Random utility maximization-based discrete choice models involve utility functions that are typically specified with explanatory variables representing alternative-specific attributes. It may be useful to specify some alternative-specific attributes as stochastic in situations when the analyst cannot accurately measure the attribute values considered by the decision maker. In addition, the parameters representing decision makers’ response to the attributes may have to be specified as stochastic to recognize response heterogeneity in the population. Ignoring either of these two sources of stochasticity can lead to biased parameter estimates and distorted willingness-to-pay estimates. Further, in some situations the analyst may not even have access to measurements of important alternative-specific attributes to include them in the utility specification. In this study, we explore the feasibility of simultaneously inferring alternative attributes and the corresponding coefficients, as well as stochasticity in both – without the help of external measurement data on alternative attributes – using mixed logit models on pooled revealed preference (RP) and stated preference (SP) choice datasets. To do so, we first theoretically examine parameter identifiability for different specifications and distributional forms of alternative attributes and their coefficients. Next, we illustrate this through simulation experiments in a travel mode choice setting and demonstrate the conditions under which pooled RP-SP data can help disentangle stochastic alternative attributes from random coefficients. In addition, an empirical application is presented in the context of commute mode choice in Bengaluru, India, to demonstrate the importance of recognizing stochasticity in mode-specific in-vehicle travel times along with the random coefficient on in-vehicle travel times.

中文翻译：

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使用合并的 RP-SP 选择数据来同时识别替代属性和这些属性的随机系数


基于随机效用最大化的离散选择模型涉及效用函数，这些效用函数通常用代表替代特定属性的解释变量来指定。当分析人员无法准确测量决策者考虑的属性值时，将某些替代特定属性指定为随机属性可能会很有用。此外，代表决策者对属性的响应的参数可能必须指定为随机的，以识别总体中响应的异质性。忽略这两个随机性来源中的任何一个都可能导致参数估计有偏差和支付意愿估计失真。此外，在某些情况下，分析人员甚至可能无法访问重要的替代特定属性的测量结果以将其包含在效用规范中。在本研究中，我们探索了同时推断替代属性和相应系数的可行性，以及两者的随机性（无需替代属性的外部测量数据的帮助），使用混合显示偏好（RP）和陈述偏好的混合逻辑模型（SP）选择数据集。为此，我们首先从理论上检查替代属性及其系数的不同规格和分布形式的参数可识别性。接下来，我们通过出行模式选择设置中的模拟实验来说明这一点，并演示汇集 RP-SP 数据可以帮助从随机系数中分离出随机替代属性的条件。 此外，在印度班加罗尔的通勤模式选择背景下提出了实证应用，以证明识别特定模式的车内出行时间的随机性以及车内出行时间的随机系数的重要性。