Personalisation in Mobility-as-a-Service: Where We Are and How to
Move Forward
Kamaldeep Singh Oberoi
CESI LINEACT, Toulouse, 31670, France
Mobility-as-a-Service, Personalisation, User Preferences, Optimal Route Recommendation, User Privacy,
User Feedback.
Within urban mobility ecosystem, Mobility-as-a-Service (MaaS) has come up as a promising approach to pro-
mote sustainable modes of transport and increase the attractiveness of public and shared multimodal mobility.
It aims to become a viable alternative to personal cars for door-to-door trips. The long-term objective of MaaS
is to change the people’s travel behaviour by nudging them to make sustainable choices. However, changing
people’s travel behaviour is not easy and MaaS has to provide personalised mobility services, catering to the
needs of each individual user, in order to be considered as convenient as a personal car. In this paper, we look
at the existing literature on personalisation in MaaS proposed by the research community as well as different
private MaaS service providers. This brief literature review helps in better understanding the current trends on
personalisation and highlights certain limitations in the way it is incorporated within existing MaaS solutions.
Based on these limitations, we discuss certain challenges which need to be resolved in order to improve MaaS
in the future. These challenges present interesting research directions towards the development of personalised
sustainable urban mobility ecosystem.
Over the past few years, European Commission has
been encouraging the development of Sustainable Ur-
ban Mobility Plans (SUMP) (EU Urban Mobility Ob-
servatory, 2023) for European towns and cities to
tackle the challenges of climate change and urban
traffic congestion. SUMP address the complexity of
urban transport, integrate passenger and freight de-
mand, and promote sustainable modes of transport to
improve the overall quality of life.
To concretize the guidelines put forth within
SUMP (EU Urban Mobility Observatory, 2023),
cities need to improve their public transport infras-
tructure, integrate new modes of transport (such as
car and bike sharing, on demand transport, carpool-
ing, etc.) and develop platforms to improve the acces-
sibility of public and shared mobility options. It turns
out that the concept of Mobility-as-a-Service (MaaS)
(Hietanen, 2014) can help overcome the challenges in
implementing SUMP and, for a city, both SUMP and
MaaS can be integrated and developed hand in hand
(Signor et al., 2019).
Over the past few years, MaaS has gained signif-
icant interest in the urban mobility and transportation
sector (Hensher et al., 2020). In essence, it aims to
bring together different mobility services, provided
by a single or multiple Mobility Service Providers
(MSPs), on a single platform, with the possibility of
searching, booking, and paying for such multimodal
services directly on the platform (Hietanen, 2014).
Furthermore, it supports the vision of mobility-for-all
and pushes for access-based mobility services as com-
pared to ownership-based mobility models with the
objective of reducing the use of personal cars (Hen-
sher et al., 2020). The concept of MaaS places user
needs and environmental conservation at the center,
while aiming for a seamless integration of multimodal
mobility services.
One of the long-term objectives of MaaS is to
gradually change people’s travel behaviour and per-
suade them to make sustainable transport choices.
According to (Hensher et al., 2020), MaaS has the
ability to influence people’s travel behaviour, how-
ever, it cannot achieve this objective if people are not
incentivized to choose shared multimodal transport
over personal cars. The authors propose that in ad-
dition to deploying MaaS, cities should also reduce
Oberoi, K.
Personalisation in Mobility-as-a-Service: Where We Are and How to Move Forward.
DOI: 10.5220/0012687100003702
Paper published under CC license (CC BY-NC-ND 4.0)
In Proceedings of the 10th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS 2024), pages 352-360
ISBN: 978-989-758-703-0; ISSN: 2184-495X
Proceedings Copyright © 2024 by SCITEPRESS Science and Technology Publications, Lda.
the attractiveness of personal cars by restricting their
use in certain areas and imposing parking regulations.
However, instead of “punishing” people for
choosing personal cars, certain behaviour change
strategies (Durand et al., 2018) can be integrated
within MaaS which encourage people to choose sus-
tainable modes of transport. Such strategies could im-
prove the overall attractiveness of shared multimodal
transport as well as the acceptance rate of MaaS as a
In this paper, we focus on one such strategy to sup-
port behaviour change, personalisation. We discuss
the aspect of personalisation within existing MaaS
research and MaaS platforms developed by private
MaaS service providers. After briefly presenting the
concept of MaaS in Section 2.1, we discuss the im-
portance of personalisation in Section 2.2, and how
it is integrated in existing MaaS solutions in Section
2.3. From this brief review of the state-of-the-art, we
are able to find some limitations in personalisation in
existing MaaS solutions. These limitations are dis-
cussed in Section 2.4. In Section 3, we present cer-
tain challenges in order to improve personalisation in
MaaS. These challenges are related to data availabil-
ity, data standardization and data sharing between dif-
ferent stakeholders, respecting user privacy while pro-
cessing and storing the collected data, integration of
dynamic user preferences, and proactive route recom-
mendations. We also discuss solutions to overcome
these challenges highlighting fascinating research di-
rections. Finally, Section 4 concludes the paper.
