MaaS bundle design and implementation: Lessons from the Sydney MaaS trial

Abstract

A central feature of Mobility as a Service (MaaS) is the design of subscription plans, also known as mobility bundles. Despite the recognition of the importance of MaaS bundles compared to the Pay as you Go (PAYG) option, there is very little guidance in the literature on what a bundle that is attractive for users and financially viable for the operator might look like. With very few actual MaaS offers in real markets, and a lack of transparency in sharing how successful the few MaaS offers have been, the call for trials has grown throughout the world. The Sydney MaaS trial is the first in Australia to introduce MaaS bundles, using an incremental strategy of adding a bundle each month after a PAYG familiarity period. This paper sets out a framework within which we designed and introduced five bundles, using a co-creation and data-driven approach to bundle design. We present the findings on how successful bundles were in attracting MaaS users away from PAYG, and what this uptake might mean for achieving goals such as reduced transport emissions, notably those associated with private car use.

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MaaS bundle design and implementation: Lessons from the Sydney MaaS Trial
Chinh Q Ho
David A Hensher
Daniel J Reck
Institute of Transport and Logistics Studies
The University of Sydney Business School
The University of Sydney NSW 2006 Australia
Chinh.Ho@sydney.edu.au
David.Hensher@sydney.edu.au
Daniel.Reck@ivt.baug.ethz.ch
Sam Lorimer
Ivy Lu
Insurance Australia Group (IAG)
Sam.Lorimer@iag.com.au
Ivy.Lu@iag.com.au
Version: pre-print
Abstract
A central feature of Mobility as a Service (MaaS) is the design of subscription plans, also known as
mobility bundles. Despite the recognition of the importance of MaaS bundles compared to the Pay as
you Go (PAYG) option, there is very little guidance in the literature on what a bundle that is attractive
for users and financially viable for the operator might look like. With very few actual MaaS offers in
real markets, and a lack of transparency in sharing how successful the few MaaS offers have been, the
call for trials has grown throughout the world. The Sydney MaaS trial is the first in Australia to introduce
MaaS bundles, using an incremental strategy of adding a bundle each month after a PAYG familiarity
period. This paper sets out a framework within which we designed and introduced five bundles, using a
co-creation and data-driven approach to bundle design. We present the findings on how successful
bundles were in attracting MaaS users away from PAYG, and what this uptake might mean for achieving
goals such as reduced transport emissions, notably those associated with private car use.
Keywords: Mobility as a Service (MaaS), Bundle design, Mobility plans, Bundle uptake, Co-creation, Sydney
MaaS trial, sustainability outcomes.
Suggested citations: HO, C. Q., HENSHER, D. A., RECK, D. J., LORIMER, S. & LU, I. 2021b. MaaS
bundle design and implementation: Lessons from the Sydney MaaS trial. Transportation Research Part
A: Policy and Practice, 149, 339-376.
Acknowledgments: The Sydney MaaS Trial is a project of the iMove Cooperative Research Centre (CRC) Program.
The partners in the trial are the Institute of Transport and Logistics Studies (ITLS) at The University of Sydney
Business School, Insurance Australia Group (IAG) as the industry lead partner, Skedgo as the App developer, and
the iMove CRC. We are grateful for the contributions of other members of the project team, especially John Nelson
(ITLS), Corinne Mulley (ITLS), Camila Balbontin (ITLS), Goran Smith (ITLS and Chalmers University), Yale
Wong (ITLS), Andre Pinto (ITLS), Cecilia Warren (IAG), David Worldon (IAG), Brandon Liew (IAG), Corinne
Liew (IAG), Amanda Meier (IAG), David Duke (IAG), Hugh Saalmans (IAG), Brian Huang (Skedgo), Tim Doze
(Skedgo), and Claus von Hessberg (Skedgo). We thank three anonymous referees for extensive constructive
comments that have improved the paper significantly.

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1. Introduction
The interest in Mobility as a Service (MaaS) has garnered support from many stakeholders who see it
as offering a way to connect greater mobility choices for travellers with an attractive information-laden
framework. Powered by digital technology, mobility suppliers and those who bring them together
through some aggregation structure (typically acting as a broker) deliver the extended set of mobility
choices. MaaS is seen as an ecosystem that can, through appropriate incentive-based regulation, offer a
way forward for government and other interested parties to achieve a wide range of sustainability
objectives such as reducing transport emissions and private car ownership (Hensher et al., 2020, Smith
and Hensher, 2020). The Sydney MaaS trial, the first in Australia, has a number of objectives of which
sustainability in terms of private car usage and a test of commercial viability are prominent.
A necessary but not sufficient condition for achieving MaaS sustainability objectives is a digital
platform, typically in the form of a smart-phone app and its back-end database. This digital platform
provides a multi-modal journey planner capability but also offers information on ways of achieving
mobility outcomes that align well with customer preferences. Some digital platforms allow the users to
specify their preferences in terms of individual priorities of different travel attributes such as time, cost,
hassle, and emission. The last piece of evidence contributes to sustainability outcomes which may or
may not appeal to users.
At the centre of the MaaS ecosystem is a recognition that although an appropriate digital platform is
required, this is not enough to move MaaS beyond being an improved journey planner with a potentially
convenient one-stop shop for all mobility services a traveller may want. We argue that this is the case
even when the MaaS app can offer payment integration, but with a pay as you go (PAYG) option only,
with or without a personal mobility wallet function through which an integrated payment mechanism
may be available.1 There is emerging evidence that MaaS offering PAYG option only which is the only
option in the majority of digital platforms promoted as MaaS, is not enough to attract potential users,
and where it has, it is unlikely to change travel behaviour in ways that can benefit individuals and society
as a whole (Ho et al., 2018, Ho et al., 2020, Matyas and Kamargianni, 2018).
What is missing is a way to co-create different ways to make MaaS offers attractive in a way that it can
impact traveller behaviour and deliver desirable aggregate changes of the performance of the mobility
network. MaaS requires some structure that can be adjusted to respond to such opportunities. This
structure is a suite of subscription bundles that offer varying combinations of mobility services for a
given subscription fee. The subscription bundles can be designed and adapted to accommodate the
preferences and travel habits of potential subscribers while also achieving gains in key sustainability
goals of government and other socially committed groups. In addition, the way in which MaaS might
be delivered to the market must recognise the need for a structural outcome that aligns with the set of
objectives set by interested stakeholders, notably the business case in terms of a commercial outcome
or a net benefit outcome to society that is supported by public subsidy.
This paper sees subscription design, through the development and implementation of a range of bundles,
as the linchpin in testing various MaaS offerings on a journey to establish a business case in line with
agreed objectives. The Sydney MaaS trial, undertaken in 2019-2020, was designed to develop a way to
identify financially viable bundles and to test them in a revealed preference setting. The trial monitors
impacts and revises the bundle offers through a sequential incremental addition of complementary
bundles as more insights into travel behaviour and bundle uptake are obtained. Based on the growing
knowledge through data analysis and qualitative interviews as the trial progressed, further opportunities
are identified and additional bundles introduced to satisfy customer needs beyond that available under
PAYG and the existing bundles. While business factors and other instructional aspects are also relevant
to the development and implementation of MaaS (Karlsson et al., 2020), we focus on revealed consumer
1 Payment integration could be in the form of an account based payment tool or a digital smart-card stored on the
user’s smart-phone. An example of the latter is the digital Opal card in Sydney where the former is typically in a
form of a digital credit-card payment such as Apple Pay and Google Pay.

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preferences to design subscription bundles that improve uptake so that the trial objectives in terms of
sustainability and commercial viability can be evaluated. This paper describes this co-creation and data-
driven approach to bundle design in detail such that other trials may benefit from the lessons learnt from
the MaaS trial in Sydney, Australia.
Reflecting on the experience of designing and implementing bundles prior to the trial through stated
preference (SP) surveys (Ho et al 2018, 2020) and for the Sydney MaaS trial, we recognise that
designing bundles in an SP setting is relatively easier than in a revealed preference setting2. Despite the
learnings from SP surveys on potential interest in bundle take up, the transition to real market
implementation is complex and challenging. The transition is not simply the offer of appealing bundles
but the entire infrastructure required in integrating all facets of MaaS. To date there is very little in the
literature and reported real experience to guide us. For example, the most widely promoted MaaS offer
with bundles, Whim in Finland, has no publicly available documentation on the underlying theory and
methodology objectives, and assessment of how successful these bundles are, and why these bundles
were designed in the way they were3. This paper provides first-hand knowledge on how to design and
implement MaaS bundles, taking into consideration the spatial and technical settings of the MaaS offers.
This paper is structured as follows. Section 2 discusses the range of issues that need careful consideration
in bundle design and implementation. Section 3 summarises the practical challenges of implementing
bundles, while Section 4 outlines approaches to bundle design. Section 5 discusses mechanisms and
levers available to design MaaS bundles. This is followed by Section 6 that details the design of specific
bundles and the uptake of such bundles with a focus on the extent to which such bundles align with
customer preferences and broader societal goals. Section 7 summarise implementation experience in
areas such as platform design, customer enrolment, and invoicing, while Section 8 provides an overview
of bundle uptakes. We conclude the paper with a summary of lessons learnt and challenges going
forward.
2. Literature review
The literature on MaaS bundle design is dominated by studies employing stated preference methods,
while only a few studies employ revealed preference methods or indeed any other methods, and only a
few trials have been transparently reported upon. Here, we provide an overview of the main
contributions to identify the research gap we address, and refer the reader to a recent review on MaaS
bundle design (Reck et al., 2020) for further details.
First and foremost, it should be noted that bundling is not a new idea. For the most part, it originated in
the Marketing literature, though there is also a large literature in Economics and Law. Stremersch and
Tellis (2002) provide a comprehensive and widely cited review of the origins and main concepts of
bundling. They define bundling as “the sale of two or more separate products in one package” (p. 56)
and structure the field by defining ‘bundling focus’ and ‘bundling form’ as two relevant dimensions to
distinguish bundles. Bundling focus refers to the level of integration of the bundled products (e.g., price
bundles without any integration and product bundles with value-adding integration). Bundling form
refers to the breadth of the sales portfolio (e.g., does the firm only sell the bundle or does it also sell the
products separately?).
Reck et al. (2020) illustrate how mobility bundles, depending on their level of integration, fit into the
resulting framework for bundling in the Marketing literature. Mobility bundles are usually offered in
2 To clarify in a little more detail, we argue that designing bundles in terms of specifying what to include in a
bundle in an SP setting is probably as challenging as it is in an RP setting; however obtaining relevant information
from users is very different. This may be easier / more straightforward in an SP setting, however limited at the
same time due to the hypothetical bias. Collecting relevant RP data is much more challenging / complex but has
the potential to yield greater insight.
3 What is out there is in many ways arbitrary and appears to be ‘suck it and see’ in real applications.

