Congestion levels in protected areas can be predicted by destination choice models estimated from choice data. There is growing evidence of subjects’ inattention to attributes in choice experiments. We estimate an attribute non-attendance latent class–random parameters model (LC–RPL) that jointly handles inattention and preference heterogeneity. We use data from a choice experiment designed to elicit visitors’ preferences towards sustainable management of a protected area in the Italian Alps. Results show that the LC–RPL model produces improvements in model fit and reductions in the implied rate of inattention, as compared to traditional approaches. Implications of results for park management authorities are discussed.

中文翻译：

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保护区的拥堵管理：在陈述的选择数据中考虑受访者的疏忽和偏好异质性

保护区的拥堵程度可以通过根据选择数据估计的目的地选择模型来预测。越来越多的证据表明受试者在选择实验中不注意属性。我们估计了一个属性不参与潜在类-随机参数模型（LC-RPL），它共同处理注意力不集中和偏好异质性。我们使用来自选择实验的数据，该实验旨在引起游客对意大利阿尔卑斯山保护区可持续管理的偏好。结果表明，与传统方法相比，LC-RPL 模型改善了模型拟合并降低了隐含的注意力不集中率。讨论了结果对公园管理当局的影响。