Vol-3194/paper69
Wolfgang Fahl
Paper
| Paper | |
|---|---|
 edit
| |
| description | |
| id | Vol-3194/paper69 |
| wikidataid | Q117344922→Q117344922 |
| title | A Federated Cloud Solution for Transnational Mobility Data Sharing |
| pdfUrl | https://ceur-ws.org/Vol-3194/paper69.pdf |
| dblpUrl | https://dblp.org/rec/conf/sebd/CarliniCDL0RT22 |
| volume | Vol-3194→Vol-3194 |
| session | → |
Paper[edit]
| Paper | |
|---|---|
| edit
| |
| description | |
| id | Vol-3194/paper69 |
| wikidataid | Q117344922→Q117344922 |
| title | A Federated Cloud Solution for Transnational Mobility Data Sharing |
| pdfUrl | https://ceur-ws.org/Vol-3194/paper69.pdf |
| dblpUrl | https://dblp.org/rec/conf/sebd/CarliniCDL0RT22 |
| volume | Vol-3194→Vol-3194 |
| session | → |
A Federated Cloud Solution for Transnational Mobility Data Sharing[edit]
A Federated Cloud Solution for Transnational
Mobility Data Sharing
Extended Abstract
Emanuele Carlini1 , Thierry Chevallier2 , Patrizio Dazzi1 , Francesco Lettich1 ,
Raffaele Perego1 , Chiara Renso1 and Salvatore Trani1
1
Institute of Information Science and Technologies (ISTI), National Research Council (CNR), Pisa, Italy
2
AKKA Technologies, Toulouse, France
Abstract
Nowadays, innovative digital services are massively spreading both in the public and private sectors.
In this work we focus on the digital data regarding the mobility of persons and goods, which are
experiencing exponential growth thanks to the significant diffusion of telecommunication infrastructures
and inexpensive GPS-equipped devices. The volume, velocity, and heterogeneity of mobility data call for
advanced and efficient services to collect and integrate various data sources from different data producers.
The MobiDataLab H2020 project aims to deal with these challenges by introducing an efficient and highly
interoperable digital framework for mobility data sharing. In particular, the project aims to propose
to the mobility stakeholders (i.e., transport organising authorities, operators, industry, governments,
and innovators) reproducible methodologies and sustainable tools that can foster the development of a
data-sharing culture in Europe and beyond. This paper introduces the key concepts driving the design
and definition of a cloud-based data-sharing federation we call the Transport Cloud platform, which
represents one of the main pillars of the MobiDataLab project. Such platform aims to ensure transnational
access to mobility data in a secure, efficient, and seamless way, and to ensure that FAIR principles (i.e.,
mobility data should be findable, accessible, interoperable, and reusable) are enforced.
Keywords
Data-sharing, Mobility Data, Cloud Platforms, Federated Platforms
1. Introduction
Over the recent years the European Union devoted many resources and efforts to promoting
data-sharing initiatives, platforms, and policies across its member states. Indeed, in the A
European Strategy for Data vision1 the European commission recognizes how exploiting data
allows the private sector to continuously innovate and create new types of thriving businesses.
At the same time, public entities and institutions can leverage public data to better understand
societal dynamics and make informed decisions. The data sharing vision driving the policies
SEBD 2022: The 30th Italian Symposium on Advanced Database Systems, June 19-22, 2022, Tirrenia (PI), Italy
$ emanuele.carlini@isti.cnr.it (E. Carlini); thierry.chevalier@akka.eu (T. Chevallier); patrizio.dazzi@isti.cnr.it
(P. Dazzi); francesco.lettich@isti.cnr.it (F. Lettich); raffaele.perego@isti.cnr.it (R. Perego); chiara.renso@isti.cnr.it
(C. Renso); salvatore.trani@isti.cnr.it (S. Trani)
� 0000-0003-3643-5404 (E. Carlini); 0000-0001-8504-1503 (P. Dazzi); 0000-0001-6914-2961 (F. Lettich);
0000-0001-7189-4724 (R. Perego); 0000-0002-1763-2966 (C. Renso); 0000-0001-6541-9409 (S. Trani)
© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings CEUR Workshop Proceedings (CEUR-WS.org)
http://ceur-ws.org
ISSN 1613-0073
1
https://ec.europa.eu/info/sites/info/files/communication-european-strategy-data-19feb2020_en.pdf
�of the European Union is that of a “[...] single European data space as a market for data, open
to data from across the world, where personal as well as non-personal data, including sensitive
business data, are secure and businesses also have easy access to an almost infinite amount of
high-quality industrial data, boosting growth and creating value, while minimising the human
carbon and environmental footprint”2 . Accordingly, several European projects and initiatives
have been heavily financed in the recent past – just to name a few, GAIA-X3 [1], SoBigData++4
[2], FENIX5 , SUNFISH6 [3], MOBiNET7 , and the Data for Road Safety Initiative8 .