2.1 Brief Overview
Inspired by the business model of telecommunica-
tion sector, Hietanen (Hietanen, 2014) proposed the
concept of Mobility-as-a-Service (MaaS) as Netflix of
transportation where users would subscribe in order
to use different mobility services provided by a mo-
bility operator who, in turn, would be responsible for
integrating such services onto a single platform. On
the platform, users would be presented with different
combinations of transport modes as well as different
payment options (like mobility bundles, monthly sub-
scription plans, etc.) which the user could choose ac-
cording to his/her preferences and requirements. The
idea was to develop the transportation system with,
to and by the users as an interconnected ecosystem
consisting of transport infrastructure, transportation
services, transport information and payment services
(Hietanen, 2014).
As the concept of MaaS gained traction, more def-
initions were proposed. For example, (Burrows et al.,
2016) defined MaaS as the provision of transport as
a flexible, personalised on-demand service that inte-
grates all types of mobility opportunities and presents
them to the user in a completely integrated manner
to enable them to get from A to B as easily as pos-
sible. Similarly, (Kamargianni et al., 2018) defined
MaaS as user-centric, intelligent mobility manage-
ment and distribution system, in which an integra-
tor brings together offerings of multiple mobility ser-
vice providers, and provides end-users access to them
through a digital interface, allowing them to seam-
lessly plan and pay for mobility.
In order to facilitate the understanding of MaaS,
(Sochor et al., 2018) reviewed multiple definitions
and proposed a topology to compare existing MaaS
platforms. According to this topology, MaaS could
be perceived at four different levels (from level 1 to
level 4), each improving the services integrated at
lower levels, ranging from the integration of informa-
tion such as route planner, price, and environmental
cost of a route (level 1) and payment options (levels
2 and 3) to the integration of societal goals (level 4)
defined within government policies to incentivize the
use of MaaS at large scale. Similarly to the work of
(Sochor et al., 2018), other topologies that character-
ize MaaS have also been proposed in the literature.
For example, (Opiola, 2018) considers MaaS at six
different levels, with higher levels going beyond mo-
bility and integrating other digital services (like smart
homes and IoT) within MaaS. Taking into account the
cognitive effort required to undertake a multimodal
route, (Lyons et al., 2019) proposed a topology hav-
ing five levels of integration, with higher levels as-
sociated to the requirement of lower cognitive effort.
It is argued that, in order for MaaS to compete with
the convenience of personal cars, it should be as easy,
convenient, and flexible for everyone.
Out of various definitions explored in (Sochor
et al., 2018), more than half describe MaaS as a per-
sonalised service for planning, booking, paying and
executing the trips. Furthermore, from the differ-
ent topologies discussed above (Sochor et al., 2018;
Opiola, 2018; Lyons et al., 2019), one can note
that, at higher levels, MaaS integrates multiple modes
of transport with various payment options, while
proposing personalised mobility services catered to
the needs of the user. The importance of person-
alisation within MaaS is evident not only from the
existing literature, but users also want MaaS plat-
forms which provide personalised support, as noted
by (Polydoropoulou et al., 2020) through a user sur-
vey and a focus group based study.
Personalisation in Mobility-as-a-Service: Where We Are and How to Move Forward
2.2 Role of Personalisation in MaaS
In the field of information systems, personalisation
is defined as the automatic adjustment of informa-
tion content, structure, and presentation tailored to an
individual user (Perugini and Ramakrishnan, 2003).
It takes into account the user’s preferences and past
habits while presenting relevant information or con-
tent at the time it is required (Gao et al., 2010), ideally
without any explicit demand from the user (Mulvenna
et al., 2000). The aspect of personalisation plays a key
role in nudging the user and supporting him to change
his behaviour (Prost et al., 2013) as well as improves
the user’s trust in the system (Briggs et al., 2004).
In case of multimodal transportation, incorporat-
ing personalisation as a tool to persuade users in
changing their travel behavior has been discussed in
the literature (Anagnostopoulou et al., 2018; Hensher
et al., 2020). Furthermore, (Andersson et al., 2018)
reviewed different behaviour change support strate-
gies and concluded that personalisation plays a signif-
icant role in improving user satisfaction. Better satis-
faction motivates the user to keep using the service
which, in turn, has positive long-term effect in chang-
ing user behaviour. More so, personalisation has been
identified as a core characteristic for any MaaS ser-
vice as it makes it more attractive and improves its
rate of acceptance (Jittrapirom et al., 2017).
The availability of heterogeneous information
about different modes of transport and possible pay-
ment options within MaaS increases the complexity
of using the service and choosing the right combina-
tion. As noted in (Hartikainen et al., 2019), users
face difficulties in finding the relevant information
about, for example, the schedule of public transport
and where to buy tickets. This is especially true for
unfamiliar areas and is a reason for increased travel-
related stress (Hartikainen et al., 2019). Personalisa-
tion of the service helps in reducing the complexity
of multimodal travel by providing right combination
of modes and payment options at the right time. The
trial of MaaS platform NaviGoGo in Scotland (Smith,
2019) showed that personalisation makes traveling
easier and increases user’s self-confidence in travel-
ing using multiple modes. Simplifying multimodal
travel reduces the cognitive effort required to make
different choices, making MaaS as easy as using the
personal car (Lyons et al., 2019).
