Policy Briefs
NOESIS Policy Briefs - Page 2
- Table of Contents -
NOESIS project ........................................................................................................................... 3
Big Data in Transport / Overview............................................................................................... 4
Main obstacles identified........................................................................................................... 6
Policy recommendations ......................................................................................................... 12
Skilled personnel within transport authorities and companies ........................................... 12
Promoting cooperation among stakeholders ...................................................................... 13
Transferability of Big Data know-how ............................................................................... 14
Improve the quality of data.................................................................................................. 15
Opening data to boost the economy and research activities .............................................. 16
Ensuring data protection compliance and supporting the implementation of the GDPR... 17
Understand the benefits of big data solutions to better incorporate them in transport ... 18
Encouragement of feasible business models and access to financing ................................ 19
References ............................................................................................................................... 20
NOESIS Policy Briefs - Page 3
NOESIS project
The NOESIS project has been a two-year Horizon 2020 coordination and support action, from
November 2017 until October 2019 and is part of the ‘Smart Green and Integrated Transport’
Programme. The consortium consisted of eight (8) partners from six (6) EU Member States
and comprised of experts from ITS, transport management and operation, decision making
and data management fields.
The NOESIS project identified critical factors/features which lead to successful
implementation of Big Data technologies and services in the field of transport and logistics
with significant value generation from a socioeconomic viewpoint. The impact of Big Data has
been evaluated by analysing a series of transportation use cases. The project has been
developing the Big Data in Transport Library (BDTL), the first collection of Big Data use cases
in transport. Based on this library, the project developed a Decision Support tool that is able
to predict the socioeconomic value generated from Big Data investments, taking as input the
specific characteristics and contextual information of the transport system under evaluation
and associating it with a predefined set of use cases with similar characteristics by employing
Big Data (machine learning) techniques.
The goal of the NOESIS policy briefs document is to share insights derived from our experience
working with more that 100 Big Data transportation use cases. The policy briefs focus on
suggesting pathways and good practices to policy makers, transport stakeholders and
businesses about the successful implementation of Big Data in transport projects on the EU,
national and regional level.
NOESIS Policy Briefs - Page 4
Big Data in Transport / Overview
Data size grows tremendously every day, but not all transport organisations use it to derive
insights and make decisions. Big Data has been the next big technology phenomenon for a
long time, but organisations are still evaluating ways to successfully leverage Big Data and
analytics to:
create a better customer experience providing more efficient, effective and quality
services and modify user behaviour,
achieve organisation objectives (e.g. optimize operational efficiency, avoid failures,
financial performance) and
gain a competitive advantage (innovate).
Things are changing quite fast in the transportation sector. Powered by Big Data, Artificial
Intelligence (AI) and the Internet of Things (IoT), the transportation system can become safer,
more efficient and smarter.
Though there is a growing number of Big Data applications and use cases in the transportation
sector, we can argue that there are still many things that need to be done in order to realise
the full potential of Big Data in this sector.
On the one hand side, both government and public transport bodies begin to offer support
by creating and providing access to public datasets. On the other side, data collection is being
further supported by an entire ecosystem of private data providers and software developers
supporting the public sector capacity. Data collection is further boosted by the rapid
penetration of connected devices into everyday life providing e.g. location data at a scale
never seen before, IoT devices. Smart phones, telematics, sensors, credit card transactions
etc. offer real-time and highly detailed data about peoples transportation demands. Big Data
technologies offer the ability for the production of new analytics introducing new
opportunities to plan and build a sustainable transportation ecosystem that will fulfil user
Data is vital to how we plan, invest, and evaluate transportation networks. However, there
are important obstacles at all levels that need to be taken into account regarding data
collection and use in the EU. Before presenting these obstacles, it is important to mention
some important observations we made when working with Big Data in transport use cases:
Data collector / provider is changing: Though historically the public sector had a leading role
and was investing resources to collect and analyse transportation related data, we now see
that the private sector is becoming the leading source of Big Data that can be later utilised by
public authorities and transport organisations. As an example, Google now knows more about
NOESIS Policy Briefs - Page 5
where people move on a daily basis than every government in the EU or in the world. In most
cases we observe that authorities responsible for building and maintaining transportation
networks have less data then Google has.
