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首页> 外文期刊>Transportation Research >Updating and transferring Random Effect models: The case of operating speed percentile estimation
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Updating and transferring Random Effect models: The case of operating speed percentile estimation

机译：更新和传输随机效果模型：操作速度百分位数的情况

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Random Effect (RE) models are used for analyzing data that are non-independent or when data are characterized by a hierarchical structure. In traffic and highway engineering, RE models have been successfully employed to estimate free-flow speed distributions from data containing observations that are naturally nested according to different levels (i.e. direction, sections, roads). Empirical studies conducted on both urban arterials and rural two-lane highways have shown that RE models, by properly accounting for the survey design, are superior to traditional Fixed Effect (FE) models. However, RE models are non-transferable because of the unknown RE value for roads or road sections belonging to a different network or road of the same network that were not originally used to develop the model. In this paper, the transferability of RE models to road sections that were not in the original sample used for model estimation was studied, under the assumption that for these additional sections very few observations are available or can be collected. This problem poses two challenges. First, random effects for the new road sections should be estimated in order to make outof-sample predictions. Second, the original model formulation makes use of speed quantiles as predictors of the linear model which are not readily available for the new sections. The method proposed estimates an auxiliary model, in which the RE of the original model are correlated to the RE to be defined for the new section, with the former being used to predict the latter. The RE pairs are modeled jointly, taking advantage of their potential mutual correlation. The model coefficients obtained are also validated using a jackknife technique. Results show that the method converges quite fast and that a handful of observations for the new road section are sufficient for good RE estimates.
机译：随机效果（RE）模型用于分析非独立性的数据或者数据的特征在于分层结构。在交通和公路工程中，RE模型已成功用于从包含根据不同级别自然嵌套的观察的数据估算自由流速分布（即方向，部分，道路）。在城市动脉和农村双车道高速公路上进行的实证研究表明，通过适当核算调查设计的RE模型优于传统的固定效果（FE）型号。然而，RE模型是不可转让的，因为属于不同网络的不同网络或道路的道路或道路部分的未知RE值，其不原始用于开发模型。在本文中，研究了RE模型对不在用于模型估计的原始样品的道路部分的可转换性，假设对于这些附加部分来说，可以获得或可以收集或可以收集。这个问题带来了两个挑战。首先，应估计新路段的随机效果以便进行样本预测。其次，原始模型配方利用速度量级作为线性模型的预测因素，这些模型不容易获得新部分。该方法提出估计辅助模型，其中原始模型的RE与用于为新部分定义的RE相关联，前者用于预测后者。重新对共同建模，利用它们的潜在相互关联。使用千刀技术还验证所获得的模型系数。结果表明，该方法收敛得非常快，并且对新路段的少数观察足以使得良好的RE估计。

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