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DC 欄位 | 值 | 語言 |
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dc.contributor.advisor | 坂井勝哉(Katsuya Sakai) | |
dc.contributor.author | Chen-Ting Wu | en |
dc.contributor.author | 吳承庭 | zh_TW |
dc.date.accessioned | 2021-06-15T12:42:50Z | - |
dc.date.available | 2020-08-14 | |
dc.date.copyright | 2020-08-14 | |
dc.date.issued | 2020 | |
dc.date.submitted | 2020-08-11 | |
dc.identifier.citation | Bruce D. Greenshields, Henry P. George, Neil S. Guerin, M. Ross Palmer Robin T. Underwood (1961). Quality and Theory of Traffic Flow. Carlos F. Daganzo (2007). Urban gridlock: Macroscopic modeling and mitigation approaches. Transportation Research Part B 41 (2007) 49–62 Chinese Engineering Consultant Inc. (2016) Research of Macroscopic Traffic Flow Model. Annual Research Report (2016) Erik Jenelius, Ida Kristoffersson Magnus Fransson (2017). Validation of Traffic Simulation Models Based on the Macroscopic Fundamental Diagram. Transportation Research Procedia 27 (2017) 561–568 Kara M. Kockelman (2014). The Travel and Environmental Implications of Shared Autonomous Vehicles, Using Agent-Based Model Scenarios. Transportation Research Part C, Vol 40 (2014): 1-13 Kashif Naseer Qureshi Abdul Hanan Abdullah (2013). A Survey on Intelligent Transportation Systems. Middle-East Journal of Scientific Research 15 (5): 629-642 (2013) Liang X., G. H. de A. Correia B. van Arema (2017). An optimization model for vehicle routing of automated taxi trips with dynamic travel times. Transportation Research Procedia 27 (2017) 736–743 Ludovic Leclercq Nikolas Geroliminis (2013). Estimating MFDs in Simple Networks with Route Choice. Procedia - Social and Behavioral Sciences 80 (2013) 99 – 118 Meng I-Heng (2020) Intelligent Transportation System (ITS) makes traveling better. Retrieved from https://futurecity.cw.com.tw/article/1326 Michael Hyland Hani S. Mahmassani (2018). Dynamic Autonomous Vehicle Fleet Operations: Optimization-Based Strategies to Assign AVs to Immediate Traveler Demand Requests. Transportation Research Part C 92 (2018) 278–297 Michael W. Levin, Kara M. Kockelman, Stephen D. Boyles Tianxin Li (2017). A General Framework for Modeling Shared Autonomous Vehicles with Dynamic Network-Loading and Dynamic Ride-Sharing Application. Computers, Environment and Urban Systems 64 (2017) 373–383 Michael W. Levin (2017). Congestion-aware system optimal route choice for shared autonomous vehicles. Transportation Research Part C 82 (2017) 229–247 Nikolas Geroliminis Carlos F. Daganzo (2008). Existence of urban-scale macroscopic fundamental diagrams: Some experimental findings. Transportation Research Part B 42 (2008) 759–770 Siamak A. Ardekani, Mostafa Ghandehari Shiva M. Nepal (2011). Macroscopic Speed-Flow Models for Characterization of Freeway and Managed Lanes. Department of Civil Engineering, the University of Texas, Arlington, USA. Sunghoon Kim, Sehyun Tak Hwasoo Yeo (2018). Agent-Based Network Transmission Model Using the Properties of Macroscopic Fundamental Diagram. Transportation Research Part C 93 (2018) 79–101. Takahiko KUSAKABE (2020). Adaptive Control Model of Vehicular Accumulation for Shared Use Autonomous Vehicle Mobility Service. The 99th annual meeting of Transportation Research Board, Washington, DC, USA, 2020.1 Takahiro Tsubota (2014). Exploring the Properties of Macroscopic Fundamental Diagram: Analyzing Brisbane Urban Network. Smart Transport Research Centre, Science and Engineering Faculty, Queensland University of Technology | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/50487 | - |
dc.description.abstract | 近年來,智慧運輸系統(ITS)變得越來越流行。世界各地的國家都在嘗試制定與ITS相關的政策和基礎架構。 智慧運輸系統中包含許多主題,例如,公共交通系統集成,智慧號誌系統和移動即服務(MaaS)等。實際上,它是交通系統的新概念。總而言之,智慧運輸系統是一個概念,每個運具都能夠與其他交通運具非常快速的聯繫,代表車輛或其他交通運具會上傳其當前訊息及車輛概況,並下載其他運具的資訊。如此高頻率的訊息交換,亦是5G發展的另一個主要原因。車輛可以透過互連技術相互連接,其中一項技術為自動駕駛技術。本文將開發一自動車營運系統控制策略的模擬模型。 該控制策略主要參考Kusakabe(2020)該篇論文的控制策略。該策略試圖通過動態控制共享自動駕駛系統的服務車隊規模來最大程度地減少乘客的總旅行時間,這意味著服務車隊規模是隨時間變化的。另外,根據宏觀車流理論基本圖(Daganzo,2007),可以得知系統中的車輛數和車輛平均速度的關係圖為負相關性。 本文將重新開發動態控制策略,並架構事件導向的模擬邏輯。事件導向的邏輯是由各個旅行事件組成:候車事件,乘客上車事件,乘客下車事件等。為了驗證該控制策略是否有用以及模擬模型是否正確,本文將在一維路網中測試,並在模擬軟體SUMO中定義一個更實際的二維模擬,以確保動態控制策略在更現實的路網中可行。 | zh_TW |
