A decomposition method for estimating recursive logit based route choice models
A decomposition method for estimating recursive logit based route choice models
复制标题
一种估计基于递归logit的路径选择模型的分解方法
DOI：
10.1007/s13676-016-0102-3
复制
发表时间：
2016
影响因子：
2.4
通讯作者：
Emma Frejinger
中科院分区：
文献类型：
--
作者：
Tien Mai;Fabian Bastin;Emma Frejinger
Fosgerau等人（2013）最近提出了路径选择问题的递归logit （RL）模型，该模型可以一致估计，并且易于用于预测，而无需对选择集进行采样。然而，它的估计需要求解许多大规模的线性方程组，这对于实际数据集来说可能是计算成本很高的。我们设计了一种分解（DeC）方法，以减少要解决的线性系统的数量，从而为估计更复杂的基于RL的模型（例如混合RL模型）提供了可能性。我们通过在两个超过7000和40000链路的网络上估计RL模型来测试DeC方法的性能，我们表明DeC方法显着减少了估计时间。我们还使用DeC方法来估计两个混合RL规范，一个使用随机系数，一个包含与子网相关的误差分量（Frejinger和Bierlaire 2007）。模型在实际网络上进行了估计，并进行了交叉验证研究。结果表明，用DeC方法可以在合理的时间内对混合RL模型进行估计。这些模型产生了合理的参数估计，样本内和样本外的拟合明显优于RL模型。
Fosgerau et al. (2013) recently proposed the recursive logit (RL) model for route choice problems, that can be consistently estimated and easily used for prediction without any sampling of choice sets. Its estimation however requires solving many large-scale systems of linear equations, which can be computationally costly for real data sets. We design a decomposition (DeC) method in order to reduce the number of linear systems to be solved, opening the possibility to estimate more complex RL based models, for instance mixed RL models. We test the performance of the DeC method by estimating the RL model on two networks of more than 7000 and 40,000 links, and we show that the DeC method significantly reduces the estimation time. We also use the DeC method to estimate two mixed RL specifications, one using random coefficients and one incorporating error components associated with subnetworks (Frejinger and Bierlaire 2007). The models are estimated on a real network and a cross-validation study is performed. The results suggest that the mixed RL models can be estimated in a reasonable time with the DeC method. These models yield sensible parameter estimates and the in-sample and out-of sample fits are significantly better than the RL model.