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.
Highlights
Understanding road users’ receptivity towards fully automated vehicles, is paramount prior to their adoption in urban transport networks
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 beta hurdle regression and the truncated lognormal regression models were estimated against the empirical data and their performances were compared to assess the suitability of the hurdle setting
Summary
Understanding road users’ receptivity towards fully automated vehicles, is paramount prior to their adoption in urban transport networks. Many studies have investigated such receptivity by designing hypothetical experiments and applying psychosocial theories such as the Technology Acceptance Model (Davis, 1985; Davis et al, 1989), the Theory of Planned Behaviour (Ajzen, 1991), and the Unified Theory of Acceptance and Usage of Technology (Venkatesh et al, 2012) in order to identify the behavioural factors that are associated with the intention to use fully automated vehicles in the near future. Moody et al (2020) surveyed a large number of respondents across 51 countries and found that age, gender, education level, employment status, average household income, and awareness of automated vehicles are associated with safety perceptions towards these vehicles
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