Along with improving user satisfaction and reduc-
ing travel-related stress, personalisation plays a sig-
nificant role in making recommendations to users.
Recommendations maybe be about possible routes,
between origin and a destination, with different
modes of transport available or about available pay-
ment options (e.g. mobility bundles, subscriptions,
pay-as-you-go, etc.) according to user requirements
(Arnaoutaki et al., 2021). To make appropriate rec-
ommendations, user preferences are incorporated in
terms of static user profiles (Arnaoutaki et al., 2019)
or computed using the past data (de Oliveira e Silva
et al., 2022).
In the following, we discuss some of the existing
research on personalisation within MaaS as well as
the features of existing MaaS platforms. The listed
features personalise the user experience, as noted by
the MaaS operators behind said platforms. Here, we
focus only on information personalisation and not on
the personalisation of payment options. For more in-
formation about the latter, the reader is directed to
(Arnaoutaki et al., 2021).
2.3 Personalisation in MaaS Research
and Existing MaaS Platforms
Within the MaaS research community, some work has
been done to provide personalised mobility services
according to the user’s requirements. For example,
(Melis et al., 2018) developed a microservice archi-
tecture to propose personalised and accessible routes
to the users. The motivation behind using a microser-
vice architecture is to make the software scalable by
distributing its modules as microservices (Dragoni
et al., 2017). The authors demonstrated the applica-
bility of this approach by considering the use cases
of a blind user and a tourist, and discussed the ser-
vices required to deploy a personalised route planner
for both use cases. In the proposed approach, user
preferences (such as audible traffic lights, tactile in-
formation boards, acoustic announcements, etc. for
blind users and scenic routes through historical build-
ings for tourists) were fixed while setting up the user
profile and were taken into account while calculating
the route between an origin and a destination.
Within the (MaaS4EU, 2020) project, (Georgakis
et al., 2020) proposed a personalised multimodal
route recommender integrating the information about
third-party services for bike and car sharing modes
as well as parking facilities. The route recommender
was based on the choice architecture design elements
where the users were provided with default route
choices between an origin and a destination which can
be filtered according to the user preferences (such as
user’s preferred walking and biking distances). Addi-
tionally, (Anagnostopoulou et al., 2020) constructed
the users’ “persuadability profiles” based on their per-
sonality types and proposed to attach personalised
messages to nudge users towards sustainable modes
of transport. The persuadability profiles described the
VEHITS 2024 - 10th International Conference on Vehicle Technology and Intelligent Transport Systems
Table 1: Features of some of the existing MaaS platforms which personalise the user experience. Indicated features are
susceptible to evolve over time.
MaaS Platforms
Sync with
Store fav.
Occupancy of
bike stations
parking lots
Tuup/Kyyti MaaS Platform
Compte Mobilit
users’ susceptibility to change their travel behaviour
and were used as a tool for supporting behaviour
change (Durand et al., 2018).
In addition to the ideas proposed by the research
community, some existing MaaS platforms, devel-
oped by private MaaS operators, have incorporate cer-
tain features to personalise the user experience. These
features are presented in Table 1. The list of plat-
forms in Table 1 is not exhaustive but it gives a gen-
eral idea of the level of personalisation in existing
MaaS platforms. It is noteworthy that we only con-
sider the features available on smartphone versions of
the platforms, those available on the web versions are
not considered.
All MaaS platforms have an integrated journey
planner with which users can search for a trip using
a combination of multiple modes available. Most of
the platforms consider public transport, bike and car
sharing, ride-hailing services etc. but some platforms
like Moovit and Ecomode also integrate carpooling
as a possible mode of transport. At the time of in-
stallation, the users are asked to register using their
personal information such as age, email, phone num-
ber etc. (which is common for most platforms), but
some platforms such as WienMobil, NaviGoGo, Op-
tymod’Lyon and Ecomode ask for extra information,
such as if the user owns a bicycle, a car etc. and how
far is the user willing to walk in order to take a public
or shared mode of transport. This information is used
to create the user’s profile which helps in filtering the
route propositions presented to the user.
It is also possible for users to synchronise their
personal calendars with MaaS platforms in order to
receive automatic alerts about their trips which re-
spect their daily constraints. Moreover, the option for
storing preferred routes and locations eliminates the
hassle to search for regular routes before every trip.
Platforms like WienMobile and TransitApp display
the carbon footprint with each route and incentivize
users to choose sustainable options. In order to alert
the user about traffic jams and delays in public trans-
port, platforms such as TripGo, Optymod’Lyon, Mo-
biPalma, and TransitApp take into account the real-
time traffic information. In addition, TransitApp up-
dates the bus schedules automatically when they are
changed, for example, on weekends or during rush
hour traffic.