Types of data are evolving and changing over time: Data collected until now were e.g. mainly
data concerning travel patterns of a sample set of commuters, data from traffic counters that
were collected and analysed regularly etc. Now, mobile phones, sensors, GPS trackers, and
other navigation devices offer real-time demand-side data. This is real-time Big Data at a level
of detail that simply did not exist before. The Internet of Things is predicted to contain 50
billion devices in 2020, rapidly expanding the volume, velocity and variety of data related to
transport and mobility. This data is expected to become a new form of oil for transport
systems. While earlier datasets focused more on vehicular transportation, emerging data
sources can offer comparable information regarding bicycle, pedestrian, and mass transit
travel - plus multimodal trips.
Transport authorities in most cases do not use private data sources: this is a missed
opportunity and it also most of the time characterizes the public sectors refusal to develop
innovative geospatial data or to establish synergies with the private sector to acquire such
Returns on data investments might take time: Innovation in transport requires time, and
commitment. Using Big Data to create value does not mean that automatically Big Data
investments can lead to quick and easy results. Instead, introducing new technologies into
the transport planning process takes time, and strong leadership and adequate resources are
In order to better understand the policy actions needed in the Big Data in transport sector,
NOESIS identified some gaps/obstacles that transport organisations face when developing Big
Data solutions. Based on these obstacles, NOESIS consortium suggest related Policies that
should be taken into account by Policy Makers and Transport experts.
NOESIS Policy Briefs - Page 6
Main obstacles identified
Lack of skilled technical personnel to work with data for Transport Authorities
There are important technical challenges that transport authorities should take into
account when deciding to work with Big Data and make datasets available and/or develop
insights from these datasets. Often in-house personnel lack the data skills to create
appropriate and reliable data feeds (Wani and Jabin, 2018) and to conduct analytics that
will help transport authorities reach their goals. In most cases transport authorities do not
have the financial capacity to hire data scientists; salaries offered in the private sector for
them are much higher and they have plenty of opportunities to work in a more start-up
business environment. Leveraging data analytics for transport planning requires specific
skills and knowledge that might not be available in the local job market. Furthermore, at
the managerial level, there is a lack of training to know the potential Big Data analytics for
the transportation sector. This lack of training limits the agencys ability to obtain data in
a usable format, analyse it, and effectively deploy it at the decision-making stage.
Lack of data collaboration and sharing among institutions
Partnerships and synergies are needed among public and private data providers and other
transport organisations. The large number of emerging datasets reveal the need to
establish synergies in order to share costs and to be able to establish a wider and
synergetic Big Data strategy at regional or national level. For instance, the lack of sharing
transport data is a big headache for potential MaaS providers who want to provide a
better travel offer.
At the same time, many transport organisations lack the technical capacity to experiment
with cutting-edge technologies. To overcome these hurdles, partnerships with
development agencies and specialized tech firms are essential.
Lack of understanding between transportation experts and data scientists
Transportation experts should play a key role to help interpret the outcomes of Big Data
analytics, but they often lack machine learning and statistical training to benefit from data.
The Big Data tools that exist are generic and designed for data scientists to model big data
with different characteristics and to apply to different domains to serve their own
purposes. These tools may not maximise their utilities without domain expert’s
NOESIS Policy Briefs - Page 7
Lack of access to different data sources and data variability
A large number of NOESIS Big Data in Transport Library (BDTL) use cases rely solely on
data that transport organisations collected themselves. This contradicts to the purpose of
using large quantities of data from several sources in order to gain a better understanding
of what is happening in the area of interest. The reason for this is partially because of
technical limitations, such as not having access to the sources that could supply the data
needed, but also because of policy decision where the collectors of data do not want to
share their data or are not aware of how they could share their data. Furthermore,
disperse data generated from different sources with disparity speeds and resolutions tend
to be a challenge for data integration to serve business objectives. They may be not useful
at all, if they are too diverse.
