=Paper=
{{Paper
|id=Vol-2939/paper4
|storemode=property
|title=Linked MaaS: A Vision for Leveraging Semantic Web Technologies for Mobility as a Service
|pdfUrl=https://ceur-ws.org/Vol-2939/paper4.pdf
|volume=Vol-2939
|authors=Shams Ghazy,Jing Ying Wong,Pieter Colpaert,Yu Hoe Tang,Andy Chan
|dblpUrl=https://dblp.org/rec/conf/i-semantics/GhazyWCTC21
}}
==Linked MaaS: A Vision for Leveraging Semantic Web Technologies for Mobility as a Service==
https://ceur-ws.org/Vol-2939/paper4.pdf
Linked MaaS: a vision for leveraging Semantic
Web Technologies for Mobility as a Service
Shams Ghazy1 , Wong Jing Ying1 , Pieter Colpaert2 , Yu Hoe Tang1 , and Andy
Chan1
1
University of Nottingham Malaysia, Selangor, Malaysia
jingying.wong@nottingham.edu.my
2
Ghent University, 9052 Gent, Belgium
Abstract. Mobility as a Service (MaaS) promises to dissolve the bound-
aries of today’s transport network through the integration of transport
modes. However, stakeholders such as the operators, public transport au-
thorities, or end-user routing application developers, are facing interop-
erability barriers in the way of providing a seamless travel experience. To
identify, understand, and devise a plan for integration, we must first be
able to clearly define the interoperability requirements of MaaS. In this
paper, we propose a framework consisting of 6 layers: (i) the Stakeholder
layer, (ii) the Data layer, (iii) the Infrastructure layer, (iv) the Own-
ership layer, (v) the Auth layer, and (vi) the Connection layer. In each
layer, we outline the opportunities for Semantic Web technologies. In ad-
dition, each layer is equipped with use-cases to illustrate the application
of the framework. Through the Linked MaaS framework, we formulate a
baseline that extends the research focus of semantics in transportation
and builds a guideline for the development of an explicitly defined and
understood MaaS.
Keywords: Mobility as a Service · Linked Data · Semantic Web Tech-
nologies · Integrated Mobility · Semantics · Knowledge Graphs ·
Smart Mobility
1 Introduction
Mobility as a Service (MaaS), an emerging concept that promises to dissolve
the boundaries of today’s transport network, continues to face a manifold of
closed doors. The progress towards the seamless vision of MaaS, which provides
users with a carefree experience of planning, booking, and payment of transport
services, requires integration at multiple levels of the MaaS ecosystem, including
data integration for journey planning, ticketing and payment integration, and
the integration and alignment of stakeholder’s commercial goals. To identify, un-
derstand, and devise a plan for integration, we must first be able to clearly define
“What is a MaaS ecosystem and what are its interoperability requirements?”
Copyright © 2021 for this paper by its authors. Use permitted under Creative Com-
mons License Attribution 4.0 International (CC BY 4.0).
2 S. Ghazy et al.
It can be argued that having a clearer vision of what MaaS and its inter-
operability requirements are is not a significant solution to the barriers facing
MaaS. Yet, the lack of a common understanding of MaaS, its potential, and an
agreement over its fundamentals leads to an unresolved business model which, in
turn, leads to a lack of interest in stakeholders contributing to the development
of MaaS. In pursuit of finding the answer to this question, a thorough literature
review was used in an attempt to provide an unclouded understanding of MaaS.
However, there exists over 15 definitions attributed to ”Mobility as a Service”
in the literature with no agreed-upon definition [4]. Nevertheless, the literature
offered a clear view of the building blocks of MaaS out of which we were able
to derive a novel framework consisting of 6 intrinsic layers of the MaaS ecosys-
tem. These layers will model the MaaS ecosystem enabling a clear reference for
multiple questions about MaaS and within it, as well as, machine-powered an-
alytics, as further illustrated through the use-cases presented under each layer.
The framework has 3 Graph layers, (i) the Stakeholder layer - representing the
stakeholders of the system and the relationships between them, (ii) the Data
layer - representing MaaS data entities, their properties, and interconnections,
and (iii) the Infrastructure layer - forming a digital twin of the physical and digi-
tal infrastructure in the ecosystem. Between these layers, the framework includes
three linking layers, (iv) the Ownership layer - linking stakeholders and their in-
frastructure assets, (v) the Auth layer - modeling data management, ownership,
and access rules for stakeholders, and (vi) the Connection layer - modeling the
connectivity of the infrastructure. In this paper we propose this framework as
a guiding tool for an explicitly defined and understood MaaS ecosystem. In ad-
dition, we highlight the opportunities for using Semantic Web technologies as a
solution to complications present in each layer.