dc.description.abstract | In recent years, intelligent transportation system (ITS) becomes more and more popular. Countries all over the world are trying to develop the policies and infrastructure related to ITS. There are many topics included in ITS, for example, public transportation system integration, smart signaling system, and mobility as a service, etc. Actually, it is a new concept of transportation system. In a small conclusion, ITS is a concept that every modal has a strong connection to others, which means vehicles or other modals upload their current information and download others very frequently. This is the reason why 5G is also another popular issue. Vehicles can connect to each other with the interconnecting technology, one of which is known as the auto-pilot technology. This paper focus on developing a simulation model with an operation strategy for the autonomous vehicles. The operation strategy is mainly referred from Kusakabe (2020). This strategy tries to minimize the total travel time for passengers by controlling the serving fleet size of a sharing autonomous vehicle system dynamically, which means the service fleet size is time variable. In addition, based on the macroscopic fundamental diagram (Daganzo, 2007), it can be seen that more vehicles in the system causes low speed of vehicles. This paper will re-develop the dynamic operation model and construct an event-based simulation logic. Those events are the traveling events: waiting for vehicles, picked up by vehicles, dropped off by the vehicles, etc. To validate that the operation strategy is useful and the simulation model is correct, this paper will run a simplified one-dimensional simulation case. Then, this paper will define a two-dimensional simulation in SUMO environment, which is more practical to make sure the dynamic operation strategy is workable in more realistic network. | en |
dc.description.provenance | Made available in DSpace on 2021-06-15T12:42:50Z (GMT). No. of bitstreams: 1 U0001-1108202012464100.pdf: 3360122 bytes, checksum: 9ea36db06d01da8cfea981f86c3214c4 (MD5) Previous issue date: 2020 | en |
dc.description.tableofcontents | 摘要 i ABSTRACT ii CONTENTS iii Chapter 1 Introduction 1 1.1 Background 1 1.2 Research Purpose 4 Chapter 2 Literature Review 5 Chapter 3 Kusakabe’s Model 9 3.1 Static Operation Model 9 3.1.1 Assumption 9 3.1.2 Model Formulation 10 3.1.3 Result……………………………………………………..13 3.2 Dynamic Operation Strategy 14 3.2.1 Assumption 15 3.2.2 Simulation Construction 15 Chapter 4 One Dimensional Simulation 18 4.1 Simulation Logic 18 4.2 Input Settings 25 4.3 Result and comparison 27 4.3.1 Network settings 27 4.3.2 Result……………………………………………………..27 4.4 Conclusion 30 Chapter 5 Two-Dimensional Validation 32 5.1 Introduction of Simulation of Urban Mobility 33 5.2 Constructing Simulation with SUMO and Python 35 5.2.1 Simulation Settings 35 5.2.2 Simulation Logic 38 5.2.3 Input Value 44 5.2.4 MFD Estimation 44 5.3 Result 46 5.4 Conclusion 48 Chapter 6 Conclusion and Suggestion 50 6.1 Conclusion 50 6.2 Discussion and Suggestions 51 REFERENCES 53 | |
dc.language.iso | en | |
dc.title | 在SUMO環境下建立宏觀車流理論之交通行動服務自駕車控制策略模擬模型 | zh_TW |
dc.title | Two-Dimensional Network Simulation in SUMO for MFD-Based Operation Model of Autonomous Vehicle Mobility Service | en |
dc.type | Thesis | |
dc.date.schoolyear | 108-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 許聿廷(Yu-Ting Hsu),朱致遠(Jhih-Yuan Chu) | |
dc.subject.keyword | 自動車,控制策略,事件模擬,SUMO,宏觀車流理論基本圖, | zh_TW |
dc.subject.keyword | autonomous vehicle,control strategy,event-based simulation,SUMO,macroscopic fundamental diagram (MFD), | en |
dc.relation.page | 60 | |
dc.identifier.doi | 10.6342/NTU202002922 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2020-08-11 | |
dc.contributor.author-college | 工學院 | zh_TW |
dc.contributor.author-dept | 土木工程學研究所 | zh_TW |
顯示於系所單位: | 土木工程學系 |
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