Complementary to the ones presented in Table 1,
platforms such as MobiPalma, Moovit and Moovizy
integrate additional features to promote inclusive mo-
bility as their services are available for people trav-
elling with wheelchairs or those with visual impair-
2.4 Limitations in Personalisation in
Existing MaaS Solutions
Although the existing work, both from the research
community and from private MaaS operators, is
promising, there still exist some limitations which
need to be resolved in order to improve the aspect
of personalisation of MaaS. For example, user pref-
erences and requirements need to be better taken into
account and user feedback should be automatically in-
tegrated to enhance the service provided (according to
the definition of the term personalisation presented in
Personalisation in Mobility-as-a-Service: Where We Are and How to Move Forward
Section 2.2). More importantly, user privacy should
not be taken lightly. Users should be consented ev-
ery time before processing their personal data (if done
on third-party servers) or it should be stored and pro-
cessed locally on their smartphones.
In the following, we briefly discuss these user-
centric limitations of existing MaaS solutions:
User Preferences: Existing platforms are able
to filter the routes according to user preferences
(such as shorter walking distance, less carbon
footprint, less travel time etc.). However, user
preferences, once integrated, are kept static. To
the best of our knowledge, there does not exist
a platform which automatically updates the user
preferences over time. Furthermore, as discussed
above, (Melis et al., 2018) take into account the
static preferences of a blind user. However, the
user is obligated to declare a personal health con-
dition in order to use the personalised service.
This might have an impact on the acceptability
of platforms which are based on such methodolo-
User Feedback: Most of the existing MaaS plat-
forms demand explicit feedback from the users
about their experience with the platform. How-
ever, feedback about the quality of the routes pro-
posed and whether they conform to the user’s re-
quirements and context of travel is not considered.
It has been shown that integrating user feedback
makes the system more user-friendly (Belhajjame
et al., 2011; Klinkm
uller and Weber, 2021), how-
ever, current MaaS platforms are still limited in
this aspect.
User Privacy: One of the main issue with exist-
ing MaaS platforms is related to user privacy. Pri-
vacy policies of platforms such as Whim, Tran-
sitApp and Moovit are publicly available to en-
lighten the user about storage and potential use
of her data. However, these policies are not easy
to understand and sometimes provide conflicting
information (Cottrill, 2020). More importantly,
these policies are limited to the responsibilities of
MaaS service providers, who store the user data
on third-party servers, but do not extend to the
companies maintaining these servers who are ac-
tually responsible for securing the data (Cottrill,
2020). Hence, the actual impact of existing MaaS
privacy policies is fairly limited.
In addition to the above mentioned user-centric
limitations, features enabling the personalisation in
existing MaaS platforms need further improvements
as well. For example, if a user prefers to use her bi-
cycle or personal car in combination with public or
shared modes of transport, she should be provided
with this option. Existing multimodal route planners
need to be improved to make “true” multimodality a
feasible option.
Some existing MaaS platforms such as Ecomode
and Moovit propose carpooling as a mode of trans-
port. Moovit takes into account the needs of users
acting as passengers and users acting as drivers sep-
arately. However, a user who uses carpooling both
as driver and passenger is left out. Putting in place
an efficient carpooling system is a challenge in itself
(partly due to the complexity of ride-matching algo-
rithms (Zafar et al., 2022)), but integrating carpooling
with other modes of transport while taking into ac-
count varying preferences of passengers and drivers
as well as the contextual information (such as if the
passenger has luggage with him and if the driver’s car
has enough space to safely store it during the trip) fur-
ther increases the complexity. There is a need to look
for technical solutions in the form of efficient opti-
misation algorithms which would integrate carpool-
ing as an attractive choice in the multimodal mobil-
ity space. Having discussed some user-centric and
feature-centric limitations in personalisation in exist-
ing MaaS solution, in the next section, we discuss
how these limitations can be resolved to make MaaS a
personalised, secure, flexible and trustworthy choice
for daily use.
As discussed before, personalisation refers to present-
ing the tailored content to the user, according to her
preferences, without explicit demand. Within MaaS,
where mobility services are provided by different ser-
vice providers, public and shared transport infrastruc-
ture is highly dynamic, and user preferences vary over
time, personalising the information in real-time does
not happen without challenges. In this section, we
discuss some of these challenges and possible solu-
tions to overcome them.
3.1 Integration of Dynamic User
Existing MaaS solutions take as input the user data
(such as age, gender, preferred walking distance, does
the user own a bicycle etc.) at the time of registration
to set up user profile which is used to filter out in-
compatible routes. Some of these data (like preferred
walking distance) define individual user preferences
VEHITS 2024 - 10th International Conference on Vehicle Technology and Intelligent Transport Systems
whereas other data (such as age, gender etc.) define
preferences for a category of users. The user cate-
gorisation can help in understanding preferences at a
higher level, however, in order to improve the large-
scale acceptability of MaaS, individual user require-
ments and preferences need to be better incorporated
into the system (Durand et al., 2018). In addition,
user preferences are susceptible to change over time
and they need to be updated in order to recommend
optimal routes and/or payment options to each user.