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addition to stand-alone services (i.e., they are ‘mixed bundles’). Without any integration, they can be
considered as pure price bundles (e.g., WHIM offers allowances of separate services without any
integration). With higher levels of integration along operational, transactional and informational
dimensions, Mobility bundles can be considered as product bundles. Tripi in Sydney or Yumuv in Zurich
are two examples of services that offer intermodal apps to plan, book and pay for trips involving several
modes covered by the bundle.
While the Marketing terminology for bundling can (and should) be applied to bundles of mobility
services, the underlying motivations to bundle products and services differ between the classic
Marketing theory and Mobility as a Service. Classic Marketing theory motivates bundling as a tool for
revenue maximization in highly competitive markets (Ansari et al., 1996; Bakos and Brynjolfsson, 1999;
Kobayashi, 2005). Various mechanisms have been researched, such as exploiting consumer preference
heterogeneity and the interrelated nature of demand by offering various tariff structures (multipart
pricing) (Lambrecht et al., 2007, Iyengar et al., 2008, Köhler et al., 2014), decoupling transaction costs
and benefits to decrease the consumers’ likelihood to consume a paid-for service (Soman and Gourville,
2001), displaying price information in a favourable way for the profit-maximizing firm (Johnson et al.,
1999), the predictive value of bundling (Bakos and Brynjolfsson, 1999, 2000) and the optimal number
of items to be included in a bundle (Ansari et al., 1996).
Mobility as a Service, on the contrary, is overwhelmingly motivated as a concept to improve the
sustainability of transportation (Hensher and Mulley, 2020, Jittrapirom et al., 2017, Kamargianni et al.,
2016, Mulley, 2017, Wong et al., 2020), though it has been noted that service bundling can increase the
competitiveness of transportation services with a low market share (Panou et al, 2015). Historically, the
most prevalent form of bundling in transportation has thus been public transportation season tickets,
which are heavily subsidised to incentivize sustainable travel. In contrast to the Marketing literature, the
(most prevalent) reason to bundle is thus not profit maximization, but rather to influence travel behaviour
towards more sustainability. Previous research results in the Marketing literature on the optimality of
bundling to maximize revenue, while useful, are of limited application to designing mobility bundles
with a focus on societal value. Methodologically, however, research can very much build on Marketing.
Early research into mobility bundle design has employed stated preference surveys and discrete choice
modelling to explore potential uptake of and willingness to pay (WTP) for different bundle
configurations along the lines of Ben-Akiva and Greshenfeld (1998). Recent studies include, for
example, Caiati et al., 2020; Feneri et al., 2020; Guidon et al., 2020; Ho et al., 2018; Ho et al., 2020;
Matyas and Kamargianni, 2019a; Mulley et al., 2020; Polydoropoulou et al., 2020. Methodologically,
most authors present their study participants with a number of choice situations where bundle
configurations vary (e.g., by included transport modes or allowances) and subsequently model bundle
uptake using discrete choice models. One common finding is that uptake intention is correlated with
current mobility tool usage (Ho et al., 2018; Ho et al., 2020; Matyas and Kamargianni 2019b;
Polydoropoulou et al., 2020). While public transportation is preferred by most participants,
heterogeneity is much higher for shared modes. In particular, bundles containing taxi or ridesourcing
budgets only appeal to their current users (which are often in the minority of the samples resulting in
overall negative coefficients for these modes). While there appears to be a high general interest in buying
MaaS bundles and using MaaS apps (Ho et al., 2018; Matyas and Kamargianni, 2019a), only participants
in Zurich appear to be willing to pay for an app per se (Guidon et al., 2020; Ho et al., 2020).
Methodologically diverting from previous studies, Caiati et al. (2020) conducted a portfolio choice
experiment (Wiley and Timmermanns, 2009) where participants could design their own bundles
containing variations of modes, pricing schemes, geographical validity and extra features. They found
that price and social influence variables had a substantial effect on bundle uptake. Finally, taking a more
macroscopic approach, Esztergár-Kiss and Kerényi (2020) related city characteristics to bundle contents
and levels.
While SP studies were very useful early explorations of MaaS bundle design, they are subject to
hypothetical bias (Hensher, 2010). Thus, real-world trials are needed to test previous findings and
generate further insight. The earliest and probably most thoroughly documented MaaS trial is the UbiGo
trial conducted in Gothenburg in 2013/2014 (Sochor et al., 2015; Sochor et al., 2016; Strömberg et al.,

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2016; Strömberg et al., 2018). 83 households participated in the trial and could subscribe to customizable
MaaS bundles. Strömberg et al. (2016) and Sochor et al. (2015) conclude that the trial helped participants
to try out new transport modes and made multimodal travel less expensive and more convenient. By
means of interview analyses, the trial was found to promote usage of public transport and active modes
over private car use and induced changes in mode choice behaviour for 42% of the participants.
One issue that researchers and practitioners face when planning a new trial is how to design MaaS
bundles. Previous trials and studies have either used customizable portfolio choice approaches (Ubigo,
Caiati et al., 2020) or relied on pre-designed plans following the authors’ intuition. In a recent study,
Reck et al. (2020) synthesized ten fundamental design dimensions of MaaS bundles, providing
researchers and practitioners a framework within which to design new bundles. An open question,
however, is of a procedural nature: how to design MaaS bundles using revealed preference data?
To date, only two studies have started research in this direction. Liljamo et al. (2020) directly related
current mobility costs to willingness to pay for MaaS bundles using linear regression analysis. Reck and
Axhausen (2020) use tracking data to fit MaaS bundles to the mobility traces of a panel of Danish
students. In line with previous SP studies, they find that public transportation season tickets are a viable
core for MaaS bundles, fitting the majority of the samples’ travel patterns, while usage of shared modes
(carsharing, bikesharing, taxi) is too infrequent to include as recurring credit in MaaS plans.
While these studies provide first guidance for how to design MaaS bundles, they also remain
hypothetical, as participants in those studies never actually had to buy them. Hence, to our knowledge,
no one so far has designed and tested uptake in a real-world trial setting.
3. Factors to consider in bundle design and implementation
In designing and implementing MaaS bundles, an obvious starting point for MaaS integrators is a mix
of transport modes they can offer to their customers (i.e., MaaS users). This is because MaaS operates
on the concept of uniting existing transport modes to provide a better service to travellers. In this context,
Reck et al. (2020) offers a master design framework which distinguishes necessary design dimensions
from complementary design dimensions. Choosing which modes to include is the most important
dimension of a MaaS bundle design, in addition to other necessary dimensions including metrics (how
to measure quantity/entitlement), targeted customers (individuals or groups), subscription cycle
(fortnight, month, quarter, or year), and spatial coverage (or valid area).
3.1 Mix of transport modes
In the context of SP experiments, selecting a mix of transport modes to offer in a bundle is typically
driven by two factors: the availability of different modes in the study area, and the roles of these modes
in the local setting (e.g., trunk services vs. first and last mile services, transport modes for everyday
travel vs. weekend outings). There is, however, a marked difference between designing MaaS bundles
for SP experiments and designing bundles for real-world subscriptions. This is because an SP design
does not need to justify the ‘cost’ of including these modes against the marginal benefits of their
inclusion, whether this benefit is financial (e.g., improving the attractiveness of the bundle and hence
uptake) or societal (e.g., encouraging sustainable behaviour and hence reducing greenhouse gas
emissions).
Conversely, in real-world experiments or operation, the process of selecting which modes to include in
a MaaS bundle involves trade-offs and often long negotiations. First, negotiations with transport service
providers (TSP) must take place for each mode to be included in the MaaS platform. This negotiation
could mean obtaining an agreement with TSPs to resell their transport tickets in the case of public
transport to end-users, or require TSPs to provide a special arrangement for business-style accounts so
that bookings and payments for transport services made by MaaS users can be done automatically and
in bulk by a MaaS integrator. However, the negotiation may also require TSPs to provide a discount

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from the standard fare for bulk purchase such that financial levers are available to create attractive and
commercially viable MaaS bundles for subscription.
Once agreement has been obtained, TSP’s technical readiness presents a second hurdle to be addressed
before the corresponding mode could be included in the MaaS (bundle) offers.4 The required level of
technical readiness depends on the targeted level of technical and operational integration. For example,
the basic level of integration associated with informational integration across some transport modes does
not require much technical set up beyond an access to the TSP’s service timetable database or app
through, for example, an API gate (Application Programming Interface). Travellers in many
cities/countries have already enjoyed informational integration through free apps such as Moovit and
Google Maps. Aiming to offer MaaS users a fully integrated experience, however, requires significant
technical set up, and hence cost, to achieve not only informational integration but also operational and
transactional integrations across all modes of transport included in a MaaS offer (Lyons et al., 2019).
The highest level of integration additionally allows users to pre-pay for their travel by subscribing to a
mobility bundle that best suits their travel needs (Reck et al., 2020). These subscription bundles could
either be pre-defined by the MaaS integrator (e.g., Whim) or be co-created with the customers (e.g.
UbiGo). Also, mobility bundles can be designed with or without an integration of societal goals such as
reducing CO2 emissions (Sochor et al., 2018). In all forms and shapes, a fully integrated version of MaaS
should allow its users to search for the services they want, but also to book, pay, and manage their
account almost in real-time or near real-time, depending on how often the transaction database is
updated. We argue that only through this full integration with built-in bundles does MaaS become a
mainstream product, and hence can realise its potential to change travel behaviour towards more
sustainable travel (i.e., MaaS is used to achieve societal goals as promoted in Hensher, Ho, Mulley et
al. 2020).
Putting the above-mentioned considerations into practice, the Sydney MaaS trial starts with a long list
of transport modes that the participants are interested in using as part of the MaaS offers. These include
all public transport services available in Sydney (i.e., bus, train, ferry, light rail, metro, and on-demand
transport services accessible via Opal smartcard), taxi, ride-hailing/ride-sharing (Uber/UberPOOL), car-
sharing (GoGet, Car Next Door), car-rental (Thrifty, Hertz), bike-sharing (e-Lime/Mobike), and even
the private car5. Since the Sydney MaaS trial aims to reduce the negotiation time and cost by leveraging
existing relationships between Insurance Australia Group (AIG), the mobility integrator, and a wide
range of transport service providers, this long list of transport modes is shortlisted for inclusion in the
trial, with a focus on the modes where the trial has real prospects of being able to offer them to the
participants.6 The aim is to give the trial participants a ‘smorgasbord of transport options’ (Strömberg,
Karlssson and Sochor, 2018) for their travel needs, covering not only day-to-day travel such as
commuting but also first/last mile travels, regular weekend outings and shopping, and irregular trips
(e.g., returning home late at night, running late to a meeting).
The shortlisted modes include all public transport services accessible via the Opal smart-card system,
private car, Uber, Taxi, GoGet car-share, Thrifty car-rental, and Lime-E bike-sharing. IAG has existing
relationships with all TSPs, except for Opal, Lime-E, and private car. As public transport appears to be
the backbone of MaaS offers (Ramboll, 2019, Caiati et al., 2020, Guidon et al., 2019, Ho et al., 2020),
alternative methods to allow trial participants access to all PT services were discussed, including directly
involving Transport for NSW (the local transport authority), and Cubic (provider of the Opal ticketing
system) in the trial. After lengthy discussions, it was decided that the most feasible approach to including
4 Long negotiations and willingness to resell and the point on readiness are so important and probably take longest
to solve in real-world trials (and indeed can break them). For the Swiss MaaS trial, for example, the technical
implementation and deep integration of existing TSPs has taken more than a year which stands in stark contrast
with the dynamics of the market (i.e., one of the e-scooter providers went out of business in the meantime).
5 While the private car is typically out of scope in the MaaS definition, it may have a new role post-Covid-19 in
what Hensher describes as a ‘familiar sharer’ setting (Hensher 2020).
6 The point to emphasise is the huge gap between simple bundle design concepts (i.e., choose a couple of TSPs)
and practice. The negotiation process is lengthy and subject to failure with a number of suppliers.

