The potential of mobility as a service bundles as a mobility management tool

Mobility management or demand management, commonly referred to as soft measures, aims to alter the way people perceive travel alternatives as opposed to physically altering the options themselves (Headicar 2009). While hard measures modify the objective environment, for example through infrastructure improvements or prohibiting the use of cars of certain streets, soft measures seek to change people’s perceptions of the available choices (Bamberg et al. 2010). As stated by Borg (2004), whether travelers are ‘able’ to take a certain transport mode is determined by availability and individual constraints; while whether they ‘want’ to take a certain mode is determined by information, perception and subjective preference. This means that there is potential to influence people’s behavior without physically changing the objective conditions. Mobility management strategies can take the form of carrots, that provide a reward for exhibiting desired travel behavior, or sticks, that use disincentives to discourage undesirable travel behavior (Meyer 1999). However, research has shown that carrots and sticks are frequently used in combination as this can provide more effective results (Piatkowski et al. 2017). Some examples include information campaigns, travel awareness campaigns, discounted travel passes, travel plans and travel feedback mechanisms (Bamberg et al. 2010; Tornbald et al. 2014; Cairns et al. 2004).

Soft measures can be used to support a number of transport policy initiatives and can be implemented alongside hard measures or on their own. One important aim of soft measures is decrease private vehicle dependence and promote sustainable travel (Richter et al. 2011; Cairns et al. 2008; Taylor and Ampt 2003). To be successful at promoting sustainability, mobility management campaigns need to be centered around social motivation and making sustainable transport behavior the norm (Hiselius and Rosqvist 2016). For those who use their private vehicles, it endeavors to make alternative options more appealing. For example, a review of 19 studies by Scheepers et al. (2014) concluded that soft measures have positive results in shifting people from cars to active modes in nearly all studies. However, once travelers have discovered the convenience of car driving it is difficult to alter this behavior (Headicar 2009). As a result, these measures play a key role in keeping people who have not yet developed private car dependence from doing so. Although some argue that soft measures on their own may not be enough to significantly reduce private vehicle use, and as a result, CO₂ emissions (Möser and Bamberg 2008; Santos et al. 2010), others provide strong evidence of the opposite. Studies have shown that mobility management can result in a 5–15% reduction in car trips sustained over time (Brog et al. 2009). For example, in an application of personalized travel planning in Perth, vehicle kilometers decreased by 17% (Brög 2002).

Ticketing schemes, when passengers can buy bulk tickets, passes or cards for public transport or in some cases across modes, are also a mobility management tools (Cairns et al. 2004; Kamargianni et al. 2016). These, together with smart cards, have proven to have a significant impact on the use of public transport modes. When the new inter-modal Travelcard season ticket was introduced in London, car use decreased by 9% while bus and underground trips were up by 7% (White 1984). This suggests a shift from car to the public transport modes. Similar trends were observed in other areas, for example, Paris’ Carte Orange increased overall ridership by 33% (NEA 2003). Scott and Axhausen (Scott and Axhausen 2006) found that as the number of season tickets increased, car ownership decreased in Germany. This means, that there is a strong substitution between these two mobility tools. Further, introducing smart cards has also had very positive effects on public transport patronage by easing the transitions at stations as well as the payments (Blythe and Holm 2002; Prakasam 2009).

The presented literature shows how several aspects of mobility management have already been studied. There are several characteristics of the Mobility as a Service concept that make it fall under the mobility management umbrella term. For example, the personalized multimodal journey planner can provide information about the available options to users; and the integration of all modes can provide a viable and attractive alternative to private vehicles (without actually altering the physical system or adding any new modes; Kamargianni and Matyas 2017). In this paper, we focus on the MaaS subscription plans, which by bundling various modes together, can alter users’ perceptions about what is available to them. To our knowledge, this has not yet been addressed in the literature.

Bundling is a marketing method by which products and/or services are sold together as a single product at a special price (Guiltinan 1987). It is a very common method used to support diffusion of new or underutilized products and services. The shared services discussed in the context of MaaS fall under this second category; despite the fact that they have been around for a while, awareness of them and their usage is still limited. For example, even though the case study city of this paper, London, is very open to innovative ideas, 42% of our sample is not aware of what car sharing is. As such, methods to support their further adoption would be beneficial to the industry.

