Are we willing to relocate with the future introduction of flying cars? An exploratory empirical analysis of public perceptions in the United States

Abstract

Due to recent technological advancements, flying cars are expected to be introduced in the near future, and to offer flexible mobility patterns, as well as shorter and more reliable travel times. This paper aims to analyse whether a residence relocation trend (from urban to rural areas, or vice versa) is imminent if flying cars are introduced.  In this respect, responses of 584 individuals from the United States were collected through an online survey. The resulting data are statistically analysed through correlated grouped random parameters bivariate and univariate probit models, while accounting for unobserved heterogeneity. The analysis reveals that various socio-demographic characteristics and opinions of the individuals towards the perceived benefits and challenges of flying cars influence residence relocation consideration. The findings from this study offer early insights into the travel demand, land use, and urban and regional planning related challenges that may emerge from the future introduction of flying cars.

KEYWORDS:

• Residence relocation
• flying cars
• bivariate probit models
• binary probit models
• urban air mobility
• advanced air mobility
• unobserved heterogeneity

Acknowledgements

The authors would like to thank the editor and the five anonymous reviewers, whose comments have helped significantly in improving this paper. The contents of this paper reflect the views of the authors, who are responsible for the facts and the accuracy of the data presented herein. The contents do not necessarily reflect the official views or policies of any agency, nor do the contents constitute a standard, specification, or regulation.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 It should be noted that the pairs of dependent variables for the bivariate probit model were selected based on correlation matrix retrieved from a multivariate probit model. Pairs with higher correlation coefficient were selected over others. This correlation matrix is presented in table A1.