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PT services in MaaS offers was that IAG create a corporate Opal account, issues Opal cards to the
participants, and link all those cards to the corporate account so that all PT trips made by the trial
participants were paid by the IAG corporate card (see payment integration below). As for bike-share,
negotiations were commenced with Lime-E but not Mobike. Unfortunately, the timing for Lime-E was
not good since the trial was scheduled to launch just a couple of months after Lime-E launched their
services in Sydney7. As a result, apart from walking and ride share, the Sydney MaaS trial was not able
to include in the offers an affordable micro-mobility mode of transport for the participants to access and
egress from PT services. This first and last mile issue is addressed by designing appropriate MaaS
bundles targeting participants who would use PT but being deterred by the distance to/from PT services
(see more details in Section 6).
Like PT services, private car use makes up a major part of everyday travel, and thus it was deemed
important to capture private car use during the trial so that the trial can answer one of the most critical
questions about MaaS; namely how MaaS may change private car use. Put it differently, should MaaS
be positioned as a complementary or supplementary service to the private car? Again, alternative
arrangements were considered, including incentivising car-owning participants to share their cars
through a car-sharing platform of Car Next Door for the duration of the trial, and reimbursing the sunk
cost of car ownership if the participants send the car keys in – the latter being similar to the Ubigo trial
(Sochor et al., 2016). Given the 6-month trial period, these arrangements did not appear to have much
appeal and hence were abandoned.
The adopted approach to including private car use in the trial relies on a complementary program called
Safer Journeys run through IAG before the MaaS trial was launched. Although the private car is not part
of the MaaS offering, data on its use is crucial in assessing the impact of MaaS on private car use. Safer
Journeys is a car-based program with GPS tracking technology installed to make car journeys safer, by
for example deploying an ambulance to the accident location if the driver did not answer the phone from
the call centre who recognises some sudden incidents that may have happened with the trip, based on
the tracking data. A by-product of this program is that private car use can be tracked and used as a
complementary data source to assess the success of the MaaS trial in terms of reducing emissions
through reduced car kilometres. All car-owning participants to the MaaS trial were asked whether they
were a member of the Safer Journeys program, and if not whether they would be interested in joining.
All safer journeys users who participated in the Maas trial consented to sharing their Safer Journeys data
with the Trial.
The final transport providers for integration and experiment during the trial are Opal (i.e., an integrated
ticket for all public transport services in Sydney, including bus, train, ferry, light rail, BRIDJ), Uber,
Taxi (Cabcharge), GoGet, and Thrifty car hire. Before these services could be included in bundle offers
for the participants to subscribe to, users must be provided with a means to search for their availability,
book, and pay for these services. The next section describes the tasks required to integrate these services
into the digital platform developed for the trial, the Tripi app to obtain the desired levels of search,
booking, and payment integrations. Before turning to the technical setup, however, it is worth discussing
briefly the other necessary dimensions associated with designing a MaaS bundle.
3.2 Other necessary design dimensions
Once the mix of transport modes/services to be included in a MaaS bundle has been finalised, we need
to consider other necessary dimensions of the MaaS bundle design. These include metrics, targeted
customers (individuals or groups), subscription cycle (fortnight, month, quarter, or year), and spatial
coverage (or valid area). Of these dimensions, choosing the appropriate metric to define subscriber’s
entitlements to each mobility service included in a MaaS bundle is most challenging because this
decision depends on not only the local context (e.g., how public transport fares are charged such as
distance-based, zone-based, flat fare, with or without daily/weekly cap) but also the mechanisms and
7 Lime-E was too busy to set up their own business and had little interest in joining the trial, given that it lasts for
only six months with a small number of participants.

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levers that the MaaS operator may have at their disposal to define a MaaS bundle. These are discussed
in detail in Section 5.
The design for the remaining necessary dimensions is much simpler as they are more or less defined by
the objectives of the trial and how it is run. Specifically, to obtain payment integration, the trial relies
on the corporate accounts that IAG has with various TSPs. To add the participants as members to these
corporate accounts, and hence obtaining payment integration, the participants must be IAG employees.
That is, the targeted customers are individuals instead of groups (the terms and conditions of the trial
detail this eligibility criterion and usage agreement). With respect to the subscription cycle, we chose a
monthly subscription to reduce the administration cost (i.e., processing data and issuing invoices) and
to simplify the payment for participants. Since all participants are IAG employees and the trial target is
to obtain a minimum of 100 participants, the monthly budget required from the trial to pay for all trips
made by the entire sample every month is not too large. Thus, a monthly cycle is chosen as a compromise
between the budget and administration costs. Finally, while the trial is offered to Sydney IAG
employees, the spatial coverage of the service is defined by the availability of each service with no
spatial boundary defined. For example, the spatial coverage of public transport services is defined by
the validity area of the Opal cards, which includes urban and inter-city travel by all public transport
modes. Similarly, participants can access GoGet car-share, Uber, Taxi and Thrifty rental car from
anywhere they find these services (i.e., Australia wide, except for Uber which is international and could
also be accessed in the trial).
3.3 Technical setup for desired levels of integration
To offer MaaS users an integrated experience, technical setup is required to integrate search, booking,
and payment into the same digital platform (i.e., Tripi) that MaaS users interact with. Search integration
means that when a user requests a trip between point A and point B, the app is able to check the location
of services from a provider and their estimated costs and time taken for the trip, and map out a route for
the user. This function can be described as a journey planner that many travellers have experienced with
and hence, typically considered as the most basic function of any MaaS app. Booking integration means
the user is able to seamlessly book a service (once found through the search function or through a
different method) to complete a trip from the app. Booking integration can be achieved via different
levels, in order of decreasing complexity as summarised in Figure 1, with a recognition of the extensions
that the trial has made highlighted in Figure 2 through extending previously reported frameworks on
MaaS integration levels by providing more detail in the booking and payment categories.
Figure 1: Booking integration options

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Figure 2: Extending previously reported frameworks on MaaS integration levels
Payment integration means the user is able to pay for the service they use in various ways (see Table 1
and Figure 1). Depending on the desired level of payment integration, this could be undertaken inside
or outside the MaaS app. Also, a payment may be required for every trip taken by the user; however,
from a user perspective, periodical payments such as weekly, fortnightly or monthly invoices may
improve user experience at the expense of billing shock. Table 1 summarises the level of integrations
that the TripGo app, which was used as the basis for the trial app, already provided for the services to
be included in the Sydney MaaS trial app, Tripi. As can be seen, while search integration was well
advanced, booking and payment integration were not offered via the TripGo app, except for Uber
booking. This meant that resources and technical setups were required to facilitate booking and payment
via the Tripi app. The next section describes the technical design implemented for the integration.
Figure 3: The increasing levels of payment integration

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Table 1 Tripi integration levels
Provider
Search integration
Booking integration
Payment integration
Opal
Yes
Not applicable (not required)
No
Uber
Yes
Yes
No
Cabcharge
Yes
No (deep link to GoCatch app)
No
GoGet
Yes
No (opens public GoGet website)
No
Thrifty
No
No (Opens generic hire-car
aggregator website)
No
3.4 Integration designs and implementations
3.4.1 Search integration design
Search integration is required for all providers except Thrifty car hire which is excluded from Tripi
because the trial participants may occasionally rent a car for long distance travel, and thus it does not
make sense to show this transport mode and its attributes for every search they may do. As for other
modes, integration already exists in the TripGo app (see Table 1). This allows users to see a list of
possible methods for a given trip from point A to point B via each provider, by knowing locations of the
services and their cost estimates for the trip.
3.4.2 Booking integration design
For this trial, we prioritised deep linking as the preferred method of providing bookings, as it maintains
a relatively seamless user experience without the high-cost and technical complexity involved with
creating and maintaining a direct integration to a transport provider’s booking stack. For public
transport, all participants were provided with an Opal card for all their public transport travel which
does not required bookings. For Uber booking, the TripGo app currently has a direct integration with
Uber but it does not support Uber Ride Profiles, which can be described as the ability of users to select
the appropriate account they have more than once (e.g., personal vs. business profile). The trial replaced
the direct integration with a deep link to the Uber app, where the destination is pre-populated, and the
MaaS Trial Ride Profile can be left as the default method of payment for participants. As for Taxi, Tripi
offered an anonymous linking to the GoCatch app for taxi bookings, in addition to a phone number
which the users can click to call a taxi. To book a GoGet car-shared, Tripi users simply open the GoGet
website or app and logs into their membership account. Finally, for car-rental booking, a web link is
provided from the Profile section for the participant to make a booking by filling out the information
required by Thrifty.
3.4.3 Payment integration design
For the trial, IAG is acting in the MaaS operator role, meaning all payments are facilitated via IAG. The
aim is to provide a seamless experience to participants where all of their trips with associated costs are
able to be shown to them via a mobility wallet function of Tripi, and they only need to make a single
payment to IAG to access all modes. The wallet tab of the app enables users to track all trips taken as
well as costs, mode use, date and time of trip start, start location, as well as the current credit/debit
balance (see Figure 4). Payments to private transport providers (i.e., Uber, Taxi, GoGet, Thrifty) are
facilitated by IAG holding the master account with each provider and passing on costs on a monthly
basis to participants via separate invoice during the live trial period.