Let us now focus our attention on mobility data sharing, which is a particular instantiation
of the vision described above and the main topic of our work. In general terms, mobility data
can be defined as data that provides information on mobility patterns. For instance, in the
urban domain it can be encountered in the form of network description, timetable information,
car traffic, public transportation or other mobility modes, parking data, and accessibility data
[4]. It is then clear that a common mobility data space would enable different stakeholders to
share their data into a single, possibly distributed, platform that facilitates access, pooling, and
sharing of data from existing and future transport and mobility databases.
Mobility data sharing – and enabling it appropriately – plays also a critical role in the
decarbonization of the European Union. For instance, the European Green Deal9 and the
European Data Space strategy10 strictly depend on the capacity of organizations to digitalize
and share mobility data, since data-sharing and smart mobility solutions constitute an essential
pillar toward the decarbonization of the European transportation sector. Indeed, over the recent
years many systems enabling connected and automated multi-modal mobility took advantage of
smart mobility solutions leveraging shared data and artificial intelligence. For instance, mobility
data sharing can support several novel applications that improve intermodal connections in
transport hubs – e.g., solutions that search for optimal levels of vehicle availability in car and
bicycle sharing systems. All these considerations align with the goals pursued by the Global
Roadmap of Action toward Sustainable Mobility [5], which states that mobility data-sharing
programs and platforms can help the transition toward greener, safer, more accessible, and more
efficient mobility systems.
Tackling the heterogeneity and peculiarities of mobility data, as well as the various constraints
related to their safe and trusted sharing, is a core objective of the European H2020 MobiDataLab
project 11 . MobiDataLab envisions the usage of an open and federated cloud-based architecture
to easily and practically enforce complex and often contrasting requirements coming from
FAIR (Findability, Accessibility, Interoperability, and Reusability) and privacy principles. Indeed,
a federated cloud can in principle support the sharing of arbitrary resources from arbitrary
application domains, with arbitrary consumer groups across multiple administrative domains
2
https://eur-lex.europa.eu/legal-content/EN/TXT/
3
https://www.data-infrastructure.eu/GAIAX/Navigation/EN/Home/home.html
4
https://plusplus.sobigdata.eu/
5
https://fenix-network.eu/
6
http://www.sunfishproject.eu/
7
https://cordis.europa.eu/project/id/318485
8
https://www.dataforroadsafety.eu/
9
https://ec.europa.eu/info/strategy/priorities-2019-2024/european-green-deal_en
10
https://digital-strategy.ec.europa.eu/en/policies/strategy-data
11
https://mobidatalab.eu/
�[6]. The MobiDataLab project primary objective is to foster data sharing amongst transport
authorities, operators, and other mobility stakeholders operating in Europe, which in most
cases want to maintain their data governance.
In the following we discuss the Transport Cloud platform, the cloud-based data-sharing
federation the MobiDataLab project intends to propose.
2. A cloud-based architecture for FAIR mobility data sharing: the
MobiDataLab Transport Cloud platform
The MobiDataLab Transport Cloud platform aims to facilitate access to mobility data in an
open, interoperable, transnational, and privacy-preserving way. The platform also aims to adopt
the FAIR principles [7] to model said access, i.e., mobility data available in a vast and diverse
ecosystem that possibly encompasses many different sources should be findable, accessible,
interoperable, and reusable.
