Recently introduced regret-based choice models in transportation research have mainly adopted the assumption of identically and independently distributed unobserved regrets. The central argument underlying this paper is that this assumption is difficult to defend considering the fundamental nature of the concept of regret, which states that regret is generated through the comparison of choice alternatives on an attribute-by-attribute basis. To support this stance, we identify and diagnose specification errors in classic regret-based choice models, and provide an alternative specification. Results show that relaxing the assumption of identically and independently distributed error terms and applying the proposed error components Frechit structure leads to a substantial improvement in model performance of the regret-based models for the data used.

中文翻译：

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随机后悔选择模型中的误差分布假设：走向误差 Frechit 规范

最近在交通研究中引入的基于后悔的选择模型主要采用相同且独立分布的未观察到的后悔的假设。本文的核心论点是，考虑到后悔概念的基本性质，这一假设难以捍卫，该概念表明后悔是通过在逐个属性的基础上比较选择选项而产生的。为了支持这一立场，我们在经典的基于后悔的选择模型中识别和诊断规范错误，并提供替代规范。结果表明，放宽相同且独立分布的误差项的假设并应用所提出的误差分量 Frechit 结构可以显着提高基于遗憾的模型对所用数据的模型性能。