The paper begins by presenting the background behind the study and a
summary of the related work. Subsequently, an overview of the Linked MaaS
framework is presented which is then broken down to its constituent layers. Each
layer is explained thoroughly demonstrating its applicability through example
use-cases. The paper is then concluded with the vision for the Linked MaaS
framework and a few examples of synergistic use-cases that benefit from the
framework holistically.
2 Related Work
When MaaS was first coined by Hietanen [7], he referred to the ecosystem as
a combination of transportation services, information, infrastructure, and pay-
ment services. Kamargianni and Matyas [8] outlined a business ecosystem for
MaaS which consists of the multiple organizations and actors of MaaS. Hensher
et al. [6] developed one of the recent MaaS definitions, encapsulating the main el-
ements of MaaS, which includes multimodality, user-centricity, sustainable goals,
payment and ticketing integration and more. The definition by Hensher et al. [6]
is the reference definition for this paper, however, it will not be quoted directly
due to its length. Spencer [12] characterized progress as the shift from homo-
The Linked MaaS Framework 3
geneity of structure to heterogeneity of structure in a system which increases
the complexity of the structure. With the increase of complexity in ecology, the
term ecosystem was defined as a network of interconnected organisms and in-
organic resources which build their distinct domain of analysis [13]. Analogous
to interconnected organisms, are interconnected firms in an economic ecosystem
[1]. Presently, Mobility-as-a-Service is inherently a set of densely interconnected
and heterogeneous firms and resources that constitute a multilayered ecosys-
tem. This interconnection sparks the main interest behind this framework being
built specifically under MaaS context. As transportation, in general, does not
necessitate the integration and harmonization of these resources, whereas MaaS
does.
Meadows [9] stated that a system must consist of three kinds of things: el-
ements, interconnections, and a function or purpose. We conducted a thorough
exploration of the literature on MaaS which encapsulated an in-depth biblio-
graphic analysis of the full record of 347 articles on MaaS, extracted through
the query ALL=("Mobility as a Service") from Web of Science. The query3
was performed on July 20th, 2021. The result of a co-occurrence analysis showed
that all tangible concepts fall under one of three categories: (1) Business, Policy,
and Stakeholders, (2) Data and Technology, and (3) Infrastructure (Physical and
Digital). We argue that there exists a lot of discrepancies over the semantics of
MaaS elements which has lead to a cascade of incompatibilities at various layers
of the system. Such incompatibilities gave rise to a major obstacle to MaaS which
is the lack of agreement or keenness of different parties of interest to participate
in the ecosystem [2]. Through the proposed framework, we aim to provide a
guideline that aids in eliminating barriers to MaaS semantic interoperability on
a conceptual, technological, and organizational level.
A notable framework, by Sochor et al. [11], introduced 5 levels of MaaS,
each representing the maturity of integration: (0) no integration, (1) integra-
tion of information, (2) integration of booking and payment, (3) integration of
the service offer, including contracts and responsibilities, and (4) integration of
societal goals. The framework offers a clear description of each maturity level,
yet, it does not cover how can the interoperability be assessed and enhanced
in order to enable the next level of maturity for a specific MaaS setting. Smith
and Hensher [10] broke down MaaS into its fundamental pillars, offering a clear
description for each, in pursuit of developing a framework for MaaS policies. The
Linked MaaS framework builds on top of these pillars while formalizing them
in a machine-understandable format that can be used for advanced analytics.
It further enables the identification of interoperability gaps which can be filled
to upgrade the maturity of MaaS. In summary, the Linked MaaS framework
offers a novel contribution through its formalization of MaaS concepts, which is
envisioned to enable an extensible and future-proof MaaS analytics engine.