The vision behind these goals stems from the needs and interests of the stakeholders behind
the MobiDataLab project. Indeed, these are public and private institutions that have an interest
to either act as mobility data providers (for instance, provide real-time public transportation
data, road-network data, vehicle data), or are interested in consuming mobility data through the
access and services provided by the transport cloud platform. Therefore, the platform has been
designed according to federated cloud principles to offer solutions that strive to reduce (and,
whenever possible, eliminate) current technical limitations that act as barriers to mobility data
sharing and reuse.
In the following, we provide more details on the transport cloud platform that is being
developed by the MobiDataLab project. First, we focus on the actors interacting with the
transport cloud platform. Subsequently, we focus on the inner components of the architecture
to detail how the platform enables, facilitates, and promotes mobility data sharing between data
consumers and data producers.
2.1. Actors interacting with the transport cloud platform
Figure 1 introduces a first abstract perspective on the transport cloud platform architecture.
The architecture is composed of a collection of components that perform key operations within
the transport cloud. In the Figure it is also possible to identify four different types of actors that
can interact with the transport cloud, namely:
∙ Administrators are individuals in charge of managing user accounts and the platform
access, deploying applications within the platform, and configuring the components
operating within the platform.
∙ Developers are individuals who deal with the deployment and integration of transport
cloud components.
∙ Data consumers are entities that use the data and services available within the transport
cloud. Relevant examples can be data scientists, researchers, domain experts, transport
� Data Consumer
Data and Service Access
MobiDataLab Transport Cloud Platform
Data Privacy & Anonymization
Configuration and Management
Data and Service Provision
Data/Service Harmonization & Standardization
Administrator Data Processing Data and Service
Providers
Data Fusion and Enrichment
Data and Component Integration
Developer
Figure 1: The MobiDataLab transport cloud platform architecture – simplified view.
customers, or external services that use the data and services offered by the transport
cloud platform.
∙ Data providers are entities that provide, either passively or actively, data or services to the
Transport Cloud. Some examples that were deemed relevant to the MobiDataLab project
are Trip planners (e.g., Navitia12 , HERE13 ), MobiDataLab stakeholders (e.g., transport
operators or public institutions that actively share their data and services for the good of
the MobiDataLab project), and open data/services providers (e.g., OpenStreetMap14 ).
2.2. Information flow and key components within the transport cloud
The architecture of the transport cloud platform has been primarily designed to promote mobility
data sharing between data consumers and data providers. Indeed, the components operating
within the platform have the role of powering, sustaining, and adding value to the information
flowing between these two types of actors. In the following, we focus on how the data consumers
12
https://navitia.io/
13
https://www.here.com/
14
https://www.openstreetmap.org/
� MobiDataLab Architecture
Actors Transport Cloud Computational Resources
Processors
5
1 Data and Service Access Components 6
3
Virtual Instance
Privacy and Anonymization
Data Consumer
Metadata Catalogue Service Catalogue Identity Manager
Virtual Instance
Data Fusion
Channels Storage Resources
2
API Components 7
4
API
Data Enrichment
Distributed File System
Data API Service API
Web Interface
Database
Other Processors
Generic Endpoint
Third Party Providers
8
Data Provider Service Provider
Figure 2: The Mobidatalab transport cloud platform architecture – detailed view.
and producers interface and interact with the transport cloud platform, and elaborate on the
functionalities that key components provide to sustain the underlying information flow. To this
end, we introduce in Figure 2 a more detailed overview of the platform architecture.
On the one side of the transport cloud we have the Data consumers (box 1 in Figure 2), which
can interact with the platform through the several transport cloud channels (box 2 in Figure 2,
Data and Service Access box in Figure 1). Said channels are (1) API endpoints (mainly dedicated to
REST API services), (2) web interface endpoints, i.e., endpoints dedicated to services which need
interaction with the end user (for example scenarios involving data analysis and visualisation
tasks), and (3) generic endpoints – for instance, a SPARQL endpoint may enable a data consumer
to access some knowledge base via Resource Description Framework (RDF) queries.
