaction between demand and supply through simulation. The results of the analysis will support decision-making about comprehensive system design for the AMoD services entering the Taiwan market.
論文目次 Abstract i
摘要 iii
Contents vi
List of Figure viii
List of Table x
CHAPTER 1 INTRODUCTION 1
1.1 Research Motivation and Background 1
1.2 Research Objectives 4
1.3 Research Flow Chart 5
CHAPTER 2 LITERATURE REVIEW 8
2.1 Autonomous Vehicles 8
2.1.1 Features of Autonomous Vehicles 10
2.1.2 Key elements of Autonomous Vehicles 13
2.2 Mobility-on-Demand 15
2.3 Autonomous Mobility-on-Demand 16
2.3.1 Features of Autonomous Mobility-on-Demand 17
2.3.2 Approaches for Autonomous Mobility-on-Demand 18
2.3.3 Real-world Implementations of Autonomous mobility-on-Demand 19
2.4 Vehicle Routing Problem 21
2.4.1 Categories of Vehicle Routing Problem 22
2.4.2 Three-Echelon Framework for Dynamic Vehicle Routing Problem 23
2.4.3 Solution Strategies of Vehicle Routing Problems 28
2.5 Assignment problem 29
2.6 Agent-based simulation 31
2.6.1 Structure of Agent-based simulation 32
2.6.2 Certain Essential Characteristics of Agent 33
2.6.3 Agent-based Modeling Design 36
2.6.4 Applications of Agent-based simulation for Autonomous Mobility-on-Demand 37
2.7 Summary 40
CHAPTER 3 RESEARCH METHODOLOGY 41
3.1 Problem Statement and Research Assumption 41
3.2 Research Framework 42
3.3 Definition of the Variables and Parameters 43
3.4 Strategy-based Fleet Management 45
3.4.1 Strategy 1 46
3.4.2 Strategy 2 47
3.4.3 Strategy 3 49
3.4.4 Strategy 4 50
3.5 Solution Algorithm 52
CHAPTER 4 EMPIRICAL STUDY 54
4.1 Program Flowchart 54
4.2 Input Data Description 56
4.2.1 Basic Data of Experimental Network 56
4.2.2 Fleet Operation Parameters 59
4.3 AMoD Service Simulation Framework 61
4.4 Test Experiments 63
4.4.1 Small Instance with 5 Requests 63
4.4.2 Large Instance with 20 Requests 65
4.5 Results of Analysis 67
4.5.1 Small Instance with 5 Requests 68
4.5.2 Large Instance with 20 Requests 70
4.6 Sensitivity Analysis 74
4.7 Summary 82
CHAPTER 5 CONCLUSIONS AND SUGGESTIONS 83
5.1 Conclusions 83
5.2 Suggestions 83
REFERENCES 85
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