How much should a pedestrian be fined for intentionally blocking a fully automated vehicle? A random parameters beta hurdle model with heterogeneity in the variance of the beta distribution

Abstract

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.