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Figure 4: Illustrative mobility wallet screens
To pay for all public transport trips, Opal cards were procured by IAG for each participant and linked
to several Opal accounts controlled by IAG. To capture and reflect these trips in the mobility wallet, the
app developer provided an automated mechanism which log in and read all new trips taken by each Opal
card linked to these accounts and update the back-end portal on a regular basis. The study team has the
capability to manually add and edit trips in the back-end portal as well.
The system architecture for the integration of information, booking, payment, and contracting elements
of the Sydney MaaS trial is summarised in Figure 5. The integration of information, typically in the
form of timetable data, allows users to use the journey planner function of Tripi to search for mobility
services. For services that require booking, Tripi uses a deep linking method to facilitate booking
integration, while all payments are integrated through several master accounts held by the MaaS
operator, which allows centralised billing for all trips taken by the users. The trial integrates mobility
services through subscription contracts, where users can subscribe to a monthly bundle or stay with
PAYG.
Figure 5: System architecture for the integration of information, booking, payment, and contracting
elements in Tripi

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4. Approaches to MaaS bundle design
Very little knowledge exists to guide the real world design of MaaS bundles, especially when these are
offered to participants for subscription. However, emerging evidence from SP and RP studies (e.g., Reck
and Axhausen 2020) suggests that understanding travel demand is critical to obtain a certain level of
acceptance of MaaS bundles amongst the targeted customers. Without much understanding of the
targeted participants’ transport needs for everyday travel, we can adopt one of the following two
approaches to designing MaaS bundles.
The first approach requires a survey of the targeted customers to understand their transport needs and
their personal circumstances, including socio-demographics and the environmental setting (availability
of different transport modes, existing memberships with mobility service providers, and their levels of
services). The survey can preferably include SP choice tasks so that user’s preferences and willingness
to pay for various MaaS bundles can be estimated, and the ones with highest potential can be selected
to offer for subscription. This approach works pretty much in the same way as an SP survey method but
with a decision support system that helps MaaS operators design bundles and estimate the demand for
a given population (see Ho et al., 2020). The second approach allows the targeted customers to construct
their own bundles. This approach requires no prior knowledge about the targeted customers because the
operator effectively hands over the design of the bundles to the customers, who understand their travel
needs best. This second approach was adopted by the UbiGo trial (see Sochor et al., 2016) and previous
research by Caiati et al. (2020).
Both approaches to bundle design have pros and cons. The first approach, referred to as a data-driven
approach, in which bundles are pre-designed by analysts, is time-consuming and always subject to
hypothetical bias (Hensher 2010). On the positive side, this approach gathers valuable data, which can
be used to segment the population and target the potential segments to minimise resources and maximise
uptake. The second approach, referred to as a customer-centred approach, whereby customers choose
the elements they like and provide input into building their own bundles, does not necessarily require
any survey or model, and hence no prior investment is required. The downside, however, is that many
bundles will be created by the customers because their transport needs are likely to significantly vary.
This results in a high administrative cost to manage many different bundles.
For the trial, the targeted customers are IAG employees living in the Sydney Greater Metropolitan area.
IAG is one of the biggest insurance groups in Australia, with more than 10,000 employees in Sydney,
and thus the targeted customers are expected to have varying travel needs. Adopting the data-driven
approach to designing MaaS bundles means that before the trial is officially launched, we must (i)
identify potential participants, defined as IAG employees who are most likely to participate in the MaaS
trial and have a high potential of actively using mobility services offered via the monthly bundle, and
(ii) specify (or speculate) what the participants can do and the associated benefits of buy-in to MaaS.
Given the prior knowledge from previous SP studies in Sydney (Ho et al., 2018) and Tyneside UK (Ho
et al., 2020), we know the critical role of the following aspects in identifying early adopters of MaaS:
a. Current travel patterns (how much a person uses different modes of transport and how much they
pay, on average, per month)
b. Personal and household characteristics (socio-demographics) such as car-ownership, age group,
and employment status (part time or full time)
c. A MaaS bundle that tailors to each individual/segment’s travel needs
d. The benefit of using MaaS (e.g., discounted price, unlimited use of public transport, access to
cars)
Thus, the first task requires a pre-trial survey, covering all points a – d above, plus any other items that
are unknown at this state in deciding bundle acceptance such as the form and/or level of incentive
(financial vs. non-financial). The pre-trial survey should include a stated bundles choice experiment to
help design mobility bundles that are more attractive to each segment, while also interesting for the

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mobility broker/operator (i.e., IAG) to test, considering the budget and the potential of commercialising
these after the trial is completed.
To design the SP survey, we need to know the mobility services, cost, and non-mobility service that will
be included in the MaaS in-field trial. Note that section 3.1 identified the transport services Tripi ended
up offering, but at the stage of the pre-trial survey, it was far from clear if we would be able to include
bike-share and car-share in the field trial. Note also the possibility that IAG as a MaaS operator, but also
an insurer in this case, would want to bundle non-mobility offers (such as home insurance, car insurance,
or any other products/services) into a MaaS bundle to make the products more attractive, and indeed
add some value to a potential commercial business case by broadening MaaS to be multi-service rather
than just multi-modal (Hensher 2020).
Initially, the Sydney MaaS trial was planned to go down this path to collect SP data and identify a limited
number of MaaS bundles and the people who are most likely to subscribe to these bundles. Once the
targeted participants have been identified, we then planned to recruit and interview 100 participants to
the MaaS field trial. However, discussions amongst the study team and the progress of app development
shifted the approach towards a new and innovative method to design MaaS bundles.
The proposed method uses the first month of the field trial as a learning period to collect necessary data
and to seek qualitative feedback from the participants for the implementation of the data-driven approach
to bundle design from the second month onwards. After the first bundle is introduced to participants for
subscription, additional data can be collected, and monthly informal qualitative interviews can be
conducted with a few participants to collect input for the design of bundles in the next month. We term
this approach an incremental and co-design approach, which leverages the strengths of both the data-
driven approach and the customer-centred approach.
Using the incremental and co-design approach means that we do not need to design all MaaS bundles
prior to the field trial, nor undertake a pre-trial SP study8. Thus, we can simplify the pre-trial survey to
a much parsimonious and more manageable form which acts as an ‘Expression of Interest’ to participate
in the MaaS field trial. Before elaborating on how the incremental and co-design approach helps to
design subscription bundles for the Sydney MaaS trial, one additional issue needs to be discussed. MaaS
bundles may aim to obtain societal and/or commercial value, and it is important to understand the
technical mechanisms and financial levers available at the MaaS operator’s disposal to design MaaS
bundles. These two concepts are not the same, although they are closely related.
5. Mechanisms and levers available to design MaaS bundles
MaaS bundles define subscriber’s entitlements to various services based on some ‘operational rules’.
These rules act as technical mechanisms for the MaaS operator to update user accounts every time a
transaction happens. Operational rules also create a platform for MaaS operators to use various levers
when combining multiple services and their entitlements into a bundle that has the potential to attract
targeted segments. Using the MaaS bundle design framework developed by Reck et al. (2020),
exemplary mechanisms are:
• Discounts
a. A fixed discount percentage per mode of transport on a bundle, applied per trip. For example,
10% off each Uber trip.
b. A fixed discount amount per mode of transport on a bundle, applied per trip. For example, $5
off every Uber trip. A mechanism must be built in to avoid a trip having a negative balance (a
$8 trip taken with a $10 discount costs $0, instead of -$2) or to combine with constraints, such
as trips connect to PT.
8 We also felt that a focus on getting used to Tripi under PAYG had much merit, and that introducing bundles
would be better undertaken once some familiarity and experience with Tripi under PAYG had occurred. A
recognition that subscription bundles would be subsequently introduced was mentioned.

14
• A cap per mode of transport on a bundle. For example, subscribers are entitled to 5 taxi trips up to 5 km
per month, and 200 km car car-sharing per month where they can use them all at once or across multiple
hires. A mechanism must be built to allow the subscribers to use up to these amounts every month without
additional fees. A different treatment (e.g., pay per ride) is required for every service beyond the caps.
• A subscription fee that a participant is charged up-front each month they are on a bundle. For
example, a $50 subscription fee each month on a bundle which could offer discounts and/or entitlements
as defined above. A mechanism must also be built to debit from the participant's credit the specified
subscription fee each month they are on the bundle.
• A minimum purchase/pre-payment cost that a participant must pay up-front to spend on transport that
month. For example, a $200 minimum purchase cost means at the start of the month a participant must
add $200 to their credit. Once paid, this amount should be added to a participant’s credit to spend each
month they remain on the bundle.
• A reset / roll-over of credit balance at the end of every subscription cycle. This rule governs what
happens to credit, in monetary or entitlement terms, at the end of each month when the subscriber may
effectively switch bundles. Under a “use it or lose it” mechanism, the credit balance will return to zero at
the start of a month. Under a “roll-over” mechanism, the only rules that applies are (i) every $1 of payment
received from a participant will increase their credit balance by $1, and (ii) any unused credits will be
added to the next month entitlements. That is, the balance will always continue to flow over to the next
month and MaaS operators do not penalise subscribers by removing an unused positive balance.
Overall, these mechanisms enable the MaaS operator to define various bundles and manage a user
account balance. Once the mechanisms have been built, the operator can pull the corresponding levers
to design mobility bundles. Table 2 summarises the various levers and their corresponding mechanisms
that MaaS operators may want to have at their disposal to define and implement MaaS bundles. An
illustration of how these levers work is provided in Appendix A while Appendix B summarises the
mechanisms and levers that are used by current MaaS providers. Using these levers, Appendix C shows
how the MaaS operators can define monthly bundles and apply the appropriate treatment to every
transaction that occurs to the subscriber’s account.
It is worth noting that some mechanisms are easier to implement than others. We identify in Table 2 the
preferred levels and their corresponding mechanisms to design bundles for the Sydney trial. Our
preferences for these levers are driven by three desires. First, monthly bundles must be easy for the trial
participants to understand. Second, the levers are useful to alter subscriber’s behaviour towards more
sustainable choices of transport modes while are not too labour-intensive to manage. Finally, time and
resources required to develop the corresponding mechanisms so that these levers can be used in defining
bundles must be within the timeline of the trial. The Sydney trial settled on using the upfront cost, billed
cost, fixed unlimited cost, fixed discount, percentage discount, and credit roll-over as levers to design
month bundles. The next section describes the process of designing monthly bundles in detail.

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Table 2 Levers and mechanisms that MaaS providers can utilise to define MaaS bundles
Lever
Description
Mechanism
Upfront cost*
Participant puts money into their ‘Mobility Wallet’ to
spend on services
Minimum purchase/pre-payment
Billed cost*
Participants accrue a cost over the month, and pay at the
end of the month
Roll-over of credit balance
Ticket cost
Participants pay for singular trips, when necessary
Fixed discount (inc. 0%) applied
per trip
Fixed unlimited
cost
*
Fixed cost to gain unlimited access to a mode
Fixed discount (100%) and
subscription fee
Fixed access cost*
Fixed cost to gain access to other discounted prices (like
subscription cost)
Fixed discount + subscription fee
Fixed ticket cost
Single modal tickets at a fixed cost
Flat fare per mode
Capped trips **
Pay $x for trips costing up to $y (x<y)
Capped fare per mode
Capped discounts
allowance **
Users have a capped amount per month to use at a
discounted rate (with PAYG rates after)
Capped allowance per mode
Capped surcharging
Surge prices capped to a limit or removed completely
(specific to modes of transport)
Capped discount per mode
Fixed discount*
Take a fixed amount off the cost per mode/ trip
Fixed discount amount per mode
Percentage
discount*
Take a percentage amount off the bill per mode
Fixed discount percentage per
mode
Time-based
incentives
Apply discounts based on the time the trip is taken
Fixed discount percentage by time
period
Multimodal
incentives**
Apply discounts based on the number/type of modes used
in one journey
Multiple modal discount (fixed or
percentage)
Volume discounts
The more money converted into ‘mobility credits’, the
more discounts applied
Quantity discounts
Ratio discounted
credits
Convert $x dollars into credits based on a ratio
Ratio discount
Travel tokens
Token has an equivalent value for a transport mode
Token values + subscription fee
Credits roll-over*
Any unused credits (points, tokens, allowance) are
transferred to the next subscription cycle
Roll-over of credit balance
Unlimited access
Upfront cost of $x allows unlimited access (partial access)
to all modes
Fixed discount (inc. 100%) +
subscription fee
Note: * indicate the levers desired and used in the Sydney MaaS Trial. ** indicate desired levers but too manual to use.
6. Design Bundles for the Sydney MaaS Trial
The Sydney MaaS trial used an incremental and co-design approach to designing monthly bundles.
Over the trial period, four monthly bundles were designed and introduced incrementally, plus a PAYG
option, as shown in Figure 6 (from Hensher, Ho and Reck 2020). This section describes the process of
designing these bundles.