One way to take into account individual user pref-
erences is for the MaaS platform to observe the user in
his daily routine and collect data about his trips using
sensors (such as GPS and accelerometer) embedded
in his smartphone. These data can be processed to ex-
tract the user’s regular movement behaviour and auto-
matically learn his preferences (such as his preferred
departure time, preferred mode of transport, number
of times he changes the mode of transport etc.).
There exist numerous approaches to process GPS
and accelerometer data collected from the smart-
phones (Zheng, 2015; Hemminki et al., 2013; Zhang
et al., 2023). Within the context of MaaS, the user’s
movement data could be processed in real-time in an
online fashion by considering the incoming data as
a stream (Gaber, 2012). Although stream data min-
ing has been applied to numerous use cases, there ex-
ist certain challenges, such as handling delayed in-
formation, two-phased (offline and online) data pro-
cessing, analysing complex data, etc. which need to
be resolved in order to apply data stream mining to
real-world applications (Krempl et al., 2014). Some
of these challenges can be overcome using adap-
tive multi-agent system based methodologies, as dis-
cussed in (Grachev et al., 2020).
Once the GPS and accelerometer data is processed
to compute the user’s regular movement behaviour,
his travel preferences can be extracted from this in-
formation. The Recommendation Systems research
community has made huge contributions in model-
ing and updating user preferences while incorporating
user feedback (de Gemmis et al., 2009). For exam-
ple, collaborative-filtering based techniques consider
user ratings given to various items by different users
and tries to group users with similar interests so as
to recommend similar items to them. Content-based
recommender systems look at the content of the items
being recommended and the attributes describing an
item, and check if they match with user interests. It
has been observed that incremental preference learn-
ing, where the preferences are updated in real-time is
better than batch processing (Gallacher et al., 2013)
for highly dynamic use cases (such as MaaS). Hence,
such methods need to be incorporated within MaaS to
learn user preferences in real-time and personalise the
service provided.
3.2 Proactive Route Recommendations
Once the user preferences have been extracted from
the user’s travel data, they can be employed to recom-
mend optimal routes to the user. Personalised MaaS
should recommend such routes proactively, without
any explicit demand from the user (as discussed in
Section 2.2). From the user’s regular movement be-
haviour, it can be anticipated that he/she needs to take
a trip, between an origin and a destination, at a given
time. Then, various possible routes between origin
and destination can be ranked according to the user’s
Recommender systems use ranking models to ei-
ther calculate the rank (score) of a particular item
(Score-based ranking) or use supervised machine
learning where the score function is learned using
some labeled training data (Learn-to-Rank approach)
(Zehlike et al., 2022). Although these methods pro-
vide accurate recommendations, they might lead to
“overly accurate” recommendations over time (Mc-
Nee et al., 2006). The problem is that, generally, the
recommender systems are developed to be accurate
and over long-term they tend to recommend similar
items which the user has used in the past.
This approach is problematic, especially for
MaaS, since the objective of MaaS is to promote sus-
tainable modes of transport and gently “nudge” the
user to change her behaviour. If the route recom-
mender system integrated within MaaS keeps making
recommendations similar to the user’s actual and past
behaviour (which might not be sustainable enough),
it would not be useful. Hence, from time-to-time
MaaS route recommendation system needs to make
unexpected and novel but relevant recommendations
to promote sustainable options. This kind of recom-
mendation is referred to as serendipitous recommen-
dation (Ziarani and Ravanmehr, 2021). The challenge
in implementing such serendipitous route recommen-
dation system is the potential trade-off between what
the user needs at a given moment depending on her
past behaviour and preferences, and what and how
often novel recommendations can be made to change
her behaviour. This presents an interesting research
opportunity for the MaaS community.
3.3 Privacy-Aware Data Storage and
While learning user’s regular movement behaviour,
extracting his/her preferences and recommending op-
Personalisation in Mobility-as-a-Service: Where We Are and How to Move Forward
timal routes, his/her privacy should not be put aside.
With this privacy-centric vision for MaaS, the ques-
tion of data storage and processing presents a huge
challenge. One solution is to store (with user’s con-
sent) and process the data on third-party servers.
However, this does not fully respect the criteria of
user privacy and also suffers from latency issues. A
better approach for large data storage and processing
is based on Mobile Edge Computing (MEC) (Mach
and Becvar, 2017). MEC, a standard edge computing
architecture, is different from traditional cloud com-
puting since, with MEC, the data is processed in close
proximity (in terms of mobile network topology) to
the device which collects the said data. The idea is to
exploit the capabilities of nearby (to a source device)
mobile IoT-enabled devices to collectively process the
data and send it back to the source device. This leads
to improved latency and faster overall processing but
there still exist certain privacy concerns (Ranaweera
et al., 2021).
For MaaS applications, MEC provides an interest-
ing alternative to store and process huge amounts of
data (Xie et al., 2022). However, MEC is a relatively
recent concept and comes with its own challenges
such as optimised resource allocation, transparency,
etc. (Ahmed and Rehmani, 2017). Overcoming these
challenges present the objectives of recently launched
EU funded (EMERALDS, 2023) project.
