A method of integrating correlation structures for a generalized recursive route choice model
A method of integrating correlation structures for a generalized recursive route choice model
复制标题
广义递归路径选择模型的关联结构整合方法
DOI：
10.1016/j.trb.2016.07.016
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发表时间：
2016
影响因子：
6.8
通讯作者：
Tien Mai
中科院分区：
文献类型：
--
作者：
Tien Mai
我们提出了一种方法来估计一个广义的递归路径选择模型。该模型推广了文献中其他现有的递归模型，即，（Fosgerau等人，2013 b; Mai等人，2015 c），同时更灵活，因为它允许在每个阶段选择网络多变量极值（网络MEV）模型的任何成员（Daly和Bierlaire，2006）。广义模型的估计需要定义一个压缩映射和执行压缩迭代来求解贝尔曼方程。鉴于收缩映射是基于选择概率生成函数（CPGF）定义的事实（Fosgerau等人，2013 b），这些CPGF是复杂的，广义模型变得难以估计。我们处理这一挑战，提出了一种新的方法，网络的相关结构和结构参数的网络MEV模型集成到运输网络。该方法可以简化压缩映射，使估计实用的真实的data.We应用新的方法对真实的data.We提出了一个递归交叉嵌套logit（RCNL）模型，广义模型的成员，在每个阶段的选择模型是一个交叉嵌套logit。我们报告的估计结果和预测研究的基础上，一个真实的网络。结果表明，RCNL模型在拟合和预测方面明显优于其他递归模型。
We propose a way to estimate a generalized recursive route choice model. The model generalizes other existing recursive models in the literature, i.e., (Fosgerau et al., 2013b; Mai et al., 2015c), while being more flexible since it allows the choice at each stage to be any member of the network multivariate extreme value (network MEV) model (Daly and Bierlaire, 2006). The estimation of the generalized model requires defining a contraction mapping and performing contraction iterations to solve the Bellman’s equation. Given the fact that the contraction mapping is defined based on the choice probability generating functions (CPGF) (Fosgerau et al., 2013b) generated by the network MEV models, and these CPGFs are complicated, the generalized model becomes difficult to estimate. We deal with this challenge by proposing a novel method where the network of correlation structures and the structure parameters given by the network MEV models are integrated into the transport network. The approach allows to simplify the contraction mapping and to make the estimation practical on real data.We apply the new method on real data by proposing a recursive cross-nested logit (RCNL) model, a member of the generalized model, where the choice model at each stage is a cross-nested logit. We report estimation results and a prediction study based on a real network. The results show that the RCNL model performs significantly better than the other recursive models in fit and prediction.