Lack of standards in databases
There is a lack of standards regarding data formats and structures for many types of data
relevant to transport (Kumar and Prakash, 2014). For example, in many open data portals,
raw data feeds are presented as CSV, XLS and XML files, as well as PDF documents and a
range of other forms of data. That makes the process of finding data time-consuming, and
in most cases difficult to identify the appropriate datasets. One of the barriers to
multimodal journey planning is the lack of standardized data formats for all transportation
modes. Lack of standardized data formats also means that transportation planners have
a less complete picture of the multimodal transportation network and traveller behaviour.
Lack of data quality
Private users and organisations make their datasets publicly available, but the quality of
these data can be put into question, as they may not be consistent, complete,
understandable, or trustworthy. For instance, data that is old or redundant might lead to
conclusions and realisations that might be no longer relevant. Therefore, it is important
to keep quality of datasets. Furthermore, Big Data consists of oversized data, frequently
unusable without pre-processing. Only a small portion of this data is relevant for
stakeholders to use. Therefore, cleaning the data and removing noise is important.
Lack of infrastructure for storage
Data needs to be represented in a valid format and stored in a repository in a consistent
way, so that it can be searched, retrieved and manipulated consistently and efficiently.
NOESIS Policy Briefs - Page 8
The secure storage, management and analysis of Big Data requires investment both in
hardware and software. A data infrastructure such as servers or cloud storage and
software services are needed. However, many transport organisations still use legacy
systems and are risk averse to testing new products.
Cost of purchasing the data
Nowadays, when transport organizations intend to implement Big Data techniques within
their activities, they find that data has to be purchased. Some big data sources are
currently expensive (e.g. CDR data), others are provided free through APIs (e.g. social
media) while not exceeding a certain limit of data downloaded. At this point, the
development of big data solutions run the risk that in the future data providers would
make the access to free data more restricted or charge large amounts of money to acquire
information, thereby making big data analysis costly and, for some purposes, even not
Potential of open data not fully realized
There exist a wide range of transportation related datasets available, ranging from closed
to open. Until now, public or private transport organisations have not yet recognized the
full potential of opening up their data (Soriano et al. 2016). Though transport
organisations already collect a significant amount of data (timetables, status of the
service, etc.), in most cases they do not publish this data as open mainly because: a) data
is not open (there are restrictions for releasing data as open), b) they do not wish to share
data at all, c) they do not have the resources to release data. Despite the fact that the
potential benefit for all the society (including the government and the transport company)
is high, many of these companies are reluctant to open up the data they collect (EU, 2016).
Uncertainty about data ownership regulation
Currently there are no legal regulations for "data ownership", but EU could be considering
the introduction of a property right over data in its efforts to boost the European data
economy (Stepanov, 2019). When we speak of data ownership, we do not mean the right
to use the data (e.g. through a license), but an absolute right similar to tangible property.
Unlike physical objects, data is characterized by non-rivalry, non-exclusivity, and non-
wear and tear. This means that a large number of users can use data without affecting
the use of the other. In addition, data can be copied without particular financial expense
and is not subjected to wear and tear or aging. All this is different for physical objects and
explains the existence of an absolute property right for physical objects (Hoeren, 2019).
NOESIS Policy Briefs - Page 9
In addition, it could further exacerbate existing power disparities and dependencies. As
an example, data from connected cars could be mentioned here, where the dependency
on the car manufacturer could become even greater through the introduction of data
This obstacle is also related to open data, knowing that many organisations do not share
their data. We have seen that many transport authorities contract external organisations
to manage their data infrastructures and these contracts do not have data ownership
provisions. Contractors can be reluctant to share or exchange data with their partners
(Kennedy and Moss, 2015) mainly due to competition (Jagadish et al., 2014). There are
some cases were organisations who own the data do share it with researchers or agencies
under a non-disclosure agreement. Many organisations are reluctant to make data
available or to identify new business models for providing their data.
Uncertainty on data privacy and protection law
Data protection law was comprehensively reformed with the General Data Protection
Regulation (GDPR)
. GDPR is directly applicable, as it is an EU-Regulation and not an EU-
Directive. Although the Member States also have their own data protection laws, these
aim to harmonise the laws of the Member States with the GDPR or to use opening clauses
provided within the GDPR for individual legal areas (e.g. data processing in the context of
employment). There are no such opening clauses for Big Data applications. For such
applications, the GDPR is therefore primarily important.