Recently, Federated Learning (FL) based methods
have also been proposed to resolve the problem of
user data privacy in MaaS (Chu and Guo, 2023). FL is
a type of machine learning approach which is specif-
ically designed to preserve data as well as model pri-
vacy (Zhang et al., 2021). In FL, the machine learning
model is trained locally on the device which collects
the data. In this manner, the data and the model are
kept private and only the results are shared with other
devices through a central server (in case of centralised
FL) or directly over the network (distributed FL). Al-
though interesting, FL based approaches are not with-
out problems (Lyu et al., 2020) which require further
investigations, especially in case of MaaS.
3.4 Data Availability, Standardization
and Sharing
Finally, one of the major challenges in personalis-
ing mobility services is the lack of available real-
time data (Maas, 2022). The data collected by differ-
ent mobility service providers is not made available
since most of these service providers are private en-
tities and start-ups which have their financial needs
in mind, and sharing their data with their competitors
does not help their business. As mentioned in the re-
port published by the House of Commons Transport
Committee of the U.K. Parliament (House of Com-
mons Transport Committee, 2018), it is the responsi-
bility of the public authorities to get private businesses
onboard and incentivize them to share their data with
each other in order to develop an intelligent mobility
In order to share the data collected by different
partners and promote interoperability, data standards
need to be set up and common application program-
ming interfaces (APIs) need to be developed to pro-
cess the collected data (Polydoropoulou et al., 2020).
Such APIs should provide access to real-time data
about the state of road traffic, occupancy and loca-
tion of all public and shared modes of transport avail-
able, users’ location and travel-related data etc. to
all the involved stakeholders in a secured manner. At
this stage, the responsibilities of all public and private
stakeholders about data sharing and data ownership
should also be specified. The availability, standard-
ization and sharing of real-time data between different
service providers will help in improving the feature-
centric limitations discussed above.
Furthermore, it is also possible that the available
mobility services evolve over time with the addition
of new forms of mobility proposed by new mobility
partners. For their easy integration within existing in-
frastructure, micro-service architecture for data stor-
age, processing and sharing could be a viable option
(Dragoni et al., 2017; Maas, 2022).
Mobility-as-a-Service (MaaS) has grown into a
promising solution integrating multimodal transport
services, including public and shared modes, onto
a single platform with the possibility of searching,
booking and paying for using such mobility services.
It aims to promote sustainable mobility, change peo-
ple’s travel behaviour over time, and reduce their de-
pendence on personal cars. To become as convenient
as personal cars, MaaS needs to propose personalised
mobility services tailored to the needs of each indi-
vidual user.
In this paper, we presented a brief literature review
on the aspect of personalisation in existing MaaS re-
search and MaaS platforms. Exploring various defi-
nitions of MaaS, we found that most of them describe
it as a personalised service. This review then goes on
to highlight current trends on personalisation within
MaaS and lists some existing limitations related to
integrating user preferences, user feedback and user
privacy. Overcoming these limitations in the context
VEHITS 2024 - 10th International Conference on Vehicle Technology and Intelligent Transport Systems
of MaaS comes with certain challenges. The paper
presents technical solutions which need to be stud-
ied by the research community and integrated within
MaaS platforms. For example, instead of only relying
on static user preferences to filter routes, MaaS plat-
forms should also include their dynamic aspect. User
preferences should be learnt and updated over time as
the user continues to interact with the platform so as
to make appropriate recommendations whenever re-
quired. Trade-off between recommendations accord-
ing to user behaviour and sustainable mode choices
needs to be further studied so as to nudge the user
towards environment friendly mobility options. In-
stead of storing and processing user data on third-
party servers, system architecture inspired from Mo-
bile Edge Computing or data processing algorithms
based on Federated Learning should be integrated
within MaaS to store and process user data while pre-
serving user privacy. The proposed solutions present
exciting future research directions for improving per-
sonalisation within MaaS.
Ahmed, E. and Rehmani, M. H. (2017). Mobile edge com-
puting: Opportunities, solutions, and challenges. Fu-
ture Generation Computer Systems, 70:59–63.
Anagnostopoulou, E., Bothos, E., Magoutas, B., Schram-
mel, J., and Mentzas, G. (2018). Persuasive tech-
nologies for sustainable mobility: State of the art and
emerging trends. Sustainability, 10(7).
Anagnostopoulou, E., Urban
c, J., Bothos, E., et al. (2020).
From mobility patterns to behavioural change: lever-
aging travel behaviour and personality profiles to
nudge for sustainable transportation. Journal of In-
telligent Information Systems, 54(1):157–178.
Andersson, A., Winslott Hiselius, L., and Adell, E. (2018).
Promoting sustainable travel behaviour through the
use of smartphone applications: A review and devel-
opment of a conceptual model. Travel Behaviour and
Society, 11:52–61.
Arnaoutaki, K., Bothos, E., Magoutas, B., Aba, A., Es-
ar-Kiss, D., and Mentzas, G. (2021). A recom-
mender system for mobility-as-a-service plans selec-
tion. Sustainability, 13(15).
