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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 詹魁元(Kuei-Yuan Chan) | |
| dc.contributor.author | Ya-Yuan Yang | en |
| dc.contributor.author | 楊雅媛 | zh_TW |
| dc.date.accessioned | 2022-11-23T09:08:11Z | - |
| dc.date.available | 2021-09-17 | |
| dc.date.available | 2022-11-23T09:08:11Z | - |
| dc.date.copyright | 2021-09-17 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-08-30 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/79704 | - |
| dc.description.abstract | 自動駕駛車輛是未來交通的趨勢，在道路上的佔有比例將會逐漸增加。然而現今沒有針對車輛「須達到什麼樣的門檻才能上路」或是「制定等級以區分上路行駛範圍」的共識與規範，尤其市區的道路設計與車輛互動行為較為複雜，因此我們透過了解車流變化，給予該制度之參考依據。本研究結合微觀車流模型與巨觀車流模型的優點，建立適用於城市的車流混成模型。在此模型中，交叉路口為道路系統之連結點，車流於該處轉向，引發各種交通衝突，故路口以微觀顯示，其餘交通現象則以巨觀模擬。本模型亦同時考量人為駕駛與自動駕駛，以全速差模型表示一般人為操作車輛，用智慧駕駛模型表示自動駕駛車輛。我們經由隊列穩定性、衝擊波、衝突量，分別探討不同決策行為之自動駕駛車輛對車流的影響。模擬結果顯示若自動駕駛車輛的策略過於保守會使車流隊列拉長且難以消散，過於積極則事故風險提高。若車輛間的決策邏輯相差甚遠，則在車流交會點發生事故的可能性也會提升。本論文以一個真實路口的案例模擬，可以得到保守策略自駕車不適合行駛於所有高流量路段，而積極自駕車不適合行駛於高流量又多路口的路段之結論，為自駕車策略的允許駕駛區域提供一個指引。 | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-23T09:08:11Z (GMT). No. of bitstreams: 1 U0001-2308202120290700.pdf: 10615671 bytes, checksum: b63b9de60ec1be93a576a033d52baee7 (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | "口試委員會審定書 i 誌謝 iii 中文摘要 v Abstract vii 目錄 ix 圖目錄 xiii 表目錄 xvii 符號列表 xix 第一章 緒論 1 1.1 前言 1 1.2 自動駕駛車輛能力驗證 3 1.3 研究動機與目的 5 1.4 論文架構 6 第二章 文獻回顧 9 2.1 車流模擬方法概述 9 2.2 微觀車流模型 10 2.2.1 安全距離模型(Safe Distance Model) 11 2.2.2 刺激反應模型(Stimulus Response Model) 11 2.2.3 心理物理間距模型(Psycho Physical Spacing Model) 13 2.2.4 智慧駕駛模型(Intelligent Driver Model) 14 2.2.5 細胞自動機模型(Cellular Automaton Model) 15 2.3 巨觀車流模型 16 2.3.1 簡單連續流(LWR Model) 16 2.3.2 高階連續流(PW Model) 17 2.3.3 路網車流狀態估計 17 2.4 模型轉換研究 18 2.5 自動駕駛車輛混流研究 19 2.6 道路衝突與安全評量研究 20 2.6.1 交通衝突理論 20 2.6.2 交通衝突指標 21 2.7 小結 24 第三章 整合微巨觀與其介面之車流模型 25 3.1 交通衝突型態 26 3.2 微觀模型 28 3.2.1 跟車行為 29 3.2.2 交會點決策行為 32 3.3 巨觀模型 32 3.3.1 車輛數守恆律 33 3.3.2 有限體積法(Finite Volume Method, FVM) 34 3.4 微巨觀之轉換介面建立 37 3.4.1 巨觀轉微觀介面(M2m) 38 3.4.2 微觀轉巨觀介面(m2M) 39 3.5 圖形使用者介面 41 3.6 小結 42 第四章 車流資料分析分法 43 4.1 車流穩定性 44 4.2 衝擊波 45 4.3 衝突量 49 4.4 小結 50 第五章 結果與討論 51 5.1 隊列穩定性分析 51 5.1.1 均質車流穩定性 52 5.1.2 混合車流穩定性 54 5.2 車流模擬時空圖 57 5.2.1 跟車策略對車流的影響 57 5.2.2 交會點禮讓策略對車流的影響 60 5.3 衝突熱點 63 5.3.1 接續衝突情境 63 5.3.2 併入衝突情境 64 5.3.3 分出衝突情境 65 5.3.4 穿越衝突情境 66 5.4 小結 68 第六章 案例探討 69 6.1 模擬之路口與其模型設定 69 6.2 案例之結果與討論 72 6.2.1 低流量路況模擬 73 6.2.2 高流量路況模擬 75 6.2.3 小結 78 第七章 結論 79 7.1 研究總結 79 7.2 未來工作 80 附錄A 程式碼說明 81 參考文獻 85" | |
| dc.language.iso | zh-TW | |
| dc.title | 建立微巨觀混成模型以了解自駕車決策對車流的影響 | zh_TW |
| dc.title | A hybrid micro-macro model for understanding the impact of autonomous vehicle strategies on traffic flow | en |
| dc.date.schoolyear | 109-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 楊馥菱(Hsin-Tsai Liu),蘇偉儁(Chih-Yang Tseng) | |
| dc.subject.keyword | 自動駕駛車輛,決策行為,車流混成模型,交通衝突,隊列穩定性,衝擊波, | zh_TW |
| dc.subject.keyword | Autonomous vehicle,Decision-making behavior,Hybrid micro-macro model,Traffic conflict,String stability,Shock wave, | en |
| dc.relation.page | 91 | |
| dc.identifier.doi | 10.6342/NTU202102645 | |
| dc.rights.note | 同意授權(全球公開) | |
| dc.date.accepted | 2021-08-31 | |
| dc.contributor.author-college | 工學院 | zh_TW |
| dc.contributor.author-dept | 機械工程學研究所 | zh_TW |
| 顯示於系所單位： | 機械工程學系 | |
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