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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/33822


    Title: 利用探針車空間分佈特性推估號誌化道路旅行時間之研究
    Other Titles: A study of spatial distribution characteristics of probe vehicles on signalized arterials travel time estimation
    Authors: 簡誌良;Chien, Chih-liang
    Contributors: 淡江大學運輸管理學系碩士班
    羅孝賢;Luo, Shiaw-shyan
    Keywords: 旅行時間推估;空間平均速率;探針車;探針車空間分佈特性;Travel Time Estimation;Space Mean Speed,;Probe Vehicle,;Spatial Distribution Characteristics of Probe Vehicles
    Date: 2008
    Issue Date: 2010-01-11 04:34:37 (UTC+8)
    Abstract: 本研究係探討以探針車推估號誌化道路旅行時間時,所需探針車數量、其空間位置與旅行時間推估誤差間之關係。
    道路旅行時間的資料蒐集工具可分為固定式偵測器(Vehicle Detector)與移動式探針車(Probe Vehicle)兩種。因移動式探針車佈設成本較低,在台灣實際應用上逐漸廣泛。但是,目前探針車的研究中,多為針對高速公路等非號誌化道路的探討,市區道路之旅行時間因受號誌影響而較少討論。
    因此,本研究主要目的有二;一、在有限的探針車數量下,以探針車空間位置關係修正號誌化道路旅行時間推估誤差。第二,若未來探針車普及的情況,如何於有限的抽樣成本下,抽取適當位置的探針車資料降低號誌化道路旅行時間推估誤差。
    本研究以實驗設計與變異數分析(Analysis of Variance ,ANOVA)探討探針車分佈與空間平均速率(Space Mean Speed)之關係,並利用探針車空間分布位置做為類神經網路(Artificial Neural Network)輸入變數以修正推估誤差。研究發現,短路段時(200公尺以下) 探針車分布位置與空間平均速率之推估誤差關係較不明顯,而中長路段 (200公尺以上) 時,探針車分布位置與推估誤差有顯著關係。且若以探針車位置做為類神經網路輸入變數,可有效修正推估誤差,取得較為準確之號誌化道路旅行時間。
    This research addresses the relationship between the needed amount and spatial distributions of Probe Vehicle, and the travel time estimation errors, when practically using Probe Vehicles on signalized arterials to estimate travel time.
    Tools of travel time’s data-collection can be categorized as Vehicle Detector and Probe Vehicle. Probe Vehicle is becoming widely used in Taiwan because of its lower setup cost. However, Probe Vehicle researches at present all focus on highway, non-signalized arterials, resulting in the lack of discussion in how traffic signals affect travel time in urban districts.
    Therefore, the purpose of this research contains two main points: first, under limited amount of probe vehicles, how to efficiently use their positions to correct estimation errors of travel time on signalized arterials; second, if the number of Probe Vehicle becomes very popular in the future, how to select Probe Vehicles’ data in which locations to narrow down the errors within limited sampling.
    This research uses experimental design and ANOVA (Analysis of Variance) to explore the relationship between Probe Vehicles’ spatial distribution and Space Mean Speed, and furthermore, uses Probe Vehicles’ spatial distribution as Artificial Neural Network’s variables to correct estimation errors. The research results show that the relationship between Probe Vehicles’ spatial distribution and Space Mean Speed is not obvious for the short link under 200 meters, but it is significant for the link longer than 200 meters. When Probe Vehicles’ spatial distribution is used as variables in Artificial Neural Network, travel time estimation errors can be efficiently corrected and more accurate signalized arterials travel time can be obtained.
    Appears in Collections:[運輸管理學系暨研究所] 學位論文

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