Arnaoutaki, K., Bothos, E., Magoutas, B., and Mentzas,
G. (2019). Personalization and recommendation tech-
nologies for maas. In 2nd International Conference
on Mobility as a Service.
Belhajjame, K., Paton, N. W., Fernandes, A. A., Hedeler,
C., and Embury, S. M. (2011). User feedback as a first
class citizen in information integration systems. In
5th Biennial Conference on Innovative Data Systems
Research, pages 175–183.
Briggs, P., Simpson, B., and De Angeli, A. (2004). Per-
sonalisation and Trust: A Reciprocal Relationship?,
pages 39–55. Springer Netherlands.
Burrows, A., Bradburn, J., and Cohen, T. (2016). Journeys
of the future: Introducing mobility as a service. Tech-
nical report, Atkins Ltd.
Chu, K.-F. and Guo, W. (2023). Privacy-preserving fed-
erated deep reinforcement learning for mobility-as-a-
service. IEEE Transactions on Intelligent Transporta-
tion Systems, pages 1–15.
Cottrill, C. D. (2020). Maas surveillance: Privacy consid-
erations in mobility as a service. Transportation Re-
search Part A: Policy and Practice, 131:50–57. De-
velopments in Mobility as a Service (MaaS) and In-
telligent Mobility.
de Gemmis, M., Iaquinta, L., Lops, P., Musto, C., Narducci,
F., and Semeraro, G. (2009). Preference learning in
recommender systems. In ECML/PKDD-09 Work-
shop on Preference Learning, pages 41–55.
de Oliveira e Silva, R. A., Cui, G., Rahimi, S. M., and
Wang, X. (2022). Personalized route recommendation
through historical travel behavior analysis. GeoInfor-
matica, 26(3):505–540.
Dragoni, N., Giallorenzo, S., Lafuente, A. L., et al.
(2017). Microservices: Yesterday, Today, and Tomor-
row, pages 195–216. Springer International Publish-
Durand, A., Harms, L., Hoogendoorn-Lanser, S., and Zijl-
stra, T. (2018). Mobility-as-a-service and changes in
travel preferences and travel behaviour: a literature re-
view. Technical report, Ministry of Infrastructure and
Water Management, The Netherlands.
EMERALDS (2023). Extreme-scale Urban Mobility
Data Analytics as a Service. https://www.emeralds- Accessed January 2024.
EU Urban Mobility Observatory (2023). Sustainable
urban mobility plans. https://urban-mobility-
mobility-plans. Accessed January 2024.
Gaber, M. M. (2012). Advances in data stream min-
ing. WIREs Data Mining and Knowledge Discovery,
Gallacher, S., Papadopoulou, E., Taylor, N. K., and
Williams, M. H. (2013). Learning user preferences
for adaptive pervasive environments: An incremental
and temporal approach. ACM Transactions on Au-
tonomous and Adaptive Systems, 8(1).
Gao, M., Liu, K., and Wu, Z. (2010). Personalisation in web
computing and informatics: Theories, techniques, ap-
plications, and future research. Information Systems
Frontiers, 12(5):607–629.
Georgakis, P., Almohammad, A., Bothos, E., et al. (2020).
Heuristic-based journey planner for mobility as a ser-
vice (maas). Sustainability, 12(23).
Grachev, S., Skobelev, P., Mayorov, I., and Simonova, E.
(2020). Adaptive clustering through multi-agent tech-
nology: Development and perspectives. Mathematics,
Hartikainen, A., Pitk
anen, J.-P., Riihel
a, A., R
anen, J.,
Sacs, I., Sirki
a, A., and Uteng, A. (2019). Whimpact:
Insights from the world’s first mobility-as-a-service
Personalisation in Mobility-as-a-Service: Where We Are and How to Move Forward
(maas) system. Technical report, MaaS Global Ram-
Hemminki, S., Nurmi, P., and Tarkoma, S. (2013).
Accelerometer-based transportation mode detection
on smartphones. In Proceedings of the 11th ACM
Conference on Embedded Networked Sensor Systems.
Association for Computing Machinery.
Hensher, D. A., Ho, C. Q., Mulley, C., Nelson, J. D., Smith,
G., and Wong, Y. Z. (2020). Understanding Mobility
as a Service (MaaS): Past, Present and Future. Else-
Hietanen, S. (2014). Mobility as a service - the new trans-
port model? Eurotransport-ITS & Transport Manage-
ment Supplement, 12(2):2–4.
House of Commons Transport Committee (2018). Mobil-
ity as a service : Eighth report of session 2017–19.
Technical report, U.K. Parliament.
Jittrapirom, P., Caiati, V., Feneri, A.-M., Ebrahimighare-
hbaghi, S., Gonz
alez, M., and Narayan, J. (2017). Mo-
bility as a service: A critical review of definitions,
assessments of schemes, and key challenges. Urban
Planning, 2(2):13–25.