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Figure 6: Sequential introduction of bundles
6.1 PAYG as a default bundle for the first month (November 2019)
The incremental approach to bundle design uses PAYG as a default option when the participant joined
the trial, whenever this would be. While the trial aims to recruit and onboard about 100 participants, it
is impossible to onboard all participants at once, since the onboarding process takes the boarding team
about 30 – 40 minutes per participant. Onboarding tasks include explaining the purpose of the trial,
seeking the participant’s consent for collecting and using their travel-related data, explaining terms and
conditions of the trial, setting up accounts, familiarising the participants with the Tripi app, and taking
the participant through each step of the trial so that they know what to expect around invoicing and
bundles. Figure 7 shows the progress of onboarding the participants to the in-field trial, with most
participants being onboarded in November and December 2019. The onboarding process ended on 20th
January 2020 with a total of 93 participants, very close to the target 100 participants.
Figure 7: Progress of onboarding participants to the Sydney MaaS trial

17
Using PAYG as a default option for the first month of joining MaaS is justified from both user’s and
operator’s viewpoints. From the user’s perspective, there are many things that the participants may not
be familiar with when they were onboarded to the trial. Examples are how to use the Tripi app to search
for mobility services, compare the alternatives, book the services they want, and pay for them. Thus,
using PAYG option for the first month means that the participants can experience the Tripi app and
learn how MaaS works before they have to make a choice between paying as they go or subscribing to
a monthly bundle.
From the operator’s viewpoint, the incremental approach to bundle design, starting with a PAYG option,
is desired for two reasons. First, while some data on individual participant’s demand for various
transport services were collected in the pre-trial survey, the insights gained from analysing this dataset
were limited to the desired features of a MaaS digital platform and socio-environmental context of the
potential participants. Thus, pre-trial data are not sufficient to form an accurate basis for how much of
different transport services each participant would require for their monthly travel. The PAYG period is
useful for estimating individual transport needs and segmenting the participants so that an appropriate
bundle for each segment can be designed. Second, participants were onboarded gradually, and thus
introducing all monthly bundles at once would risk missing some emerging segments and creating
unnecessary administrative and technical work in the back-end to implement and manage these bundles.
The timeline in Figure 8 provides an overview of the different bundles and a guide to the structure for
the following sections that discuss each bundle.
Figure 8: Timeline for bundle introduction
6.2 The Fifty50 bundle for the festive season (December 2019)
Guided by previous SP studies (Ho et al., 2018, Ho et al., 2020), the study team believe that discounting
is a highly desirable characteristic to engage participants with monthly bundles. Given the scale of the
trial, we decided not to push to get a reduction in price, either per trip or per hire, from any TSP and use
them as levers to monthly bundle design. Rather, a budget of $100 per participant per month was
allocated for this via the project to ‘artificially’ offer some discounting in bundles as well as to be used
as a guide as to how much the project could afford for financial incentives. As the months progressed
and bundle offers were taken up, we would then be able to revisit the budget allocation and decide on
changes that were deemed desirable to change behaviour as well as were affordable, given the spend to
date.

18
Figure 9 Process of designing the first monthly bundle: Fifty50
Figure 9 illustrates the process of designing the first monthly bundle, which has three additional
inputs/constraints. First, very limited data were available because the trial was launched for only a week,
with about 30 participants starting generating data (see Figure 7). Second, once introduced, bundles
cannot technically be withdrawn during the trial unless no-one subscribes to them. Third, the first bundle
is offered in December when individual transport need is expected to be different from that of a typical
month.
Given that the trial goal is to reduce travel emissions, and that the December bundle should account for
seasonal effect, the initial design split the incentive budget evenly between PT ($50) and Taxi/Uber
($50), the latter facilitating short taxi and Uber trips, which are popular during the holiday periods. As
IAG shut down their business for one week over the Christmas period, the participants, who are all AIG
employees, are expected to use PT at most three weeks in December, and hence a 75% discount for PT
(3 weeks out of a 4-week month) is deemed more attractive than unlimited use of PT. Offering 75%
discount for PT in Sydney costs us a maximum of $150 per month per person, given the $200 Opal
monthly cap. Charging the subscriber a subscription fee of $100 per month means that this initial design
requires $50 incentive budget for PT. In addition, the initial design offers a $3 reduction for every
Uber/taxi trip. With a $50 incentive for taxi/Uber, this is a conservative design that enables about 17
rides per month, which is about maximum one would do per month. The design used the fixed discount
lever instead of the percentage discount lever for taxi and Uber trips because the former creates more
value for short Taxi/Uber trips than for long trip, and this may encourage participants to use taxi/Uber
to access PT services instead of using taxi and Uber, both being carbon-heavy modes, for door-to-door
travel.
The initial design was subject to a co-design process in which the business co-design (BCD) team at
IAG suggested charging a $50 subscription fee and offers a 50% PT discount instead, while keeping a
$3 discount for every Uber/taxi trip. This does not change the incentive required (still $50 for PT and
$50 for Uber/Taxi), while lowering subscription fee to $50 (i.e., a lower barrier-to-entry mentally) and
making is easier to digest a half-price discount than a 75% one. Finally, the $50 with 50% off makes it
easy to sell with a catch holiday plan name like "Fifty50".
The BCD team did a very quick poll of some current participants via an internal messaging tool and
asked: "Which plan would you be most likely to select for the December period? Of the 9 participants
taking the poll, 2 chose PAYG, 1 chose the initial design bundle, and 6 chose the co-design bundle. The
study team settled on the co-design bundle, the Fifty50. Figure 10 shows the email introducing the
Fifty50 bundle to all participants, together with instructions on how to subscribe.
Of the 66 participants who were onboarded by the end of November 2019, 11 participants subscribed to
the Fifty50 bundle in December, with the balance staying with the default PAYG option. It is noted that
the study team recommended each participant to use Tripi as a PAYG user for at least two weeks in
order to have a better understanding of their travel needs before subscribing to any bundle on offers;
however, the participant is allowed to take up a monthly bundle when it is on offer, regardless of how
long they have been onboarded to Tripi under the default PAYG option. It appears that in December

19
2019, all participants follow the recommendation, with all 11 subscribers to the Fifty50 bundle being
onboarded and made their first trip via Tripi before the 15th November 2020 (i.e., having at least two
weeks of travel records before taking up the bundle). Put it differently, all the 26 participants who were
onboarded after the 15th November 2020 stayed with PAYG in December.
Figure 10: Communication email introducing the Fifty50 bundle for December 2019
6.3 The Saver25 bundle for the New Year (January 2020)
In contrast to the design of the first monthly bundle, Fifty50, which was mainly based on a conservative
and co-design approach, the design of the second bundle for January 2020 starts to be data driven
because 80 participants have been onboarded, and have started using Tripi and generating data. The new
monthly bundle for January 2020 must be released by the 12th December 2019 so that the participants
have enough time to clarify details and consider subscribing or not (given the Christmas and New year
shutdown between 20 December 2020 and 5 January 2020 inclusive). This means that usage data are
available, but this varies across participants, ranging from 2 days to 5 weeks. Figure 11 illustrates the
process of designing the January bundle.

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Figure 11: Process of designing the January bundle: Saver25
Considering the data limitation, the study team invited all onboarded participants to a ChristMaaS
morning tea, held in the IAG city office on the 3rd December 2019, to share their experience and
upcoming January activities to help co-design a bundle for January 2020. We gathered feedback and
information via informal chats from seven participants, which largely confirmed our hypothesis: January
is a typical holiday period in Sydney with a different travel pattern. A few participants revealed that the
$50 subscription fee of the Fifty50 bundle is rather high, particularly during the irregular work months
of December and January.9 Despite all the irregularity, most participants will likely work regularly for
approximately three weeks in January.
We surveyed the booking data collected up to 2nd December 2019 to learn about participants’ travel
patterns and use these as input in our data-driven approach to bundle design. An analysis of data on
regular work weeks suggests two things. First, most trips that the MaaS participants booked are PT,
which can be regarded as the sustainable “backbone” of mobility provision in the trial. Most people,
however, have PT outlays less than the weekly Opal cap (i.e., $50 per week, with an average weekly
cost of $35). Second, Uber is most popular among the remaining modes (24 users, 75 trips), followed
by taxi (8 users, 13 trips) and GoGet is least (3 participants, 8 trips) between 4 Nov and 2 Dec 2019.
Building on the insights on participants’ anticipated activities for January obtained through the
ChristMaaS morning tea (i.e., the co-design approach) and regular travel patterns of transport mode
usage obtained from analysing Tripi booked trip dataset (i.e., the data-driven approach), the study team
agreed that an attractive January bundle should: (i) support sustainable travel, especially regular
commutes, and (ii) support flexible, daily outings without a private car. Given that we want to decrease
the bundle entry barrier in January (i.e., subscription fee), we address objective (i) with a 25% discount
for public transport (maximum monthly cost $50/person = incentive budget). As for (ii), the January
bundle aims to promote more car-sharing. While one might initially think that encouraging car-based
modes does not support a sustainability agenda, it must be linked to both short-term and long-term
impacts. Accordingly, the trial emphasised an aim to differentiate the roles of car-sharing in the short-
term (i.e., emissions) from the long-term effects (i.e., intent not to buy or sell the/a second/third private
car). The latter could be measured by asking about buying/selling intentions now and when the trial is
concluded (i.e., through the exit survey).
Following this logic, MaaS offers in January detail a one-off discount of $20 for the first GoGet trip of
users within the trial, which aims to encourage participants to experience car-sharing for the first time
so that they can assess its potential of replacing their (second) private car. January is particularly good
for testing out GoGet car-sharing since a few people would hire GoGet ute/truck for moving furniture
and other large items. The $20 GoGet credit offer is enough to cover the cost of a 2-hour hire of a typical
GoGet car, which the participants can use any time during January. GoGet further offers 20% discount
of the first GoGet trip in January for all trial participants.
9 Only seven participants were present during the ChristMaaS morning tea. Thus, the bundle design is not based
entirely on this feedback.

