论文标题
具有选择厌恶及其应用于运输网络的递归logit模型
A Recursive Logit Model with Choice Aversion and Its Application to Transportation Networks
论文作者
论文摘要
我们提出了一个递归的logit模型，该模型通过强加一个罚款术语来捕获选择厌恶的概念，以说明运输网络每个节点的选择设置的维度。我们做出三项贡献。首先，我们表明我们的模型克服了路线之间的相关问题，传统logit模型的常见陷阱，并且可以将选择厌恶模型视为这些模型的替代方法。其次，我们展示了我们的模型如何在路径选择概率中违反规律性。特别是，我们表明，删除网络中的边缘可能会降低现有路径的概率。最后，我们表明，在选择厌恶的存在下，将边缘添加到网络中可以使用户变得更糟。换句话说，可以在拥堵之外出现一种胸罩的悖论，并且可以通过衡量用户选择程度厌恶程度的参数来表征。我们通过在现实世界传输网络上捕获的GPS流量数据估算此参数来验证这些贡献。
We propose a recursive logit model which captures the notion of choice aversion by imposing a penalty term that accounts for the dimension of the choice set at each node of the transportation network. We make three contributions. First, we show that our model overcomes the correlation problem between routes, a common pitfall of traditional logit models, and that the choice aversion model can be seen as an alternative to these models. Second, we show how our model can generate violations of regularity in the path choice probabilities. In particular, we show that removing edges in the network may decrease the probability for existing paths. Finally, we show that under the presence of choice aversion, adding edges to the network can make users worse off. In other words, a type of Braess's paradox can emerge outside of congestion and can be characterized in terms of a parameter that measures users' degree of choice aversion. We validate these contributions by estimating this parameter over GPS traffic data captured on a real-world transportation network.