3
https://www.webofscience.com/wos/woscc/summary/76568eb3-b10f-4da6-ae74-
7f851a6ac50b-01778e84/relevance/1
4 S. Ghazy et al.
3 The Linked MaaS Framework
Fig. 1. Proposed structure of the Linked MaaS Framework
To compose a solution that harmonizes the semantics of MaaS, we break
down the MaaS ecosystem into its constituent sub-systems. Each sub-system is
illustrated as a layer of categorized MaaS elements and their interconnections,
with a distinct function for each layer. Together, these layers constitute the
Linked MaaS framework as summarized in Figure 1. This framework is specific
to MaaS, and not transport in general, in the sense that it focuses on enabling
transport integration through MaaS. The framework consists of 3 Graph layers
– comprised of both nodes and relationships:
1) The Stakeholder Layer,
2) The Data Layer,
3) The Infrastructure Layer,
The Graph layers can be viewed as an independent domain of analysis. In
addition, we define 3 Linking layers – comprised of relationships only:
4) The Ownership Layer - Linking between Stakeholders and Infrastructure
5) The Auth Layer - Linking between Stakeholders and Data
6) The Connection Layer - Linking between Data and Infrastructure
The proposed Linked MaaS framework introduces 6 interlinked layers, that
each can benefit from using Semantic Web Technologies (SWT) to model, anal-
yse, and formulate solutions for issues within each layer. Throughout the fol-
lowing subsections, we will discuss the functions of each layer and the reasoning
The Linked MaaS Framework 5
behind its development. Furthermore, we will discuss the recommended Seman-
tic Web technologies for different use-cases in each layer, in addition to, other
supplementary technologies.
3.1 Layer 1: The Stakeholder Layer
The first layer of the Linked MaaS framework is The Stakeholder Layer. As
the name suggests, this layer consists of the stakeholders of MaaS as the el-
ements of focus, whilst also modelling the interconnections between them. In
MaaS workflows, it is often the case that a single decision can have direct or
indirect effects on any MaaS stakeholder, whether taken collectively or individ-
ually. In such a case, delineating the relationships and dependencies between
these key players is needed. The main function of this layer is to build an ab-
stract model of the business ecosystem of MaaS. Kamargianni and Matyas [8]
provide a suitable starting point for identifying the stakeholders present in to-
day’s landscape. For each stakeholder, an explicit definition will be given that
delineates their characteristics and roles. An instance of any stakeholder class
shall inherit its properties as well as possess a unique ID for their identification
on the Web. In addition, an explicit representation of the relationships and de-
pendencies between each stakeholder will be defined. Some of the independent
use-cases envisioned for the stakeholder layer are listed below:
– Standard and shared definitions of the different stakeholders can be used for
applying policies and conditions for participating in the ecosystem (e.g. A
transport operator must have a registration number with a specific author-
ity).
– Convenient retrieval of information on stakeholders and their connections
will be enabled (e.g. querying the available MaaS providers in a specific
region).
– Analysis of the effects of different decisions and scenarios on the system,
perhaps using the Decisions Ontology4 with the aid of a systems theory
approach. These decisions can include the joining or exiting of a particular
type of stakeholder into/from the ecosystem, the testing of different business
models, etc.
– Analyzing the graph to derive new information and conclusions (e.g. Ana-
lyzing the centrality of the nodes to detect how important is a particular
stakeholder in a defined region).
While business agreements and contracts will heavily depend on and benefit
from this layer, it was rather included in the synergistic use cases as such con-
tracts will include agreements on data sharing and access, risk distribution, and
various factors supplemented by the other layers in the framework.
4
https://www.w3.org/2005/Incubator/decision/XGR-decision-20120417/
Sample_Decision_Ontology.html
6 S. Ghazy et al.
3.2 Layer 2: The Data Layer
A main area of concern within emerging transportation technologies is the repre-
sentation of data, syntactically and semantically. The digitization of transporta-
tion data led to the development of multiple standards such as GTFS static5 ,
GTFS realtime6 , GBFS7 , MDS8 , The TOMP-API 9 , NeTEx10 , SIRI11 , DATEX
II12 , OSLO Mobility13 , and more. When a standard is developed, it is common to
define it up to a certain level of granularity, leaving certain issues open [14]. The
knowledge of how a system holistically functions is expected to be understood by
the standardization community rather than reside within the standard itself. An
escalation in interoperability issues, such as incompatibility of protocols, syntax,
and other basic building blocks, occurs when the source of the different standards
stems from different standards’ bodies, each with their own approach to doing
things [14]. The data islands, formed through multi-organizational standardisa-
tion, represents the current situation of data standards in the realm of mobility.
This results in: 1) lack of an overview of the system by its implementer, specifi-
cally for a MaaS provider that is expected to work with multiple standards, 2)
Using standards beyond their original purposes due to the lack of inclusion of
particular use cases or newer scenarios in the specification of a certain standard,
and 3) Each standard defines its own rules and culture for its implementation,
which may significantly vary from those of another standard. For users working
with multiple standards, this might be a tricky situation that leads to mistakes
and confusion in digesting different concepts [14].
