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How uncertainty in input and parameters influences transport model: output A four-stage model case-study

机译：输入和参数的不确定性如何影响运输模型：输出四阶段模型案例研究

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摘要

If not properly quantified, the uncertainty inherent to transport models makes analyses based on their output highly unreliable. This study investigated uncertainty in four-stage transport models by analysing a Danish case-study: the Naestved model. The model describes the demand of transport in the municipality of Naestved, located in the southern part of Zealand. The municipality has about 80,000 inhabitants and covers an area of around 681 km~2. The study was implemented by using Monte Carlo simulation and scenario analysis and it focused on how model input and parameter uncertainty affect the base-year model outputs uncertainty. More precisely, this study contributes to the existing literature on the topic by investigating the effects on model outputs uncertainty deriving from the use of (ⅰ) different probability distributions in the sampling process, (ⅱ) different assignment algorithms, and (ⅲ) different levels of network congestion. The choice of the probability distributions shows a low impact on the model output uncertainty, quantified in terms of coefficient of variation. Instead, with respect to the choice of different assignment algorithms, the link flow uncertainty, expressed in terms of coefficient of variation, resulting from stochastic user equilibrium and user equilibrium is, respectively, of 0.425 and 0.468. Finally, network congestion does not show a high effect on model output uncertainty at the network level. However, the final uncertainty of links with higher volume/capacity ratio showed a lower dispersion around the base uncertainty value. Results are also obtained from the implementation of the analysis on a real case involving the fi-nalization of a ring road around Naestved. Three different scenarios were tested. The resulting uncertainty in the travel time savings from the comparison of the three scenarios expressed in terms of coefficient of variation, turned out to be between 0.133 and 0.145, thus confirming the importance of uncertainty analysis in transport policy assessment.
机译：如果不能正确量化，则运输模型固有的不确定性使基于其输出的分析非常不可靠。本研究通过分析丹麦的案例研究：Naestved模型，研究了四阶段运输模型中的不确定性。该模型描述了位于新西兰南部的内斯特韦德市的运输需求。该市约有80,000居民，面积约681 km〜2。该研究是通过使用蒙特卡洛模拟和情景分析来实施的，其重点是模型输入和参数不确定性如何影响基准年模型输出的不确定性。更准确地说，本研究通过调查在采样过程中使用（ⅰ）不同的概率分布，（ⅱ）不同的分配算法和（ⅲ）不同级别对模型输出不确定性的影响，为有关该主题的现有文献做出了贡献网络拥塞。概率分布的选择对模型输出不确定性的影响很小，可以用变异系数来量化。取而代之的是，关于选择不同的分配算法，由随机用户均衡和用户均衡导致的以变异系数表示的链路流量不确定性分别为0.425和0.468。最后，网络拥塞对网络级别的模型输出不确定性影响不大。但是，具有较高体积/容量比的链接的最终不确定性表明，基本不确定性值附近的分散性较低。通过对涉及奈斯特韦德（Naestved）环城公路最终定案的实际案例进行分析，也可以获得结果。测试了三种不同的方案。通过比较三种用变异系数表示的情景所得出的节省旅行时间的不确定性在0.133和0.145之间，从而证实了不确定性分析在运输政策评估中的重要性。

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  • 来源
    《Transport policy》 |2015年第2期|64-72|共9页
  • 作者

    Stefano Manzo; Otto Anker Nielsen; Carlo Giacomo Prato;

  • 作者单位

    Technical University of Denmark, Department of Transport, Bygningstorvet 116B, 2800 Kgs. Lyngby, Denmark;

    Technical University of Denmark, Department of Transport, Bygningstorvet 116B, 2800 Kgs. Lyngby, Denmark;

    Technical University of Denmark, Department of Transport, Bygningstorvet 116B, 2800 Kgs. Lyngby, Denmark;

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  • 正文语种 eng
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  • 关键词

    Uncertainty; Demand modelling; Four-stage model; Sampling;

    机译：不确定;需求建模;四阶段模型;采样;

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