Countless studies have shown that bundling products and services increases consumers’ acceptance and willingness to pay for the elements of the bundles (Eppen et al. 1991; Stremersch and Tellis 2002). In many industries, from telecommunications to medical devices, bundles are often created with complementary products, which as a whole offer added-value to consumers. These ‘solution bundles’ can be more competitive than the standalone products (Cusumano et al. 2015). However, this has not been studied yet in the case of MaaS product bundles. Looking at the MaaS case, skeptics can argue that the transportation modes being bundled are substitutes, thus it is not a ‘solution bundle’. When we look at a single leg of a trip, this may be true as you could, for example, either take the train from station A to B or a taxi. When we look at overall mobility, in fact, quite the opposite is true. Public transport may provide solutions in some situations, while taxi in other situations. These can even be in a single trip from origin to destination. We argue that all the modes offered via MaaS together can be substitutes for private vehicles.

When it comes to unknown products and services, offering them together with ones that are familiar to the customer results in consumers evaluating the new product more favorably (Reinders et al. 2010). Also, by bundling lesser known and trusted products together with ones that are already part of consumers’ usage patterns, users’ perceptions of risk associated with the new product decreases. For example, Sarin and Sego (2003) show, that bundling high-tech products decreases consumers’ perception of risk, which in turn results in increased acceptance of the products. In the case of MaaS, even though shared services are gaining wider acceptance, their use could be accelerated by bundling them together with more popular modes such as public transport and taxi. Further, people who would most likely not be willing to ‘go out of their way’ to try shared services, could be introduced to them in a simple manner.

While the potential of bundling to increase consumers’ acceptance of lesser known or currently underutilized products has been widely studied, this has not yet been done in the case of MaaS bundles. This paper aims to add to the existing literature on bundling by providing initial insights into a product bundle that to the knowledge of the authors has not yet been examined.

To start our analysis, a bundle preference model is created with the whole sample (3769 SP observations). The core variables in the model are the transport modes that are included in the plans. Bike sharing is entered into the model as a dummy variable (1 = included, 0 = not included), as this transport mode attribute takes only these two levels in our SP. The public transport mode attribute is split into two dummy variables: 1. bus pass (unlimited access to buses), and 2. travelcard (London’s public transport pass that allows unlimited travel on all modes). Car sharing is split into two continuous variables: 1. car sharing hours, and 2. car sharing days. This is done because car sharing services in London charge by the hour and the day. Also, people perceive these two durations differently (car sharing hours is for short trips, while days is more like the traditional car rental for longer trips). In addition, a dummy variable for car sharing is also included, to help understand the overall preference for car sharing. The last transport mode, taxi, is entered into the model as a continuous variable (distance based). The coefficients for the modes are generic as there are no significant differences between respondent preferences if entered into each alternative separately. This was tested by running models with alternative specific constants and examining the sign and significance of the coefficients. This is what we would expect as the plan names themselves do not carry any meaning.

In addition, systematic taste variations are included via interactions between the attributes of the MaaS plans and socio-demographic variables. A number of individual characteristics were tested including age, gender, employment status (e.g. full time employed, student) education level, household composition (e.g. children in the household) and current mobility patterns (mobility tool ownership and use); however, only a few proved significant.

The results of the MMNL choice model are presented in Table 3 and were estimated in Pythonbiogeme v2.6 (Bierlaire 2016).

Firstly, we obtain a highly significant coefficient for the standard deviation of the random panel effect σ_panel, which means that this model allows for capturing intrinsic correlations among observations of the same individual.

Turning to the plan characteristics, as presumed, the cost coefficient is negative and statistically significant at the 99% confidence level. This means that as plans become more expensive, people prefer them less. Regarding the transport modes in the plans, all of them are statistically significant. This shows that the type- and amount of transport modes in the plans are important to users and should be carefully considered in the design of plans. Only the public transport options (bus pass and travelcard) have positive coefficients, meaning that people prefer plans when one of these are included (in the SP design, these were mutually exclusive). This result shows two things. First, it demonstrates the importance of public transport as the backbone of MaaS. Second, it supports the fact that the public transport system in London has very good coverage and is very popular among the city’s residents.