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For the complementary services in the MaaS offer, we can, in principle, choose between discounts for
car sharing, Uber and taxi. Designing a bundle around car-sharing and public transports risks only being
attractive to a small subset of frequent car-sharing users. To mitigate this risk, we opt to offer all three
flexible modes and use a discount metric other than the fixed dollar discount off each trip so that the
January bundle will not be superior to the Fifty50 bundle. We therefore use the percentage discount
lever on all three remaining modes (GoGet, Taxi, Uber). We do not include discount for Thrifty car
rentals as all IAG employees have automatically received a lower rate (about 40%) than market prices.
The remaining issues in finalising the January bundle relates to the level of discount (at what %) and
bundle price (i.e., subscription fee). To this end, a data-driven approach is used in which a sensitivity
analysis is conducted to compare the appeal of the January bundle with the Fifty50 bundle and PAYG.
The sensitivity analysis uses a $25 subscription fee (make it easy to sell $25 for 25% PT discount,
drawing parallel to the Fifty50 bundle) and assumes that most people will likely work three weeks in
January, with some additional demand for taxi, Uber and GoGet expected. It is noted that the sensitivity
analysis is limited to participants who have at least a full week of data (i.e., having onboarded for 7+
days). Table 3 shows the estimated split of these participants among the three options in January by the
percentage discount on Uber, Taxi, and GoGet, assuming that these participants slightly increase their
demand for flexible modes in January as compared to their own travel record in December, and that they
would choose the option that has the highest value-for-money (i.e., lowest travel costs, including the
subscription fee).
Table 3: How many people would choose each bundle under varying discounts on flexible modes for
the January bundle?
Bundle
Discount levels for Uber, Taxi, GoGet for the January bundle
10%
15%
20%
Fifty50 bundle
11
11
11
PAYG
29
23
23
January bundle
8
14
14
Table 3 shows that using a 15% discount for Uber, Taxi, and GoGet modes for the January bundle, 11
participants would continue to subscribe to the Fifty50 bundle, 23 participants would continue with
PAYG, and 14 participants would subscribe to the January bundle10. Increasing the discount levels for
all these three modes to 20% does not change the split, mainly because the savings from these flexible
trips is small (~$2.25 to $3.15 saving per trip with a 15% discount). Thus, the bundle for January requires
a $25 subscription fee in return for a 25% discount on every public transport trip, and a 15% discount
on every car-share, Uber and taxi trip. We name this bundle Saver25, with the key marketing points
being a lower subscription fee and additional flexibility by including car-share. Communication emails
that summarise the January offers are shown in Appendix D.
6.4 The GreenPass bundle for regular public transport users (February 2020)
By the middle of January 2020 when the February bundle needs to be designed, we have 2-months of
Tripi data, representing which bundle the participants subscribed to and how much of each transport
modes they used so far. Thus, a fully data-driven approach is used to design the new bundle for February
2020, which is illustrated in Figure 12.
10 Here we assume that people are strictly rational, which is not necessarily the case (see Lambrecht and Skiera,
2006, for an overview on the different tariff-choice biases).

22
Figure 12 Process of designing the February bundle: GreenPass
The data-driven approach to bundle design rely on an in-depth analsysis of booking and subscription
data from all participants. The main aim is to segment the markets and verify factors (e.g., potential cost
saving, usage pattern) that motivate users to subscribe to a bundle in the presence of PAYG. Such
segmentation analysis helps identify market segment for the new bundle. Figure 13 compares the
monthly cost estimate for January 2020 that each participant was informed via a communication email
in mid December 2019 (see Appendix D for an example email). The graph is limited to the monthly
subscribers, with Appendix E investigating why participants stay with PAYG. The top panel of Figure
13 shows that all participants, except for P027, have the lowest estimated cost in January under the
bundle they subscribed to (i.e., Fifty50). Thus, there is a strong correlation between the decision to
subcribe to the Fifty50 bundle and the potential monies saved. By contrast, those who subscribed to the
Saver25 bundle for January travel (the bottom panel) do not necessarily have this bundle as the lowest
monhly cost estimate (e.g., P066, P177), although many participants do. Thus, while monies saved may
have influenced the participant’s decision to take up the Saver25, the lower entry cost (i.e., subscription
fee – see below) may have been appealling too. As for PAYG users (see Appendix E:), an analysis of
cost saving and booked trips reveals that many participants were on holiday, and hence prefer PAYG.
Figure 13: Estimated monthly cost of travel in January by bundle subscribers in January 2020

23
The data-driven approach to bundle design lies partly in our understanding of the value that subscribers
derive from each monthly bundle, including how subscribers use different transport modes on offers,
compared to those who have always been on PAYG. Appendix F presents a comparative analysis of
subscribers vs. PAYG users in terms of modal utility. Overall, this analysis found that those who
subscribed to the Fifty50 bundle showed a much higher average number of PT trips than those who have
always been on PAYG since being onboarded. By contrast, Saver25 subscribers showed a lower level
of PT use while a higher level of Taxi/Uber travel during the period up to 1 January 2020 when this
bundle was first available to the participants. As for other modes (GoGet, car rental), usage patterns
were remarkably similar between the subscribers and PAYG participants.
In summary, analysis evidence suggests that we have two good bundles in place to cater for different
segments of the 90 participants to date (mid-January 2020). The Saver25 bundle is more attractive to
participants with a lower level of PT use (around 4 – 5 trips per week) and one or two weekly Uber/Taxi
trips. The Fifty50 bundle is appealing to participants with a higher level of PT use (around 8 – 10
trips/week in November 2019). What we need for February 2020 is a bundle that serves relatively heavy
PT users, either in terms of trips or fare.
In February, we expected all participants would go back to work, and hence, February is the good month
to introduce an ‘all you can ride public transport’ bundle, called GreenPass, which offers unlimited
travel on PT for a subscription fee. Again, sensitivity analysis was used to design subscription fee such
that this bundle is attractive to current PAYG users while it does not reduce the attractiveness of the two
existing bundles (Fifty50 and Save25). Table 4 shows the split of users by monthly bundle that is most
financially appealing to them, assuming their historical and regular travel patterns are to be repeated in
February 2020.11
Table 4: Split of participants by most finally attractive bundle: sensitivity analysis of the GreenPass
subscription fee
Most financially attractive
bundle
$100 subscription fee for
GreenPass
$125 subscription fee for
GreenPass
Fifty50
0
24
PAYG
22
22
Saver25
17
22
GreenPass
51
22
Note: as of 20 January 2020, there are 90 Tripi participants: 14 on Fifty50 and 10 on Saver25 with the balance
on PAYG.
With a $100 subscription fee, the proposed GreenPass bundle is likely to dominate severely the Fifty50
bundle. A $125 subscription fee results in a nice even split of users amongst the bundles. An estimate
of the incentive budget required to offer the GreenPass bundle to everyone confirms that the trial has
the budget to afford this level of incentive (see Figure 14 with few participants receiving $75 incentive
per month, while many participants receiving between $25 - $50 per month under the GreenPass).
11 Note that each participant has their own travel record, with some having onboarded earlier than others, and
some being more active on Tripi than others

24
Figure 14: Estimated incentive cost provided to each participant by different monthly bundles
Thus, the new bundle for February 2020 offers unlimited use of public transport, a 15% discount for
every Taxi and Uber trip while requiring a subscription fee of $125 per month. Note that the GreenPass
bundle excludes GoGet car-sharing from the mix because the generous GoGet offers in January (see
section 6.3) did not result in any significant increase in GoGet trips. We only observed two more
participants using the $20 GoGet credit + 20% discount to try out the GoGet car-sharing in January (i.e.,
one GoGet trip per new user).
The onboarding process was completed on the 20th January 2020 with a total of 93 participants. Of these,
12 participants subscribed to the GreenPass bundle in February, 12 participants subscribed to the Fifty50
bundle, and 14 participants subscribed to the Saver25 bundle, with the balance using PAYG. While the
exact number of subscribers to each bundle differs from the estimate shown in Table 4, the three bundles
obtain a balance of users as designed (one bundle for one segment of traveller).
6.5 Fine-tuning bundles (March 2020)
March was scheduled to be the last month of the incremental bundle design process, with the plan to
switch everyone back to PAYG in the last month of the trial, in order to compare with the first month
when they joined the trial as PAYG users. By the end of February when the new bundle for March was
designed, the trial had collected an extensive amount of data for analysis to assist bundle design. Yet,
there is still one critical question relating to bundle design that we have not yet been able to answer by
analysing the trial data. That is, why a significant number of users have stayed with PAYG despite the
financially attractive bundles which have been offered?
Data analysis suggests that many PAYG users are already multimodal users who have access to a private
car and use PT, Taxi, and Uber much less often than bundle subscribers do. Monthly variability in travel
demand indeed can be a hurdle to bundle uptake, particularly for modes that are not regularly used (i.e.,
Taxi, Uber) (Reck and Axhausen, 2020). Thus, it may be difficult to design financially attractive bundles
for these participants and test how monthly subscription may change the way they travel. In addition, it
is not clear at this point whether attracting these multimodal users to a bundle would reduce their carbon
footprint, which is the ultimate goal of the MaaS trial. To investigate this in more detail, we conducted
one-on-one semi-structured interviews with 22 participants in February, covering all bundle subscribers
and PAYG users, as well as those who switched between bundles, and between a bundle and PAYG.
The main aim of this qualitative survey is to provide additional insights as to what the trial can do (i.e.,

25
which levers to pull) to promote more sustainable travel and to reduce the cost of travel, both to the
participants themselves (monetary, hassle) and to the environment (emissions).
With respect to bundle design, a few interviewed participants suggested removing from the monthly
bundles the discounts for transport services they do not use (i.e., GoGet) and increasing the discounts
for services that they use more often (e.g., Uber)12. Also, there is a dominating preference for fixed
dollar discounts over percentage discounts. Overall, the interviewed participants agreed on the
importance of PT services in monthly bundles and suggested a way forward to make PT use easier such
as including bike-share and Ola in the MaaS offers to address the first and last mile issue associated
with PT use.
Feedback from continuing PAYG users (i.e., participants who have always been using Tripi as PAYG
users) suggests four common reasons why they stay with PAYG. These relate to value for money (e.g.,
do not use a certain mode often enough to find the bundle offers worth the subscription fees), spatial
context (e.g., living close to work and could access to most activities on foot), varying travel patterns
from one month to the next, and the need to commit to a certain bundle for one month (while they are
used to paying per ride for all the services that the monthly bundle offers).
Given that the most affordable bundle (i.e., Saver25) costs $25 per month, the new bundle needs to add
extra value if it aims to address the value-for-money feedback, since reducing the subscription fee below
$25 appears to create little difference to travel budgets of the participants, who are regarded as middle
or high income earners. Apart from adding more value, it appears that the trial has limited options in
responding to the qualitative feedback from the continuing PAYG users, except for further promoting
MaaS bundles through, for example, better communications implemented by using bespoke
visualisations in communication with the participants (see below).
In responding to the preferences for fixed dollar discount, in March we fine-tuned the Saver25 and the
GreenPass bundles by replacing the 15% discount for Taxi and Uber with a fixed $3 discount as in the
Fifty50 bundle. Given that taxi and Uber trips are short in nature, the $3 discount was deemed preferable
than the 15% discount for most trips, albeit the difference is small. Regarding the suggestion of removing
the GoGet mode from monthly bundles, an analysis of booked trip data shown in Figure 15 identifies
six GoGet users, with two users using GoGet every month (P001, P176). In January, it cost the trial
more than $100 to subsidy a heavy GoGet user (P176) while the generous GoGet offer ($20 credit +
20% discount for first trip) was taken by three other participants (P024, P027, P035). Only one of these
participants is a new GoGet user (P024). A decision was made to remove GoGet from all March bundles
since offering it in monthly bundles costs money for little impact on the participants’ choices of transport
modes.
12 This finding aligns with Guidon et al. (2020) evidence that discounts a user does not want are perceived as
negative value (i.e., decreasing WTP)