Whichever the channel of choice, data consumers first interact with the transport cloud by
authenticating themselves via the identity manager (box 3 Figure 2, Data and Service Access
box in Figure 1). Once authenticated, data consumers proceed to submit their requests to the
transport cloud platform via the API Components (box 4 in Figure 2), which in turn process the
requests by querying the metadata and service catalogues (box 3 Figure 2, Data and Service
Access box in Figure 1) to find out the appropriate data sources and services (either internal or
�external to the transport cloud) the platform must use to satisfy the requests. It is worth noting
that the technologies currently under examination for the implementation of said catalogues
are being conducted according to the FAIR principles.
On the other end of the transport cloud we have the third-party providers, i.e., the data
providers previously mentioned (box 8 in Figure 2). The third-party providers always represent
the information entry point of the platform as they are responsible for the provision of information
in the form of datasets and services. The access mechanisms to these information sources are
identified and implemented according to the operations to be performed and the types of data
and services that need to be accessed. More precisely, information retrieved from third-party
providers can either be imported within the Transport Cloud, thus requiring appropriate storage
solutions (box 7 in Figure 2)15 , or accessed on the fly through the use of specific data and service
endpoints exposed by the providers. We report that the transport cloud will handle the latter
type of access using the data and service APIs components (box 4 Figure 2), which may employ
appropriate caching mechanisms to improve access efficiency.
Now that it is clear how data consumers and third-party providers (i.e., data producers) interact
with the transport cloud, we focus on a key component that enables to process information
flowing within the platform, i.e., the processor (box 5 in Figure 2, which encompasses the Data
Privacy & Anonymization, Data/Service Harmonization & Standardization, Data Processing, and
Data Fusion and Enrichment boxes in Figure 1). In generic terms, we define a processor as a
component that models some function that inputs some data and produces an output according
to a well-defined logic. This definition then allows to instantiate the notion of processor in
many different ways, thus allowing the transport cloud to provide via multiple processors a
potentially unlimited number of operations and services. For instance, in the context of mobility
data sharing a processor may be used to perform semantic enrichment based on common
vocabularies, geographical enrichment based on common geometries, data format translation
when some data format must be reconducted to another one, data fusion when multiple datasets
must be combined, data anonymisation to increase trust in the platform via privacy-preserving
techniques, injection of license specification, and in general any data processing task that is
relevant to the goals of the project. In general, it is clear how processors add further value to
the transport cloud platform, as they give the ability to create novel data and services. Finally,
we report that processors will lean on computational resources internal to the transport cloud
platform (box 6 Figure 2).
3. Conclusion and future work
This paper introduces the concepts at the basis of the Transport Cloud platform, which is the
cloud and data-sharing federation proposed by the MobiDataLab Project. The project kicked-
off in February 2021 and aims to promote the transport data sharing culture in Europe. The
MobiDataLab Transport Cloud platform is therefore intended to be an open and inter-operable
platform that eases the access and integration of distributed and heterogeneous mobility data
owned by distinct organizations. The platform will be available to public and private institutions
15
Storage solutions that the platform will include are relational databases, spatial databases, and knowledge
graph databases.
�that have an interest to either act as mobility data providers or consume mobility data through
the access and services provided by the transport cloud platform. The platform has been
designed to be compliant with the FAIR principles. In the near future we plan to further advance
the definition and the design of the Transport Cloud architecture, which will be then evaluated
according to the needs of the MobiDataLab stakeholders.
Acknowledgments
This work has been partially supported by the European Union’s Horizon 2020 Research
and Innovation program under the projects ACCORDION (Grant agreement ID: 871793) and
MOBIDATALAB (Grant agreement ID: 101006879).
References
[1] A. Braud, G. Fromentoux, B. Radier, O. Le Grand, The road to european digital sovereignty
with gaia-x and idsa, IEEE Network 35 (2021) 4–5. doi:10.1109/MNET.2021.9387709.
[2] V. Grossi, B. Rapisarda, F. Giannotti, D. Pedreschi, Data science at sobigdata: the european
research infrastructure for social mining and big data analytics, International Journal of
Data Science and Analytics 6 (2018) 205–216.