Data, in the MaaS ecosystem, is heterogeneous and present in silos. It is not
equipped with the metadata necessary to enable data interoperability. In addi-
tion to multiple standards, there still exists a vast amount of data that does not
even follow a standard [3], compounding the hindrances in the face of actualising
MaaS. For the de-Silo-fication of data and its collective use in decision-making
and routing applications, an explicit definition and formalization of the inherent
MaaS data and their relationships is required. Therefore, the main function of
this layer is to explicitly model MaaS data with its interconnections. Figure 2
illustrates 5 possible categories of data that fall under the Data Layer of Linked
MaaS, noting that these categories are extensible. While some of these categories
must be connected to some stakeholder as its responsible source, some of the data
entities can be completely independent such as weather data, geographical data,
and others.
Some of the use-cases envisioned for the data layer are listed below:
5
https://developers.google.com/transit/gtfs
6
https://developers.google.com/transit/gtfs-realtime
7
https://github.com/NABSA/gbfs/blob/v2.2/gbfs.md
8
https://github.com/openmobilityfoundation/mobility-data-specification
9
https://github.com/TOMP-WG/TOMP-API
10
http://netex-cen.eu/
11
https://www.vdv.de/siri.aspx
12
https://www.datex2.eu/
13
https://data.vlaanderen.be/ns/mobiliteit
The Linked MaaS Framework 7
Fig. 2. Illustration of 6 possible data categories under the Data Layer, including ex-
amples
– Automated integration of data from heterogeneous sources through the align-
ment with existing standards.
– Performing simple and complex queries and algorithms over MaaS data
(e.g. querying for availability of bike lanes, running route-optimization algo-
rithms).
– Reasoning over the graph to derive new information and conclusions (e.g.
Inferring which factors affect the operations of which modes, for example, an
if rule can state that if an increase in a specific factor (e.g traffic) affects a
property of a transport mode (e.g decreases the speed) then this factor should
be taken into account by the routing algorithm ).
3.3 Layer 3: The Infrastructure Layer
There is a distinction between servitizing transportation and servitizing other
industries, of which some have witnessed successful models disrupting preced-
ing distribution models. The implementation of MaaS relies on the existence of
physical infrastructure. Physical transportation infrastructure is a prerequisite
for actualizing MaaS. How can we shift people to more sustainable modes of
transport if we are lacking the infrastructure for it? Promoting bikesharing, for
example, requires bike lanes for a safer commute. Shifting to electric cars requires
charging stations. Major investments in public transport is needed to set up rail-
ways and subways. The significance of infrastructure to MaaS necessitated its
inclusion as the third layer of the Linked MaaS framework. Considering the tech-
nological advancements in transport, the core elements of the infrastructure layer
include both physical and digital infrastructure. Digital infrastructure refers to
the ticketing infrastructure, routing algorithms, MaaS aggregation platforms,
cloud infrastructure, and others. The main function of this layer is to provide a
digital twin of both physical and digital infrastructure as well as the relationships
between them. As this layer connects to the stakeholder layer, it will represent
8 S. Ghazy et al.
all the assets owned by the stakeholders. Independently, the layer can be used
to investigate the following scenarios:
– Through graph theory approaches, various analysis can be performed to
determine System Resilience, identifying the weakest links and bottlenecks
in the infrastructure. This can aid in deriving multimodal disaster mitigation
and recovery plans.
– Analysis of the system to identify missing infrastructure links at different
locations, both physical and digital, which require installation. Through such
analytics, investments can be directed to the right infrastructure sectors and
locations.
– Inter-dependencies between modes and the dynamics of a multi-modal sys-
tem can be derived from the model for usage in smart city mapping and
planning.
After discussing Graph layers 1, 2, and 3, we now look at forming interlinks
between these layers. The Linking layers represent relationships between ele-
ments from different graph layers. These layers are discussed in the subsequent
sections.
3.4 Layer 4: The Ownership Layer
There are relationships between MaaS stakeholders and the Infrastructure layer.
These relations can include which infrastructure belongs to a stakeholder’s assets,
who is responsible for the maintenance or development of specific infrastructure,
etc. These questions mainly depend on the ownership of assets. Therefore, the
main function of this layer is to model the links between the stakeholders and
the infrastructure. A few use-cases envisioned for this linking layer are:
– Analysis of risk. Through the links between the stakeholders and their assets,
an optimum risk distribution model can be determined for a given MaaS
ecosystem.
– Maintenance schedules and work orders can be linked taking into account
the effects of surrounding infrastructure.
– The layer can be used in synergy with other layers for the development of
suitable contracts and agreements based on stakeholders’ assets and liabili-
ties.