26
Figure 15: Monthly GoGet cost by users in the trial: cost to provide vs. cost to user
To address the PT first/last mile issues, the trial investigated possibilities to cap taxi and Uber fares at a
certain price to make these modes more affordable for participants who would use these modes to access
PT but being discouraged by the high fare. A spatial analysis reveals that up to 80% of PAYG users live
within 5 km of a train station (see Figure 16), and thus capping fares for taxi and Uber trips that are up
to 5 km has a great potential to attract PAYG users, in addition to the Saver25 subscribers.
Figure 16: Distribution of distance from participant’s home to nearest train station by MaaS user type
Applying a capped fare for eligible taxi/Uber trips effectively means we use a cap per mode mechanism,
which is not available for the trial, to create monthly bundles and apply discounting/allowance rules
automatically. Thus, using this lever necessarily requires an analytical method to identify eligible trips
and process them manually within a timeline that is acceptable to the users. To reduce the analytical
burden, the flat fare offer is limited to Uber only (because Uber distance is readily available, whilst taxi
distance requires network analysis) and allows a 60-minute changeover between PT and Uber. The fares
of eligible Uber trips that connect to/from PT are capped at $5, informed by a regression analysis of
booked Uber/Taxi trips which suggests a formula to estimate Uber fare ($6.00 + 2.50 × km). Capping

27
Uber fare at $5 associates with a risk of Fifty50 and GreenPass subscribers substituting walking or bus
for Uber as an access mode to train services, and hence the $5 Uber flat fare offer is limited to the
Saver25 bundle only.
In summary, the bundles offered in March 2020 are similar to those in February, with some fine-tuning.
Removing GoGet and changing a 15% discount to a $3 dollar discount are minor changes which have a
little effect on a few participants. The only significant change in March relates to the Saver25 bundle,
which adds a $5 flat fare offer for all Uber trips that connect to/from PT. We therefore rebranded this
bundle to the SuperSaver25 bundle and personally called all existing Saver25 subscribers to inform
about this significant change before sending a notification about March bundles to all participants (see
Appendix E for details). Figure18 summarises all monthly bundles offered by the Sydney MaaS trial.
Figure 17: Monthly bundles offered to MaaS participants from December 2019 to March 2020
Implicit to the design of these bundles are the issues of equity, ethics, and morality of the MaaS program
that we took seriously to ensure that the MaaS products align with equity and responsible innovation.
While the trial in itself is considered small-scale with circa 100 participants, the design approach we
have taken considers societal implications that could arise from a large-scale adoption of MaaS,
including environmental sustainability and key societal issues such as health and social inclusion
(Pangbourne et al., 2020). This moral consideration is an anticipation of the possibility that the industry
partner may decide, post-trial, to commercialise the mobility bundles and offer these to the wider
population.
Any reflection on equity issues in design necessarily initiates the concept of ethical design, which means
“considering the context of the product you create” (Sgarro, 2018). The moral questions we considered
in this trial include environmental sustainability, social exclusion, and spatial justice – all are the long-
term problems facing the passenger transport sector we are designing the MaaS solution for. We have
executed the equity through ensuring the design of mobility bundles, and more widely, the MaaS
ecosystem has sustainability goals (i.e., reducing CO2 emissions) and does not limit interest and
participation from any socio-economic (linked to social exclusion) or geographical (linked to spatial
justice) segment of society. For example, the Saver25 bundle and its successor – the SuperSaver25
bundle – lower the entry barrier and address the first/last mile issue for people who would want to use
public transport services but being deterred by the access distance/cost. Similarly, the trial set aside
budget to incentivise the participants to use green(er) transport services. We made it clear that the
financial discounts are paid for by the study and not from government tax-payers funds via the public
transport authority.
The co-creation and data-driven approach to bundle design reflects vividly the four dimensions of
responsible innovation, including anticipation, reflexivity, inclusion, and responsiveness (Stilgoe et al.,
2013). The way in which the mobility bundles were designed, offered, marketed and procured in this
trial accounted for changes in individual travel behaviour, which may lead the participant to a certain

28
decision such as revert to PAYG or leaving the trial entirely. Anticipating these situations, we took an
incremental approach to bundle design aiming at increasing resilience and identifying new opportunities
(i.e., market segments) for innovation. The co-design approach which includes deliberative pooling and
qualitative survey reflects the reflexivity and inclusion components of the responsible innovation
framework in the sense that the study team is aware of the limits of knowledge and being mindful that
a particular design of a mobility bundle may not be attractive. Hence, the trial continuously sought inputs
from both participants and lay members of scientific advisory committee. Finally, the fine-tuning of all
bundles in the final month of the trial, as well as the replacement of the Saver25 with its superior version
- the SuperSaver25 - reflects the responsiveness component of Stilgoe et al. framework for responsible
innovation. Specifically, recognising the insufficiency of knowledge during the early months of the trial,
we adjusted our courses of action in responding to new knowledge gaining from qualitative feedback
and extensive quantitative analysis.
The processes of inclusive design “inevitably force consideration of questions of powers” (Stilgoe et al.,
2013, p.1572). In the context of this trial, the broker owned all contact with the customers in terms of
their billing and use of the services, and the subsequent aggregation of their travel into a personal bill
per the subscription plans offered. No power was exercised in getting users to adopt the MaaS trial or
the mobility bundles offered. Many participants were simply interested in the prospect of a service to
centralise all their transport expenses and activities, but we did also show that the intention of the trial
was to introduce subscription plans with the potential to save money on their every-day travel and
simplify the way they travel. The trial did not pay or incentivise the participants up-front in any way, it
was all purely based on the participants wanting the service and then ultimately seeing value in the
savings or functionality provided via the Tripi app.
7. Bundle implementation
The monthly bundles described above were implemented via the wallet portal where the trial
administrator can specify the bundle details (i.e., modes, entitlements, subscription fee). Figure 18
shows the user-interface of the Tripi wallet portal, with the Fifty50 bundle used as an example.
(a) Overview of monthly bundles
(b) Example bundle details (Fifty50)
Figure 18 Implementing monthly bundles via the Tripi wallet portal
Once bundles are available for subscription, users can view all current active bundles, or a selection
thereof, via the Tripi app, plus the bundle they are currently on. A user’s view of available bundles via
the Tripi app is shown in Figure 19. Users can choose from this selection a single bundle they would
like to have for the next period. For the Sydney trial, participants can change their bundle selection as
often as they like, but the one selected on the 1st of every month is the one which will be activated.

29
(a) Current bundle
(b) Available bundles for next month
Figure 19 Tripi user’s view of monthly bundles
In the first week of each month, each participant received a summary of their activities and their closing
balance for the previous month. Where this is negative, it prompts them to pay this amount, using the
Tripi payment function or a bank transfer. All information shown in the invoice comes from the actual
trips taken and costs incurred as per the wallet view, but shown in the participants’ view (i.e., they see
all the credit adjusted prices and credit balance, not raw prices). Figure 20 shows a redacted invoice sent
to a participant who was on the GreenPass bundle for the invoicing period (reflected in the $0 price for
the $104.91 worth of Opal use).

30
Figure 20 An indicative redacted invoice in the online view of the Tripi app
8. Bundle uptake
A summary of the take up of bundles by month is given in Figure 21. The evidence on take up of bundles
is very encouraging, at 46 percent in March.13 The percentage of participants subscribed to the Fifty50
13 A comparison with available known evidence from stated preference studies such as Ho et al. (2018) is very
encouraging. To make the comparison we must ignore the status quo respondents in the SP study. We never ever
asked and accounted for in the trial those who did not participate (stayed with status quo). 37% in the SP study
choose a bundle for those who effectively participated like the trial, and 36.4% in trial choose a bundle.

31
bundle in the final month of the trial (i.e., March 2020) is 15%, and the equivalent statistics for the
SuperSaver25 and Green Pass bundles are 12% and 19%, respectively. This aggregate share from real
preference evidence is similar to what has been found in SP studies such as Ho et al. (2018) and the first
tangible evidence on the validity of the SP approach to study Maas uptake. Table 5 summarises the
transition between bundle subscriptions and PAYG as of March 2020 when all bundles, except for the
Fitfty50, are fine-tuned following participants’ feedback and an extensive data analysis. Four
participants from PAYG joined the SuperSaver25, while another four participants who were on Saver25
in February switched to PAYG in March. Nobody on GreenPass was tempted by moving to another
bundle, which saw the most growth and is also the one promoting the most sustainable travel, and the
only one with a hard cap ($125 for "all you can ride" public transport). Modelling the drivers of these
bundle subscriptions and month-to-month switching is reported in Ho et al. (2021a).
Figure 21 Summary of bundle take up by month: November 2019 – March 2020
Table 5 Bundle switching between February (rows) and March 2020 (columns)
February bundle
Fifty50
Green Pass
PAYG
SuperSaver25
Total
Fifty50
8
1
3
1
13
GreenPass
12
12
PAYG
4
3
43
4
54
Saver25
2 2 4 6 14
Total users
14
18
50
11
93
Percentage
15%
19%
54%
12%
100%
9. Conclusions
What might we have done differently? A trial, by its very nature with a typically constrained set of
resources and the technical constraints (see section 5), can only design and test a few levers to create a
limited set of bundles that attract different travel segments, and consequently altering their travel
behaviour towards more sustainable choices. It should be noted, however, that real world MaaS offerings