[3] F. P. Schiavo, V. Sassone, L. Nicoletti, A. Margheri, FaaS: Federation-as-a-Service, arXiv
e-prints (2016) arXiv:1612.03937. arXiv:1612.03937.
[4] E. Carlini, P. Dazzi, F. Lettich, R. Perego, C. Renso, Cloud and data federation in mo-
bidatalab, in: M. Cafaro, L. Ferrucci, H. Kavalionak, A. Makris (Eds.), FRAME@HPDC
2021: Proceedings of the 1st Workshop on Flexible Resource and Application Manage-
ment on the Edge, Virtual Event, Sweden, 25 June, 2021, ACM, 2021, pp. 39–40. URL:
https://doi.org/10.1145/3452369.3463819. doi:10.1145/3452369.3463819.
[5] S. M. for All (SuM4AllTM) initiative, Sustainable mobility: Policy making for data sharing,
2021. URL: https://www.wbcsd.org/Programs/Cities-and-Mobility/Transforming-Mobility/
Digitalization-and-Data-in-Urban-Mobility/Policy-to-Enable-Data-Sharing/Resources/
Sustainable-mobility-Policy-making-for-data-sharing.
[6] R. B. Bohn, C. A. Lee, M. Michel, The NIST cloud federation reference architecture, 2020.
NIST Special Publication https://doi.org/10.6028/NIST.SP.500-332.
[7] M. D. Wilkinson, M. Dumontier, I. J. Aalbersberg, G. Appleton, M. Axton, A. Baak,
N. Blomberg, J.-W. Boiten, L. B. da Silva Santos, P. E. Bourne, et al., The fair guiding
principles for scientific data management and stewardship, Scientific data 3 (2016) 1–9.
�
A Federated Cloud Solution for Transnational Mobility Data Sharing[edit]
A Federated Cloud Solution for Transnational
Mobility Data Sharing
Extended Abstract
Emanuele Carlini1 , Thierry Chevallier2 , Patrizio Dazzi1 , Francesco Lettich1 ,
Raffaele Perego1 , Chiara Renso1 and Salvatore Trani1
1
Institute of Information Science and Technologies (ISTI), National Research Council (CNR), Pisa, Italy
2
AKKA Technologies, Toulouse, France
Abstract
Nowadays, innovative digital services are massively spreading both in the public and private sectors.
In this work we focus on the digital data regarding the mobility of persons and goods, which are
experiencing exponential growth thanks to the significant diffusion of telecommunication infrastructures
and inexpensive GPS-equipped devices. The volume, velocity, and heterogeneity of mobility data call for
advanced and efficient services to collect and integrate various data sources from different data producers.
The MobiDataLab H2020 project aims to deal with these challenges by introducing an efficient and highly
interoperable digital framework for mobility data sharing. In particular, the project aims to propose
to the mobility stakeholders (i.e., transport organising authorities, operators, industry, governments,
and innovators) reproducible methodologies and sustainable tools that can foster the development of a
data-sharing culture in Europe and beyond. This paper introduces the key concepts driving the design
and definition of a cloud-based data-sharing federation we call the Transport Cloud platform, which
represents one of the main pillars of the MobiDataLab project. Such platform aims to ensure transnational
access to mobility data in a secure, efficient, and seamless way, and to ensure that FAIR principles (i.e.,
mobility data should be findable, accessible, interoperable, and reusable) are enforced.
Keywords
Data-sharing, Mobility Data, Cloud Platforms, Federated Platforms
1. Introduction
Over the recent years the European Union devoted many resources and efforts to promoting
data-sharing initiatives, platforms, and policies across its member states. Indeed, in the A
European Strategy for Data vision1 the European commission recognizes how exploiting data
allows the private sector to continuously innovate and create new types of thriving businesses.