Kamargianni, M., Matyas, M., Li, W., Muscat, J., and Yfan-
tis, L. (2018). The maas dictionary. Technical report,
MaaSLab, Energy Institute, University College Lon-
uller, C. and Weber, I. (2021). Every apprentice
needs a master: Feedback-based effectiveness im-
provements for process model matching. Information
Systems, 95:101612.
Krempl, G.,
Zliobaite, I., Brzezi
nski, D., H
ullermeier, E.,
Last, M., Lemaire, V., Noack, T., Shaker, A., Sievi, S.,
Spiliopoulou, M., and Stefanowski, J. (2014). Open
challenges for data stream mining research. SIGKDD
Explor. Newsl., 16(1):1–10.
Lyons, G., Hammond, P., and Mackay, K. (2019). The im-
portance of user perspective in the evolution of maas.
Transportation Research Part A: Policy and Practice,
Lyu, L., Yu, H., and Yang, Q. (2020). Threats to federated
learning: A survey. arXiv preprint arXiv:2003.02133.
Maas, B. (2022). Literature review of mobility as a service.
Sustainability, 14(14).
MaaS4EU (2020). Horizon2020 MaaS4EU Project. Accessed
January 2024.
Mach, P. and Becvar, Z. (2017). Mobile edge comput-
ing: A survey on architecture and computation of-
floading. IEEE Communications Surveys & Tutorials,
McNee, S. M., Riedl, J., and Konstan, J. A. (2006). Be-
ing accurate is not enough: How accuracy metrics
have hurt recommender systems. In CHI ’06 Extended
Abstracts on Human Factors in Computing Systems,
page 1097–1101.
Melis, A., Mirri, S., Prandi, C., Prandini, M., Salomoni,
P., and Callegati, F. (2018). Integrating personalized
and accessible itineraries in maas ecosystems through
microservices. Mobile Networks and Applications,
Mulvenna, M., Annual, S., and Buchner, A. (2000). Per-
sonalization on the net using web mining. Communi-
cation of the ACM, 43(8):122–125.
Opiola, J. (2018). Defining levels of maas. Traffic Technol-
ogy International, pages 70–71.
Perugini, S. and Ramakrishnan, N. (2003). Personalizing
web sites with mixed-initiative interaction. IT Profes-
sional, 5(2):9–15.
Polydoropoulou, A., Pagoni, I., and Tsirimpa, A. (2020).
Ready for mobility as a service? insights from stake-
holders and end-users. Travel Behaviour and Society,
Prost, S., Schrammel, J., R
oderer, K., and Tscheligi, M.
(2013). Contextualise! personalise! persuade! a mo-
bile hci framework for behaviour change support sys-
tems. In Proceedings of the 15th International Con-
ference on Human-Computer Interaction with Mobile
Devices and Services, page 510–515. Association for
Computing Machinery.
Ranaweera, P., Jurcut, A. D., and Liyanage, M. (2021).
Survey on multi-access edge computing security and
privacy. IEEE Communications Surveys & Tutorials,
Signor, L., Karjalainen, P., Kamargianni, M., Matyas, M.,
et al. (2019). Mobility as a Service (MaaS) and Sus-
tainable Urban Mobility Planning. Technical report,
ERTICO – ITS Europe.
Smith, D. (2019). Navigogo: Scotland’s first maas pilot.
Technical report, ESP Group.
Sochor, J., Arby, H., Karlsson, I. M., and Sarasini, S.
(2018). A topological approach to mobility as a ser-
vice: A proposed tool for understanding requirements
and effects, and for aiding the integration of societal
goals. Research in Transportation Business & Man-
agement, 27:3–14.
Xie, H., Song, X., and Zhang, H. (2022). Maas and iot:
Concepts, methodologies, and applications. In Zhang,
H., Song, X., and Shibasaki, R., editors, Big Data and
Mobility as a Service, pages 229–243. Elsevier.
Zafar, F., Khattak, H. A., Aloqaily, M., and Hussain, R.
(2022). Carpooling in connected and autonomous ve-
hicles: Current solutions and future directions. ACM
Computing Surveys, 54(10s).
Zehlike, M., Yang, K., and Stoyanovich, J. (2022). Fairness
in ranking, part ii: Learning-to-rank and recommender
systems. ACM Computing Surveys, 55(6).
Zhang, C., Xie, Y., Bai, H., Yu, B., Li, W., and Gao, Y.
(2021). A survey on federated learning. Knowledge-
Based Systems, 216:106775.
Zhang, D., Chang, Z., Yang, D., Li, D., Tan, K.-L., Chen,
K., and Chen, G. (2023). Squid: subtrajectory query
in trillion-scale GPS database. The VLDB Journal.
Zheng, Y. (2015). Trajectory data mining: An overview.
ACM Transactions on Intelligent Systems and Tech-
nology, 6(3).
Ziarani, R. J. and Ravanmehr, R. (2021). Serendipity
in recommender systems: A systematic literature re-
view. Journal of Computer Science and Technology,
VEHITS 2024 - 10th International Conference on Vehicle Technology and Intelligent Transport Systems