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研究生:張鈞凱
研究生(外文):Chun-Kai Chang
論文名稱:高速公路可變速限聯合匝道儀控最佳化模式
論文名稱(外文):An Optimization Model of Coordinated VSL and Ramp Metering Control on Freeway
指導教授:許添本許添本引用關係
指導教授(外文):Tien-Pen Hsu
口試委員:邱裕鈞胡守任
口試日期:2014-06-25
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:土木工程學研究所
學門:工程學門
學類:土木工程學類
論文種類:學術論文
論文出版年:2014
畢業學年度:102
語文別:中文
論文頁數:136
中文關鍵詞:可變速限匝道儀控數學規劃適應性控制
外文關鍵詞:Variable Speed LimitRamp MeteringMathematical ProgramTraffic-Responsive Control
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隨著私人車輛持有率和使用率不斷增加,造成交通量迅速成長,在尖峰時段或連續假日高速公路往往會發生大範圍及長時間的壅塞,甚至回堵至地方道路而造成全面性的壅塞。這些壅塞將導致高速公路運作的效率降低,也帶來了龐大的社會成本。因此,為提升高速公路的使用效率以及減少社會成本,有必要採取相關的交通控制措施以維持車流的穩定。
  本研究以國道五號北上路段為例,根據車輛偵測器資料分析研究範圍內之壅塞特性,由分析結果可知,國道五號北上路段壅塞範圍大多集中於雪山隧道,且車流速率於五分鐘內即會由90km/hr降至10km/hr,具有劇變現象。因此,本研究透過數學規劃法建構一套可變速限聯合匝道儀控最佳化模式,希望利用可變速限之特性控制車流以漸變的方式進入雪山隧道,並搭配上游匝道連鎖控制,以減緩或避免雪山隧道壅塞產生。研究中並以離散型二階巨觀車流模式-METANET為基礎,發展一套適用於國道五號北上路段之巨觀車流模式,本研究將之稱為「METANET長隧道模式」。
  然而,最佳化模式因參數校估不易以及求解速率過慢等特性,使之難以運用在線上控制中,為克服上述問題,本研究利用類神經網路建構預測模式,以預測最佳化模式之輸入參數,並透過啟發式演算法提升最佳化模式之求解效率,以達到線上控制之目的。
 為評估模式之控制績效,本研究利用微觀模擬軟體VISSIM繪製國道五號北上路段之模擬路網,並透過驗證程序確認模擬路網之真實性,最後,以VISSIM外掛程式VisVap將最佳化模式求解結果應用至模擬路網中,模擬結果顯示,在控制後主線旅行時間與雪山隧道總通過量均有顯著的改善,顯示本研究所研擬之控制模式能有效提升國道五號北上路段之使用效率。


With the growth of private car holding rate and usage rate, traffic volume increases rapidly, huge scope and long-time congestion happens in the peak hour or on long vacations on freeway, the congestion also blocked back to local roads which cause entirely congestion. The efficient of freeway reduced and social cost increased due to congestion, in order to raise the efficient and reduce social cost, it is necessary to adopt relative traffic control strategy to maintain the stability of traffic flow.
The research takes north bound of freeway No. 5 as an example. First of all, to analysis the congestion characteristics in the research scope by the data from vehicle detector. It is known that the congestion segment is focused on HsuehShan tunnel, and car velocity reduced from 90km/hr to 10km/hr in 5 minutes, which is the catastrophe phenomena. As a result, this research construct a optimal model of coordinated VSL(Variable Speed Limit) and ramp metering by mathematical program, to control traffic flow entering HsuehShan tunnel with gradually speed reduction by the characteristic of VSL, also coordinate with upstream ramp interlocked control, to mitigate or even prevent from the congestion of HsuehShan tunnel. This model also based on discrete second order macroscope traffic flow model - METANET to develop a macroscope traffic flow model witch fits the north bound segment of freeway No. 5, which is named "METANET long tunnel model" by this research.
However, due to the difficulty of parameter calibration and slow solving rate of optimal model, it is unrealistic to be used in online control. In order to overcome this difficulty, Artificial neural network is used to construct the prediction model and predict the input parameters of optimal model, also heuristic algorithm is used to enhance the solving rate, with the combination to reach the goal of online control.
In order to assess the performance of this model, microscope simulation software - VISSIM is used to sketch the network of the northbound of freeway No. 5, the validation process is taken to confirm the reality of simulation network. Finally, the plug-in software VisVap is been used to apply the result optimal model into simulation model. The result indicated that the mainline travel time and total throughput of HsuehShan tunnel increased significantly after control, which indicate the usage rate of northbound of freeway No. 5 is enhanced effectively by the control model in this research.


誌謝 i
摘要 iii
ABSTRACT iv
目錄 vi
圖目錄 ix
表目錄 xii
第一章 緒論 1
1.1 研究背景與動機 2
1.2 研究目的 3
1.3 研究範圍 4
1.4 研究流程與內容 5
第二章 文獻回顧 8
2.1 可變速限控制 8
2.2 匝道儀控 15
2.2.1 獨立型 15
2.2.2 連鎖合作型 20
2.2.3 連鎖競爭型 22
2.2.4 整合型 27
2.3 可變速限聯合匝道儀控 34
2.4 控制演算法小結 39
2.5 巨觀車流模式 40
2.5.1 車流基礎構圖 41
2.5.2 一階巨觀車流模式 44
2.5.3 二階巨觀車流模式 48
2.6 車流模式小結 54
第三章 現況分析 58
3.1 時空圖分析 58
3.2 容量分析 60
3.3 基礎構圖分析 61
3.4 速率變化圖分析 63
第四章 可變速限聯合匝道儀控最佳化模式 65
4.1 METANET模式 65
4.1.1 路段切分 66
4.1.2 建立各路段期望速率與密度關係式 68
4.1.3 決定各路段格位數 69
4.1.4 參數校估 71
4.2 METANET長隧道模式 79
4.2.1 參數校估 79
4.3 最佳化模式 84
第五章 預測模式 90
5.1 速率預測模式 91
5.2 密度預測模式 93
5.3 流量預測模式 94
5.3.1 上匝道流量 95
5.3.2 下匝道流量 96
5.3.3 上下游邊界流量 97
第六章 模擬平台建構 98
6.1 路網驗證 98
第七章 模式應用與案例分析 100
7.1 即時求解架構 100
7.2 績效評估 105
7.2.1 主線績效 106
7.2.2 匝道績效 109
第八章 結論與建議 113
8.1 結論 113
8.2 建議 114
參考文獻 115
附錄一 123
附錄二 126
附錄三 128
附錄四 135


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