32
such as Whim and Ubigo have four bundles available. The constraints mean that we were unable to
provide a customised bundle that suits individual needs, such as removing discounts for modes the
participant does not use as the qualitative feedback suggested, because this necessarily creates many
bundles to manage. While it is difficult to do a completely custom co-creation with technical and
resource limitations in the trial, indeed real world MaaS offerings have not achieved this to date, seeing
the similarities among individual preferences through data analysis and qualitative feedback is useful to
refine the bundles to better match individual transport needs. One thing we had planned to do if the trial
had if not come to a sudden stop in April 2020 due to COVID-19, is to vary the fixed discount for taxi
and Uber across the monthly bundles so that the participants can sort themselves into different bundles
according to their preferences for these services. This would have provided more quantitative data to
test the role of discounting for these flexible modes in improving the attractiveness of monthly bundle
offers.
Reflecting on the technical-rational model that Marsden and Reardon (2017) highlights with a number
of limitations (including “little attention paid to the goals, setting and objectives”, “static or context-free
reflection on policy tools rather than process”, “lack of engagement with real world policy example”,
leading to), we believe that our paper has followed the “calls for notions of ‘communicative rationality’
and ‘exogenous’ variables such as decision maker preferences to be better built into models in order to
make their predictions stronger.” (Marsden and Reardon, 2017 p.245). Indeed, this is the motivation for
us to document the geographical and institutional setting within which the trial takes place, as well as
adopting a co-design approach in designing mobility bundles. Retrospectively, we believe that our
approach effectively uses the “communicative rationality” and engages with the real-world complexities
by accounting for not only the contextual setting of the trial, but also decision maker preferences, which
in this case is the trial participants’ preferences for subscription bundles they would like to see in the
subsequent offers. Drawing parallels to what Marsden and Reardon (2017) described as “impactful
policy research”, we believe that this paper provides not only “better information and tools to aid policy
makers, but also about developing a body of knowledge that […] understands why decisions come to be
made in the way they are.” (Marsden and Reardon, 2017 p.246).
A post-trial survey of all participants (see Hensher, Ho, Reck et al 2020) revealed that a greater number
of participants would have been preferred a longer period to test and refine bundles, and to ensure we
have appropriate tests of ways to reduce private car use. The most notable lesson learnt is the ability to
be more flexible in the design of the digital platform in order to test new ideas that arise such as caps on
the amount of travel that can be subject to even larger discounts for specific modes, and the ability to
allow for bundle design during any time that is appropriate when new evidence is identified. While the
trial was able to implement this cap via data analysis and post-processing of trips booked, it would
provide better customer experience and require less admin support if the caps could have been
implemented via the back-end setup such that trips that are eligible for capped fares could be identified
and treated accordingly.
During the start-up period of the in-field trial, it was recognised that not all participants will be ready
through the onboarding program to commence the PAYG period at the beginning; that is on Monday
November 4, 2019. While collecting a full month of PAYG data (elapsed month strategy) and only
offering bundles subsequently is preferred, Tripi was designed for what we refer to as a calendar month
condition as distinct from elapsed period, resulting in some participants having a limited time under the
November month to get used to Tripi and PAYG. This was not ideal for participants who needed more
time to get used to the digital platform before having to choose between PAYG and a bundle. However,
one has to be careful in allowing too much flexibility and revision since this has resource implications
throughout the entire MaaS program as well as risks confusion for participants with too much change.
On balance, we are confident that the progress made in the Sydney trial can offer a rich informative
starting point for any future trials or indeed a market role out of a MaaS product. The logistics of rolling
out MaaS beyond a trial have been tested and we now are confident as to the operational requirements,
and associated resources, as distinct from the more challenging marketing and take up tasks that
ultimately determine the business case.

33
On-going work involves data analysis and modelling is progressing in three directions: modelling
monthly bundle uptake to identify key drivers of bundle subscription, assessing the impact of bundle
uptake on travel behaviour, particularly private car use, and modelling mode choice at the trip/journey
level conditioned on a bundle subscribed to. Hensher, Ho and Reck (2020) have analysed the sub sample
who also participated in Safer Journeys program and find encouraging evidence of a reduction in car
use when a bundle is taken up. There may also be an opportunity to see how the revealed preference
data collected by this trial might be used to calibrate the SP models developed for Sydney outside of the
trial setting.
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Appendices
Appendix A: Levers to influence mobility bundle design and user travel behaviour
Summary of levers or payment mechanisms which can be utilised (alone or in conjunction) to influence
mobility design and user behaviours. *Prioritised levers for the Sydney MaaS Trial
9.1 Pay-As-You-Go
*Upfront Cost:
Participant puts money into their
‘MaaS Wallet’ to spend on services.
*Billed Cost:
Participants accrue a cost over the
month, and pay at the end of the
month
Ticket Cost:
Participants pay for singular trips,
when necessary
9.2 Bundles with fixed entitlements
*Fixed Unlimited Cost:
Fixed cost to gain unlimited access to
a mode.
*Fixed Access Cost:

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Fixed cost to gain access to other
discounted prices (similar to
subscription cost)
Fixed Ticket Cost:
Single modal tickets at a fixed cost
9.3 Bundles with Caps
*Capped Trips:
Pay $x for trips costing up to $y
(x < y)
Capped discounts allowance:
Users have a capped
amount per
month to use at a discounted rate (with
PAYG rates after)
Capped surcharging:
Surge prices capped to a limit or
removed completely* specific to
modes of transport.

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9.4 Bundles with Discounts & Incentives
*Fixed Discount
Take a fixed amount off the cost per
mode/ trip.
*Percentage Discount:
Take a percentage amount off the bill
per mode.
Time-based Incentives/ Discount:
Apply discounts based on the time the
trip is taken.
Multimodal Incentives:
Apply discounts based on the number/
type of modes used in one journey.
(Desirable but too build/ manual
intensive for the trial)
9.5 Bundles with Credits, Points & Tokens
Volume discounted credits/ points/
tickets:
The more money converted into
‘mobility credits/ points’, the more
discounts applied.
Ratio discounted credits/ points

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Convert $x dollars into credits based
on a ratio
Travel tokens:
Token has an equivalent value for a
transport mode.
9.6 Bundles with unlimited services
Unlimited Access
Upfront cost of $x allows unlimited
access (partial access) to all modes

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Appendix B: Mobility Mechanisms utilised by current MaaS providers
As one of the leading solutions in the MaaS space, Whim notably utilises distinct mobility packages
to cater to certain population groups:
• Whim to Go: With no subscription fee, this option is likely to allow the user to experience the
app and see the potential benefits of subscribing to the other Whim models
• Whim Urban 30: With initial discounts, this package appeals to frequent PT users and multi-
modalists. The low upfront cost may make this option appealing to those who require
flexibility in their additional modes.
• Whim Weekend: This package likely appeals to users who desire a certain car for weekend
trips or those looking to get rid of second car.
• Whim Unlimited: This package appeals to users who do not own a car and frequently travel
across several modes.
Uniquely, UbiGo offers households the ability to design their own mobility bundle according to their
needs:
• There are no fixed fees and no binding time
• Unused hours/ tickets rollover into the next month
• Volume discounts are utilised to increase the number of tickets bought at once:
o i.e. for Public transport: for 20 tickets  820 SEK (41 SEK/day); 40 tickets  1440
SEK (26 SEK/day)

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Currently, it appears zenGo is trialling their MaaS offering, by asking for first user via an online form.
Regardless, zenGo’s UVP lies in the incorporation of a token system on top of an existing upfront
subscription cost.
• A upfront subscription fee enables access to public transport (unlimited within zones) and
public bikes (in Lausanne only – 30 min limit).
• A zenGo token has the equivalent value of:
o 1 taxi ride within city limits
o 1.5 flexibly divisible free-floating car-share (trial subscription with Mobility for 4
months minimum – Geneva only)
o 12 hours of rental car use
• The option to purchase additional tokens is not currently available.
• Differing prices for students/ youth
As the only public offering in the analysed MaaS providers, WienMobil has a PAYG system which
combines the routing app qando and Wiener Linien ticket app with additional mobility services.
• Single or bulk tickets are purchased as required
• Qando calculates public transport in Vienna, showing various POI (car and bike-sharing
locations, as well as stops and Park & Ride facilities) on the map. WienMobil further
considers suggests routes for bicycles, car-sharing vehicles or a combination of different
means of transport.

42
o Different routing systems are used for the two apps: qando makes use of a system just
for public transport route planning, while WienMobil uses the multimodal route
planning system provided by the Austrian Transport Information Service (VAO).
Filtering options for price, duration and environmental friendliness can be used during routing.
Appendix C: Example monthly bundle that uses minimum purchase ($200) and fixed discount
mechanisms (100% for Opal and $10 for every GoGet car-share hire)
Event
Payment
Raw Cost (actual
costs incurred on
transport)
Adjusted cost
(costs shown to
participants)
Raw cash balance
(visible to admin
wallet)
Credit balance
(shown to
participant)
Month begins
-$234
-$198
Opal journey (100%
discount)
$4
$0
-$238
-$198
Uber trip (no
discount)
$30
$30
-$268
-$228
Participant pays
$198 (from month 1
invoice)
$198
-$70
-$30
Minimum purchase
paid by participant
$200
+$130
+$170
Opal journey (100%
discount)
$4
$0
+$126
+$170
GoGet trip ($10 off)
$50
$40
+$76
+$130
(other charges during
the month so we
aren’t showing
hundreds of line
items)
$300
$220
-$224
-$90
Month 2 ends

43
Appendix D: Communication emails introducing January offers
a) GoGet car-sharing one-off offer
b) January bundles for subscription

44

45
Appendix E: Why participants stay with PAYG
Figure 22 shows that many PAYG users in January 2020 can save money if they subscribe to the bundles
offer as many participants would have the lowest monthly cost under the Fifty50 or the Saver25 bundle
if they travelled for 3 weeks in January – an assumption used in the data-driven approach. Yet, they did
not take up any of the two bundles, staying with the default PAYG option which aligns with the broader
literature findings on tariff-choice bias (see Lambrecht and Skiera 2006). Thus, the estimated potential
saving in January appears to have little appeal for these participants. We hypothesise that these
participants may not be very active in January (e.g., on holiday). An analysis of trips booked via Tripi
confirms this. Particularly, while almost all subscribers to the Fifty50 and the Saver25 bundles started
travelling again in the first week of January (after the Christmas – New Year shutdown), 15/68 PAYG
users in January are still on holiday (i.e., made no trips via Tripi), and another eight PAYG users started
travelling again from the second week of January 2020 (i.e., having an extra week holiday).
Figure 22: Estimated monthly travel cost in January for PAYG users under different options

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Appendix F: Transport usage pattern of monthly subscribers vs. PayG users
The Fifty50 bundle was introduced in December 2019 and remained available for January 2020. The
marked difference between Fifty50 subscribers and PAYG users lies in the average number of public
transport trips (i.e., Opal) per person per week. During the period when only PAYG was offered (i.e.,
November 2019), those who later subscribed to the Fifty50 bundle show a much higher average number
of PT trips than those who have always been on PAYG since being onboarded. During December 2019
when the Fifty50 bundle was first introduced, the average number of PT trips by Fifty50 subscribers
reduced, possibly due to the Christmas and New Year shutdown period; however, their level of PT usage
was still higher than that of the PAYG users (which appears to be more stable presumably due to a larger
sample size – i.e., more participant on PAYG than on the Fifty50 bundle in December 2019). Error!
Reference source not found.Figure 23 also shows that the use of transport modes other than PT were
remarkably similar between the Fifty50 subscribers and PAYG participants.
Figure 23: How Fifty50 subscribers use different transport modes compared to PAYG users
In a similar way, Figure 24Error! Reference source not found. compares the travel behaviour of
Saver25 subscribers with that of PAYG users. As can be seen, Saver25 subscribers showed a lower level
of PT use while a higher level of Taxi/Uber travel during the period up to 1 January 2020 when this
bundle was first available to the participants. The lower entry barrier of the Saver25 bundle (a
subscription fee of $25 per month) may have been one of the main appealing features of this bundle

47
which has 10 subscribers in January, compared to the Fifty50 which has 14 subscribers in the same
month. This evidence aligns well with the view of evolving personas identified for MaaS by Smith et
al. (2018).
Thus, analysis evidence suggests that we have two good bundles in place to cater for different segments
of the 90 participants to date (mid-January 2020). The Saver25 bundle is more attractive to participants
with a lower level of PT use (around 4 – 5 trips per week) and one or two weekly Uber/Taxi trips. The
Fifty50 bundle is appealing to participants with a higher level of PT use (around 8 – 10 trips/week in
November 2019).
Figure 24: How Saver25 subscribers use different transport modes compared to PAYG users

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Appendix G: Communication emails introducing March bundle offers