Looking at the other modes, all of the remaining coefficients are negative. For the dummy variables bike sharing and car sharing, this means, that when these modes are included, people tend to prefer them less. For the continuous variables car sharing day, car sharing hour and taxi, this means that the more that is included of these modes, the fewer people will prefer them. The fact that only the public transport options are preferred may seem alarming at first to those questioning MaaS plans; however, this result is not unexpected, in a city where 54% of overall journeys are conducted with public transport (excluding walking; Transport for London 2015). It is well known that travel is a habitual behavior and it has been shown many times that much of travel behavior is driven by pure repetition and habit rather than by conscious deliberation (Schlich and Axhausen 2004; Klöckner and Matthies 2004; Friedrichsmeier et al. 2013). This same idea can also be seen if we look at the interaction terms between the respondent’s current mobility patterns and the modes in the plans. For example, the interaction term between travelcard ownership and travelcard in the plan is positive and significant. This means, that those respondents who currently own travelcards prefer to have these in their plans more than those respondents who currently do not own one. Along the same lines, a variable about the frequency of taxi use is interacted with taxi. A frequent taxi user was defined as someone who uses taxi at least once a week. The interaction term is positive and significant at the 99% confidence level; that is, people who use taxi frequently prefer to have more taxi in their plans compared to those who use taxi less. In addition, two variables were interacted with bike sharing. When household cycle ownership is interacted with bike sharing, the coefficient is positive and significant. This means that those people who have bikes in their households prefer to have bike sharing in their plans more than those people who do not own bicycles. The same positive and significant coefficient can be seen when the variable ‘previous use of Santander Cycles’ is interacted with bike sharing in the plans. This indicates that those who have previously used Santander Cycles (London’s bike sharing scheme) prefer to have bike sharing in their plans more than those who have not used this service before.

Finally, out of all of the tested socio-demographic characteristics, only three proved to create significant differences between preferences for modes included in the MaaS plans. The variable ‘household income under £25,000’ is interacted with bus pass, and its coefficient is positive and significant. This means, that people with lower household incomes prefer to have bus passes more than those with higher incomes. The explanation for this is that bus passes are much less expensive than the other public transport option, travelcards, but at the same time have similar coverage. Individuals with lower household incomes have a lower willingness to pay for transport and a higher willingness to accept increased travel time that comes with using buses. Next, the ‘over 65’ variable is interacted with both bike sharing and taxi and both are significant and negative (although the latter only at 90% confidence). This indicates, that people over 65 gain less utility from both bike sharing and taxi in their plans compared to younger people.

Next, the same model was estimated with only those people who indicated that they would subscribe or consider subscribing to their chosen plan. These people were identified via a follow-up question after each SP task, asking whether they would subscribe to their chosen plan. In total, 403 respondents (in 834 choice tasks) indicated that they would definitely purchase or consider purchasing their chosen MaaS. By focusing solely on this subsample, we can identify whether the preferences outlined above for the whole sample persist or change when we only look at those who declared that they would purchase (or consider purchasing) these plans. The model results are presented in Table 4.

Next, we take a closer look at the presence of shared modes in the plans that respondents would subscribe to. Overall, 83.5% of the chosen plans include car sharing (either day or hour) while 46% had bike sharing in them. Dissecting the car sharing into days and hours, car sharing hours is the more popular choice with 68% of people choosing a plan with this in it, while car sharing days was only chosen by 28.1% of respondents. 15.3% chose plans with only car sharing days in them, while 55.4% chose plans with only car sharing hours in them. This shows that car sharing hours seems to be more attractive to potential MaaS users. The reasons behind this may include: (1) having more need for a car only for a couple of hours at a time rather than a whole day; and (2) the increased cost from hiring a car for a full day or days increase the cost of the plans too much. Only 8.6% of chosen plans did not have bike or car sharing in them.

Comparing the results to the prior behavior of these respondents (please note, that at this point we are only talking about those who would definitely subscribe or consider subscribing to their chosen plan) as little as 8.9% of them had car sharing memberships and only 57% were even aware of car sharing before. Looking at bike sharing, 30.4% of these respondents had used Santander cycles before (this includes those who have only used it a couple of times) and 5.4% had a yearly pass to it. This means, that many people who previously did not use car sharing and bike sharing would be willing to purchase MaaS plans with these modes in them. These results are summarized in Table 5.

This so far is promising, but a further question needs to be addressed: will these people actually start using these modes or just purchase access to them and not use them? To help answer this, an attitudinal statement was added after the SP experiments, to gain an insight into the overall outlook of respondents to trying new modes. Out of the 403 respondents who, in at least one SP task, indicated that they would subscribe or consider subscribing to their chosen plan, 64% responded positively to the statement “I would be willing to try transport modes I previously didn’t use if my MaaS plan included them”. 12% strongly agreed, while only 4% strongly disagreed. The breakdown of responses can be seen in Fig. 2.

The results provide some encouraging insights into the potential role that MaaS plans (bundles) can have as mobility management tools to help expose more travelers to shared modes. However, the results do need to be interpreted with caution as these are responses to a survey, rather than an actual implementation. Survey bias, which results from the hypothetical nature of questionnaires, tends to skew results. In this case, for example, some respondents may get overly excited about MaaS and say they would purchase their chosen plan, but in reality, if they would actually need to pay, they may hesitate.