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運輸學刊 TSSCI

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篇名 混合車流格位傳遞模式之建立與驗證
卷期 24:2
並列篇名 Mixed Traffic Cell Transmission Models: Development and Validation
作者 邱裕謝志偉
頁次 245-276
關鍵字 混合車流格位傳遞模式機車熵值Mixed trafficCell transmission modelMotorcycleEntropyTSSCI
出刊日期 201206

中文摘要

格位傳遞模式 (Cell Transmission Model, CTM) 能有效模擬不同交通狀態下之純車流行為,但無法滿足模擬混合車流特性。基此,本文建立混合車流格位傳遞模式 (Mixed Traffic Cell Transmission Models, MCTM) 模擬汽機車混合車流行為。由於此兩車種係以不同的車流行為競爭道路最大容量與剩餘儲車空間。因此,MCTM 必須依據不同混合比例與交通時相動態調整,以反映自由流、同步流與壅塞流車流行為。透過實際觀察,本文建立兩種MCTM模式,模式一係參考上一格位汽車與機車之比例,模式二進一步利用熵值調整速率。本文蒐集臺北市實際道路車流特性驗證兩種MCTM 模式。結果顯示,本文所建立的兩種MCTM 模式在號誌時相及混合比例的變化下,比較實際車流特性與模擬值,平均絕對誤差百分比值低於30%,可達到精準確的模擬效果。

英文摘要

Cell transmission models (CTM) can efficiently simulate traffic hydrodynamics under various traffic conditions. The conventional CTM was designed for pure traffic. Incorporation of more realistic CTM rules into the simulation of mixed traffic on urban streets is comparatively less addressed. Based on this, this study proposes mixed traffic cell transmission models (MCTM) to replicate the behaviors of mixed traffic consisting of cars and motorcycles. Both types of vehicles exhibit rather different traffic behaviors in competing for roadway capacity and remaining storage space. Thus, MCTM should be dynamically adjusted and allocated between cars and motorcycles according to the ratio of vehicles types and traffic phases - free flow, synchronized, and congested. Based on the field observations, two MCTM models are developed. The first MCTM model uses the ratio of car to motorcycle in the last upstream cell to determine the amount of roadway resources allocated to cars, and vice versa for motorcycles. The second MCTM model further incorporates an entropy index to adjust the traffic speed. To validate the proposed MCTM models, videotapped traffic data in Taipei city covering a full traffic spectrum from free-flow to congested are collected. The results show that both of the proposed MCTM models can accurately simulate the traffic flows under various traffic phases and mixture ratios with an average MAPE (Mean Absolute Percentage Error) below 30%.

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