At the same time, public entities and institutions can leverage public data to better understand
societal dynamics and make informed decisions. The data sharing vision driving the policies
SEBD 2022: The 30th Italian Symposium on Advanced Database Systems, June 19-22, 2022, Tirrenia (PI), Italy
$ emanuele.carlini@isti.cnr.it (E. Carlini); thierry.chevalier@akka.eu (T. Chevallier); patrizio.dazzi@isti.cnr.it
(P. Dazzi); francesco.lettich@isti.cnr.it (F. Lettich); raffaele.perego@isti.cnr.it (R. Perego); chiara.renso@isti.cnr.it
(C. Renso); salvatore.trani@isti.cnr.it (S. Trani)
� 0000-0003-3643-5404 (E. Carlini); 0000-0001-8504-1503 (P. Dazzi); 0000-0001-6914-2961 (F. Lettich);
0000-0001-7189-4724 (R. Perego); 0000-0002-1763-2966 (C. Renso); 0000-0001-6541-9409 (S. Trani)
© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings CEUR Workshop Proceedings (CEUR-WS.org)
http://ceur-ws.org
ISSN 1613-0073
1
https://ec.europa.eu/info/sites/info/files/communication-european-strategy-data-19feb2020_en.pdf
�of the European Union is that of a “[...] single European data space as a market for data, open
to data from across the world, where personal as well as non-personal data, including sensitive
business data, are secure and businesses also have easy access to an almost infinite amount of
high-quality industrial data, boosting growth and creating value, while minimising the human
carbon and environmental footprint”2 . Accordingly, several European projects and initiatives
have been heavily financed in the recent past – just to name a few, GAIA-X3 [1], SoBigData++4
[2], FENIX5 , SUNFISH6 [3], MOBiNET7 , and the Data for Road Safety Initiative8 .
Let us now focus our attention on mobility data sharing, which is a particular instantiation
of the vision described above and the main topic of our work. In general terms, mobility data
can be defined as data that provides information on mobility patterns. For instance, in the
urban domain it can be encountered in the form of network description, timetable information,
car traffic, public transportation or other mobility modes, parking data, and accessibility data
[4]. It is then clear that a common mobility data space would enable different stakeholders to
share their data into a single, possibly distributed, platform that facilitates access, pooling, and
sharing of data from existing and future transport and mobility databases.
Mobility data sharing – and enabling it appropriately – plays also a critical role in the
decarbonization of the European Union. For instance, the European Green Deal9 and the
European Data Space strategy10 strictly depend on the capacity of organizations to digitalize
and share mobility data, since data-sharing and smart mobility solutions constitute an essential
pillar toward the decarbonization of the European transportation sector. Indeed, over the recent
years many systems enabling connected and automated multi-modal mobility took advantage of
smart mobility solutions leveraging shared data and artificial intelligence. For instance, mobility
data sharing can support several novel applications that improve intermodal connections in
transport hubs – e.g., solutions that search for optimal levels of vehicle availability in car and
bicycle sharing systems. All these considerations align with the goals pursued by the Global
Roadmap of Action toward Sustainable Mobility [5], which states that mobility data-sharing
programs and platforms can help the transition toward greener, safer, more accessible, and more
efficient mobility systems.
Tackling the heterogeneity and peculiarities of mobility data, as well as the various constraints
related to their safe and trusted sharing, is a core objective of the European H2020 MobiDataLab
project 11 . MobiDataLab envisions the usage of an open and federated cloud-based architecture
to easily and practically enforce complex and often contrasting requirements coming from
FAIR (Findability, Accessibility, Interoperability, and Reusability) and privacy principles. Indeed,
a federated cloud can in principle support the sharing of arbitrary resources from arbitrary
application domains, with arbitrary consumer groups across multiple administrative domains
2
https://eur-lex.europa.eu/legal-content/EN/TXT/
3
https://www.data-infrastructure.eu/GAIAX/Navigation/EN/Home/home.html
4
https://plusplus.sobigdata.eu/
5
https://fenix-network.eu/
6
http://www.sunfishproject.eu/
7
https://cordis.europa.eu/project/id/318485
8
https://www.dataforroadsafety.eu/
9
https://ec.europa.eu/info/strategy/priorities-2019-2024/european-green-deal_en
10
https://digital-strategy.ec.europa.eu/en/policies/strategy-data
11
https://mobidatalab.eu/
�[6]. The MobiDataLab project primary objective is to foster data sharing amongst transport
authorities, operators, and other mobility stakeholders operating in Europe, which in most
cases want to maintain their data governance.
