Identifying the factors affecting the use and adoption of urban air mobility

Transportation systems in urban environments are facing an increasing number of challenges. Growing mobility demand imposes additional pressure on existing infrastructure and public transportation systems, leading to the need for a shift towards sustainable transportation (Rodrigue et al., 2016). The deployment of shared mobility services are providing users with more efficient travel, characterized by a lower demand for parking spaces, lower vehicle ownership, but also reduced environmental impacts resulting from lower emissions (Baptista et al., 2014). At the same time, autonomous vehicles promise safe and comfortable transportation, and most automobile manufacturers are likely to launch fully autonomous vehicles in the coming decade (Bimbraw, 2015). This trend leads to a research interest in ground shared autonomous mobility (Fagnant & Kockelman, 2014) and possibly to the exploration of the third dimension: the skyscape. The latter has gained a growing interest, notably observed in the so-called“urban air mobility (UAM)” research community. Different business models for the service have emerged; for instance, Airbus (2017) introduced UAM as the on-demand sharing mobility operated by fully-automated vertical take-off and landing aircraft (VTOL) for intra-city passenger trips. Still, this mobility concept is constrained to many aspects related to regulations, infrastructure availability, air traffic control, environmental impacts, but also community acceptance (Vascik, 2017). Relevant research is focusing on different aspects of urban air mobility; for instance, modeling the service using agent-based simulation (Rothfeld et al., 2018) or understanding its implications and the changes it brings on the inhabitants and the city (Straubinger & Verhoef, 2018). However, compared to ground autonomous vehicles, only little has been done to investigate the acceptance of this mobility service. Recently, Fu (2018) conducted a study in Munich on passenger adoption through choice modeling in a UAM environment. In an attempt to expand research on UAM, this study aims to identify the factors that affect the use and adoption of UAM outside a mode choice context, to be able to develop a framework for UAM acceptance in general and its time adoption in particular. The investigation is based on the development of a novel stated preference survey design with time adoption as a dependent variable. The designed survey aims to reveal significant factors in the adoption of this service and includes common factors in technology and automation adoption, mostly from ground autonomous vehicles acceptance studies. First, a pilot study is conducted to gain useful insights on potential biases in the survey design, which are then incorporated in the final survey that was published online. The survey was publicly available for two months starting the 18th of July 2018. The survey was structured in four parts, with a total of 31 questions (or question groups) and required around 10 to 15 minutes to be completed. The first and last parts of the survey targeted respondents’ travel behavior and socio-demographics, respectively. In the second part, UAM was introduced by presenting some of its properties found in the pertinent literature. At the end of this part, the stated preference question on the intended time adoption was presented and respondents were asked to state when they were most likely going to use UAM. Alternatives included options ranging from the first year of operation (Y1), the second or third years of operation (Y2-Y3), the fourth or fifth year of operation (Y4-Y5), 1A question consisted sometimes of a matrix including several agreement statements, focusing on one attribute for instance.