Intentionally blocking the path of fully automated vehicles is an important dimension of pedestrians’ receptivity towards these vehicles. The monetary value of this behaviour can be obtained by asking pedestrians about their perception of the “fine” for blocking the path of a fully automated vehicle. Econometric modelling of the reported fine can shed more light on factors influencing pedestrians’ receptivity towards fully automated vehicles. However, development of such an econometric model is not straightforward due to the unique characteristics of the dependent variable: it has two fundamentally different states; it is right-truncated; and it may be fat-tailed. Despite fairly extensive methodological advancements in econometric modelling of pedestrian behaviour, there is no model that can adequately explain these characteristics. While a beta distribution in a hurdle setting has the potential to address the above complexities, its applicability in dealing with limited dependent variables in transport applications has remained, by and large, unexplored.

This study aims to fill this gap by developing a new beta hurdle regression model that systematically considers the dual-state of a right-truncated dependent variable representing the fine associated with intentionally blocking a fully automated vehicle. The hypothesized model is empirically tested using data obtained from a survey administered in Queensland, Australia, and the results are compared with truncated lognormal, and truncated lognormal hurdle regression models. Results indicate that the hurdle models are superior to the non-hurdle model. The beta variant of the hurdle model provides a better statistical fit for the data that are near their right limit. In addition, parametrizing the variance of the beta distribution captures the additional heterogeneity in the data. Age, gender, education level, violations, attitudes, behaviours that appease social interactions, and perceived ease or difficulty of interacting with fully automated vehicles influence the likelihood and/or the propensity of the fine and thus are associated with the perceived monetary value of intentionally blocking the path of a fully automated vehicle.

故意阻挡全自动车辆的路径是行人对这些车辆的接受度的一个重要方面。这种行为的货币价值可以通过询问行人他们对阻塞全自动车辆路径的“罚款”的看法来获得。报告罚款的计量经济学模型可以更清楚地了解影响行人对全自动车辆的接受度的因素。然而，由于因变量的独特特征，这种计量经济模型的发展并不简单：它有两种根本不同的状态；它被右截断；它可能是肥尾的。尽管在行人行为的计量经济学建模方面取得了相当广泛的方法论进步，但没有模型可以充分解释这些特征。

本研究旨在通过开发一种新的 beta 障碍回归模型来填补这一空白，该模型系统地考虑了右截断因变量的双重状态，该因变量表示与故意阻塞全自动车辆相关的罚款。使用从澳大利亚昆士兰州进行的调查中获得的数据对假设模型进行了实证检验，并将结果与​​截断对数正态回归模型和截断对数正态障碍回归模型进行了比较。结果表明，跨栏模型优于非跨栏模型。障碍模型的 beta 变体为接近其正确极限的数据提供了更好的统计拟合。此外，参数化 beta 分布的方差可以捕获数据中的额外异质性。年龄、性别、教育程度、违规行为、态度、