In the following we discuss the Transport Cloud platform, the cloud-based data-sharing
federation the MobiDataLab project intends to propose.
2. A cloud-based architecture for FAIR mobility data sharing: the
MobiDataLab Transport Cloud platform
The MobiDataLab Transport Cloud platform aims to facilitate access to mobility data in an
open, interoperable, transnational, and privacy-preserving way. The platform also aims to adopt
the FAIR principles [7] to model said access, i.e., mobility data available in a vast and diverse
ecosystem that possibly encompasses many different sources should be findable, accessible,
interoperable, and reusable.
The vision behind these goals stems from the needs and interests of the stakeholders behind
the MobiDataLab project. Indeed, these are public and private institutions that have an interest
to either act as mobility data providers (for instance, provide real-time public transportation
data, road-network data, vehicle data), or are interested in consuming mobility data through the
access and services provided by the transport cloud platform. Therefore, the platform has been
designed according to federated cloud principles to offer solutions that strive to reduce (and,
whenever possible, eliminate) current technical limitations that act as barriers to mobility data
sharing and reuse.
In the following, we provide more details on the transport cloud platform that is being
developed by the MobiDataLab project. First, we focus on the actors interacting with the
transport cloud platform. Subsequently, we focus on the inner components of the architecture
to detail how the platform enables, facilitates, and promotes mobility data sharing between data
consumers and data producers.
2.1. Actors interacting with the transport cloud platform
Figure 1 introduces a first abstract perspective on the transport cloud platform architecture.
The architecture is composed of a collection of components that perform key operations within
the transport cloud. In the Figure it is also possible to identify four different types of actors that
can interact with the transport cloud, namely:
∙ Administrators are individuals in charge of managing user accounts and the platform
access, deploying applications within the platform, and configuring the components
operating within the platform.
∙ Developers are individuals who deal with the deployment and integration of transport
cloud components.
∙ Data consumers are entities that use the data and services available within the transport
cloud. Relevant examples can be data scientists, researchers, domain experts, transport
� Data Consumer
Data and Service Access
MobiDataLab Transport Cloud Platform
Data Privacy & Anonymization
Configuration and Management
Data and Service Provision
Data/Service Harmonization & Standardization
Administrator Data Processing Data and Service
Providers
Data Fusion and Enrichment
Data and Component Integration
Developer
Figure 1: The MobiDataLab transport cloud platform architecture – simplified view.
customers, or external services that use the data and services offered by the transport
cloud platform.
∙ Data providers are entities that provide, either passively or actively, data or services to the
Transport Cloud. Some examples that were deemed relevant to the MobiDataLab project
are Trip planners (e.g., Navitia12 , HERE13 ), MobiDataLab stakeholders (e.g., transport
operators or public institutions that actively share their data and services for the good of
the MobiDataLab project), and open data/services providers (e.g., OpenStreetMap14 ).
2.2. Information flow and key components within the transport cloud
The architecture of the transport cloud platform has been primarily designed to promote mobility
data sharing between data consumers and data providers. Indeed, the components operating
within the platform have the role of powering, sustaining, and adding value to the information
flowing between these two types of actors. In the following, we focus on how the data consumers
12
https://navitia.io/
13
https://www.here.com/
14
https://www.openstreetmap.org/
� MobiDataLab Architecture
Actors Transport Cloud Computational Resources
Processors
5
1 Data and Service Access Components 6
3
Virtual Instance
Privacy and Anonymization
Data Consumer
Metadata Catalogue Service Catalogue Identity Manager
Virtual Instance
Data Fusion
Channels Storage Resources
2
API Components 7
4
API
Data Enrichment
Distributed File System
Data API Service API
Web Interface
Database
Other Processors
Generic Endpoint
Third Party Providers
8
Data Provider Service Provider
Figure 2: The Mobidatalab transport cloud platform architecture – detailed view.
and producers interface and interact with the transport cloud platform, and elaborate on the
functionalities that key components provide to sustain the underlying information flow. To this
end, we introduce in Figure 2 a more detailed overview of the platform architecture.
