臺灣博碩士論文加值系統

微觀車流模擬由於其真實性與方便性，加上電腦硬體之快速發展，其被廣泛應用於車流分析。市面上微觀車流模擬軟體所使用之車流模型，其係以小型車為發展基礎，或者說以車道為發展基礎之模型；其對於純汽車車流之模擬已有良好之成效。然而臺灣之道路環境多為混合車流，機車在其中又扮演重要角色，且機車與汽車之行駛特性大不相同，若直接以一般基於小汽車為發展基礎之微觀車流模擬軟體進行混合車流之分析，其結果將失準。
有鑑於此，本研究發展一套能夠描述機車於混合車流路段中之車流模型。模型中車輛之推進將以車輛運動學為基礎，輔以以行人社會力為基礎之橫向行為子模型，與以跟車模型之智慧型駕駛模型為基礎之縱向行為子模型，作為影響車輛運動學模型而使車輛推進之依據。模型建構完成後並撰寫為模擬程式以實際應用。
為了模式確認、校估與驗證之用，本研究選定臺北市一路段進行微觀車流調查。調查所得之部分資料進行分析以供模型確認與校估之用。模型經校估後以另一部分資料進行模型驗證。驗證結果顯示本研究發展之模型能有效地模擬實際車流。

Microscopic traffic simulation has been widely used in traffic flow analysis due to its reality and convenience, and the rapid advancement of computer hardware. Most of microscopic traffic flow simulation software on the market utilizes lane-based traffic flow models, which are already effective in terms of simulating lane-based traffic flow. However, mixed traffic flow, where motorcycles play a great role, is everywhere on the road in Taiwan and the driving behaviors of motorcycles differ greatly from that of cars, which stand for lane-based traffic flow. If a lane-based microscopic simulation model is directly used for simulating the mixed traffic flow, the simulation results would not be satisfying.
Hence, this research develops a microscopic traffic flow model that applicable to mixed traffic flow. The model is based on the vehicle kinematics with the aid of the lateral sub-model and the longitudinal sub-model. The lateral sub-model is based on the social force model. The longitudinal sub-model is based on the intelligent driver model. These two sub-models make vehicles progress by means of the vehicle kinematics. Besides, programs are coded to make the model practical.
For verification, calibration and validation of the model, microscopic traffic flow data on a road section in Taipei City are collected. A part of data is analyzed for verification and calibration. The other part of data is for validation. The calibrated model is successfully validated to simulate real traffic flow.

摘要 iii
Abstract v
目錄 vii
圖目錄 xi
表目錄 xiii
1 緒論 1
1.1 研究緣起 1
1.2 研究目標 1
1.3 研究範圍 2
1.4 研究流程 2
2 文獻回顧 5
2.1 跟車模型 5
2.1.1 刺激反應方程式 6
2.1.2 行為門檻模式 7
2.1.3 細胞自動機模式 10
2.1.4 力場模式 11
2.2 變換車道模型 13
2.2.1 Gipps 變換車道模式 14
2.2.2 Willmann-Sparmann 模式 15
2.2.3 CORSIM 模式 16
2.3 無車道概念模型 17
2.3.1 安全空間模式 17
2.3.2 機車力場模式 19
2.4 小結 21
3 模型建構 23
3.1 模型架構 23
3.2 車輛運動學 24
3.3 縱向與側向變數 28
3.3.1 縱向淨間距 29
3.3.2 側向半淨間距 29
3.4 影響區域 30
3.5 縱向行為 31
3.6 橫向行為 37
3.6.1 周遭車輛影響力 38
3.6.2 路緣影響力 39
3.6.3 前車側向淨間距吸引力 40
3.7 決定最終轉向角度 45
3.8 車輛最大偏向角之限制 46
3.9 模擬流程 46
3.10 程式撰寫 47
4 微觀車流調查 49
4.1 調查地點選定 50
4.2 空拍影像 51
4.3 影像處理 52
4.4 軌跡追蹤 52
4.5 軌跡整理 53
5 調查資料分析 55
5.1 偏向角與速率之關係 55
5.2 最大速率分配 57
5.3 側向半淨間距 57
5.4 淨車頭距分配 59
5.5 加速度分配 59
5.6 減速度分配 60
5.7 前車側向淨間距吸引力之效用函數 60
6 模式校估與驗證 63
6.1 模型校估 65
6.2 校估指標 66
6.2.1 微觀校估指標 66
6.2.2 巨觀校估指標 66
6.2.3 校估模擬結果 67
6.3 參數校估結果 67
6.4 模型驗證 68
6.4.1 微觀驗證 68
6.4.2 巨觀驗證 71
6.5 模擬軌跡展示 71
7 結論與建議 75
7.1 結論 75
7.2 建議 76
參考文獻 77

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