3.5 Layer 5: The Auth Layer
A major debate within MaaS falls under the umbrella of data management. Some
of the questions include:
– Who will have the authority over MaaS users data?
– Who is responsible for data maintenance and quality?
– Who is authorized to access a specific type of data?
– How do we authenticate the user/agent accessing the data?
The Linked MaaS Framework 9
– Will MaaS Providers contribute to the monopolization of data or will there
be a decentralised data access?
To resolve this debate, we propose the Auth layer. The main function of this
layer is to model the links between stakeholders and data. The incorporation of
the Auth layer in Linked MaaS can aid in the following use-cases:
– Explicit identification of read-write access rules to different data entities,
perhaps through the Access Control List Ontology.
– Explicit definitions of roles and responsibilities of the stakeholders for data
management.
– Examining the effects of different data access distributions on the stakehold-
ers of the ecosystem (e.g. business opportunities through Open Data).
– Incorporation of data pods following the Solid14 Project for decentralization
of user data.
– Enforcing data policies through validation rules.
3.6 Layer 6: The Connection Layer
Although the Data layer and the Infrastructure layer appear to overlap, there is
a clear distinction between them. The Infrastructure layer models the physical
and digital assets of the ecosystem, whereas a part of the Data layer would
consist of data derived from that infrastructure. For instance, the infrastructure
layer would model railway tracks whereas data from sensors attached to these
tracks are modelled in the data layer. Between these two layers, we propose a
relationship layer, The Connection Layer, where the main function of the
layer is to model the interconnections, policies, and business rules between data
and infrastructure. Example use-cases envisioned for this layer are:
– Can be used to power the development of Internet of Things in transporta-
tion, providing insights on which parts of the infrastructure is connected over
the internet, what types of connections do they have, for instance, having
vehicle location data for a train but lacking sensor data for facility manage-
ment.
– Identifying physical entities from which data is generated can provide in-
sights on connectivity gaps. For example, identifying stations and infrastruc-
ture for which schedules are available can help identify which infrastructure
is lacking connectivity to the routing algorithms.
– Policies on the usage of data for certain infrastructure can be formalized
within this layer.
4 Conclusions and Future Outlook
In this paper, we presented the Linked MaaS framework and discussed 3 Graph
Layers and 3 Linking layers, justifying their function in the ecosystem. In align-
ment with the results of the literature, the layers contain all categories identified
14
https://solidproject.org/
10 S. Ghazy et al.
which were (1) Business, Policy, and Stakeholders, (2) Data and Technology,
and (3) Infrastructure, noting that the inclusion of policy concepts is distributed
throughout the layers rather than constituting a layer of its own. We envision
the development of Linked MaaS to be a catalyst for actualising MaaS. As part
of an ongoing research, we are focusing on works that will contribute to the
Stakeholder layer, the Auth layer, and the Data layer. We invite researchers of
MaaS and the Semantic Web community to bring the Linked MaaS vision to
reality through novel contributions to the 6 layers.
Under the previous subsections of Layers 1 to 6, we provided examples of use-
cases that the individual layers are expected to handle as part of their function.
From a holistic perspective of Linked MaaS, there is an opportunity for syn-
ergistic use-cases which rely on the collective use of the layers. Some examples
are:
– Generation of data-driven contracts, based on the analysis of the stakehold-
ers’ relationships with infrastructure, with data, and with each other.
– A simulation engine can be derived to predict the effects of adding, removing,
or changing any elements in the system (e.g. a merger between two compa-
nies, construction of a new highway, incorporation of a new technology).
– Data Governance, including policies, roles, standards, and metrics, can be
formalized through the Stakeholder layer, the Auth layer, and the Data layer.
Semantic Web technologies were chosen as the core tools powering the Linked
MaaS framework for their extensive capabilities to link data from heterogeneous
sources, develop abstract models that reflect the complexity of the world in the
form of simpler ideas, and reason over encoded knowledge to infer new and
meaningful information. The high-level concepts for all the layers, likely repre-
sented as ontologies or vocabularies, can serve as a foundation upon which a
Linked MaaS knowledge graph is constructed specific to given geographic re-
gions where MaaS operates. This will set a roadmap for MaaS to achieve an
Adaptive level of interoperability - highest level in the Enterprise Interoperabil-
ity Model [5] - where firms are capable of adapting to new environments and
accommodating new partners, making interoperability itself a subject of contin-
uous improvement. The extensibility of Semantic Web technologies will serve as
a future-proof solution for the continued introduction of novelty into the MaaS
ecosystem.
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