On the one side of the transport cloud we have the Data consumers (box 1 in Figure 2), which
can interact with the platform through the several transport cloud channels (box 2 in Figure 2,
Data and Service Access box in Figure 1). Said channels are (1) API endpoints (mainly dedicated to
REST API services), (2) web interface endpoints, i.e., endpoints dedicated to services which need
interaction with the end user (for example scenarios involving data analysis and visualisation
tasks), and (3) generic endpoints – for instance, a SPARQL endpoint may enable a data consumer
to access some knowledge base via Resource Description Framework (RDF) queries.
Whichever the channel of choice, data consumers first interact with the transport cloud by
authenticating themselves via the identity manager (box 3 Figure 2, Data and Service Access
box in Figure 1). Once authenticated, data consumers proceed to submit their requests to the
transport cloud platform via the API Components (box 4 in Figure 2), which in turn process the
requests by querying the metadata and service catalogues (box 3 Figure 2, Data and Service
Access box in Figure 1) to find out the appropriate data sources and services (either internal or
�external to the transport cloud) the platform must use to satisfy the requests. It is worth noting
that the technologies currently under examination for the implementation of said catalogues
are being conducted according to the FAIR principles.
On the other end of the transport cloud we have the third-party providers, i.e., the data
providers previously mentioned (box 8 in Figure 2). The third-party providers always represent
the information entry point of the platform as they are responsible for the provision of information
in the form of datasets and services. The access mechanisms to these information sources are
identified and implemented according to the operations to be performed and the types of data
and services that need to be accessed. More precisely, information retrieved from third-party
providers can either be imported within the Transport Cloud, thus requiring appropriate storage
solutions (box 7 in Figure 2)15 , or accessed on the fly through the use of specific data and service
endpoints exposed by the providers. We report that the transport cloud will handle the latter
type of access using the data and service APIs components (box 4 Figure 2), which may employ
appropriate caching mechanisms to improve access efficiency.
Now that it is clear how data consumers and third-party providers (i.e., data producers) interact
with the transport cloud, we focus on a key component that enables to process information
flowing within the platform, i.e., the processor (box 5 in Figure 2, which encompasses the Data
Privacy & Anonymization, Data/Service Harmonization & Standardization, Data Processing, and
Data Fusion and Enrichment boxes in Figure 1). In generic terms, we define a processor as a
component that models some function that inputs some data and produces an output according
to a well-defined logic. This definition then allows to instantiate the notion of processor in
many different ways, thus allowing the transport cloud to provide via multiple processors a
potentially unlimited number of operations and services. For instance, in the context of mobility
data sharing a processor may be used to perform semantic enrichment based on common
vocabularies, geographical enrichment based on common geometries, data format translation
when some data format must be reconducted to another one, data fusion when multiple datasets
must be combined, data anonymisation to increase trust in the platform via privacy-preserving
techniques, injection of license specification, and in general any data processing task that is
relevant to the goals of the project. In general, it is clear how processors add further value to
the transport cloud platform, as they give the ability to create novel data and services. Finally,
we report that processors will lean on computational resources internal to the transport cloud
platform (box 6 Figure 2).
3. Conclusion and future work
This paper introduces the concepts at the basis of the Transport Cloud platform, which is the
cloud and data-sharing federation proposed by the MobiDataLab Project. The project kicked-
off in February 2021 and aims to promote the transport data sharing culture in Europe. The
MobiDataLab Transport Cloud platform is therefore intended to be an open and inter-operable
platform that eases the access and integration of distributed and heterogeneous mobility data
owned by distinct organizations. The platform will be available to public and private institutions
15
Storage solutions that the platform will include are relational databases, spatial databases, and knowledge
graph databases.
�that have an interest to either act as mobility data providers or consume mobility data through
the access and services provided by the transport cloud platform. The platform has been
designed to be compliant with the FAIR principles. In the near future we plan to further advance
the definition and the design of the Transport Cloud architecture, which will be then evaluated
according to the needs of the MobiDataLab stakeholders.
Acknowledgments
This work has been partially supported by the European Union’s Horizon 2020 Research
and Innovation program under the projects ACCORDION (Grant agreement ID: 871793) and
MOBIDATALAB (Grant agreement ID: 101006879).
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