Who uses shared mobility solutions and what is its future growth potential?

Although shared mobility solutions are very diverse, existing work indicates that their ‘typical’ users have some key common features: they tend to be well-educated young adults living in urban areas; who don’t (yet) have children and own fewer cars per household. However, the number of studies is very limited and they are sometimes based on small samples that are not always statistically representative. Most tend to focus on the profiles of existing users. Moreover, all studies of shared mobility are vulnerable to self-selection bias. Not correcting for these biases may lead to an overestimation of the future growth potential of shared mobility.

Over the last decade, most shared mobility solutions have experienced spectacular growth (albeit from a low base). One of the burning questions in mobility policy is now whether the quantum leap that has been made possible with mobile apps and increasing modelling capacity has already reached the entire possible target population, or just the “early adopters”, who will be followed in due course by a broader audience.

The socio-economic profiles of the users of other types of carsharing appear to be broadly similar, but the evidence base is less reliable, especially in the case of peer-to-peer users (see Le Vine et al., 2014; Steer Davies Gleave, 2015; and Kopp et al., 2015).

The “young” profile of carsharing users is often referred to in concert with indications of a downward trend of car ownership and use amongst young adults, at least in some countries. However, Le Vine et al. (2014) emphasize that they “are unaware of unambiguous evidence showing shifts in young adults’ consumption preferences relating to cars”. Instead, they refer to structural changes in the constraints young adults face (such as the fallout from the financial crisis).

In turns out that the users of bikesharing schemes have socio-demographic profiles that are very similar to those of round-trip car sharers: they tend to be highly educated, young city dwellers who own fewer cars per household (Martin and Shaheen, 2014).

Broadly similar results have also been found for the users of “ridesourcing” services in San Francisco (Rayle et al., 2016) and for the population of the US as a whole (Pew Research Centre, 2016).

The brevity of this survey also indicates its most important limitation – the number of studies is very limited (see also Fishman, 2016). Some studies are based on small samples that are not always statistically representative, and many tend to focus on the profiles of the people who already use the services, and do not consider the profiles of the people who do not use them.

Moreover, all studies of shared mobility are vulnerable to self-selection bias. For instance, with shared cars and bicycles, it is the location of the vehicles (mostly in densely populated areas or close to public transport hubs or major employment centres) that may determine the profiles of the users, rather than the other way around (see Marsden et al., 2015). Obviously, suppliers will tend to focus on areas with a high potential customer base.

Not correcting for these biases “may lead to overestimating the effect that carsharing would have on travel behaviour if adopted by someone without those propensities, i.e. if policies promoting carsharing led to its adoption across a broader segment of society” (Mishra et al., 2015). In more general terms, it is likely that members of car-sharing schemes already displayed different travel behaviours to the average population before joining the scheme, and the observed changes are therefore difficult to attribute (Kopp et al., 2015).

There is thus a huge need for better understanding the motives for using (and not using) shared mobility.