The GDPR was intended to harmonise data protection law and follows a one size fits it
all- approach in two respects: Firstly, it does not differentiate according to the type of
organisation processing the data. Second, there is no differentiation according to the
amount of data being processed. The GDPR thus applies to the small handicraft enterprise
that processes customer data, as well as to large social networks. The same rules apply
for a small spreadsheet with personal data as for large Big Data applications. This,
undoubtedly in good intention pursued, one size fits it all-principle, represents also the
biggest problem of the GDPR. Since the GDPR must capture so many different situations,
it is very broadly formulated. That leads to many unclear regulations that, depending upon
point of view, can be interpreted differently. Thereby a big legal insecurity exists, that
might be settled only in some years, when courts and supervisory authorities have fine-
tuned the GDPR. This legal uncertainty is causing many companies to adopt a rigorous
approach. Before they expose themselves to the risk of a fine, they take the safe option
protection of natural persons with regard to the processing of personal data and on the free movement of such
data, and repealing Directive 95/46/EC (General Data Protection Regulation).
NOESIS Policy Briefs - Page 10
and prefer to forego the development and introduction of innovative Big Data
There is a concern by transport authorities that creating data and making data available
(even in anonymised form) may violate the GDPR. Big Data projects capture large amounts
of personal or commercially sensitive data. Organisations responsible for collecting,
storing and analysing Big Transport Data should make sure that data is available only in
anonymised and aggregated form and prevent its misuse.
Uncertainty in the benefits provided by the investment in Big Data
Big Data in transport is a new area and there are not enough insights on how an
organization can move from traditional approaches to new Big Data solutions. It is also a
complicated area that it makes it difficult to predict from the beginning the economic
benefits and the social impact of Big Data investments. This uncertainty can become an
obstacle taking into account that in most cases the return on Big Data investments might
take time.
Lack of financing or business models
In order to face the new challenges and meet the requirements in the new information
era, digitization and adoption of state-of-the-art technologies is necessary for all transport
and logistic companies to improve their activities and services. This digitization is giving
birth to huge and increasingly growing datasets that can be efficiently analysed to create
new business values in the transport and logistics domains (Borgi et al, 2017).
Transport companies are now taking advantage of the huge amount of data created in the
transport chain and start developing new or extend the already established services and
generate new assets. However, there is still a lack of financing and/or sustainable business
models to support their initiatives.
NOESIS Policy Briefs - Page 11
Table 1: Main obstacles identified
Lack of skilled technical personnel to work with data for Transport Authorities
Lack of data collaboration and sharing among institutions
Lack of understanding between transportation experts and data scientists
Lack of access to different data sources and data variability
Lack of standards in databases
Lack of data quality
Lack of infrastructure for storage
Cost of purchasing the data
Potential of open data not fully realized
Uncertainty about data ownership regulation
Uncertainty on data privacy and protection law
Uncertainty in the benefits provided by the investment in Big Data
Lack of financing or business models
NOESIS Policy Briefs - Page 12
Policy recommendations
Skilled personnel within transport authorities and companies
The following actions are proposed to increase the rate of skilled personnel within transport
authorities and companies:
To promote university programs, such as a Master of Science on Big Data for transport
management and systems, to improve the amount of people with common skills on
both Big Data and transport.
To encourage transport companies, the establishment of a Chief Data Officer (CDO)
position, at the highest level of the Data Governance Strategy of the company (NOESIS
D4.2, 2019). CDO will be a senior executive who is responsible for organization-wide
governance, management and utilization of data as an asset, data availability,
business intelligence, data mining, reporting, data quality initiatives, etc.
Main actors involved are:
Research and academia communities in Europe should promote a Master of Science
on data analytics for transport. To do so, academia and research centres from
different background such as data science, transportation science, law and business
management areas should cooperate. The Big Data in Transport Working Group
proposed by NOESIS could be the first step in the definition of this program.
Transport operators and authorities are encouraged to implement a Data Governance
Strategy including a Chief Data Officer with required skills in data science.
Policy recommendation 1
Educating personnel is as important as developing new tools. This includes training
staff on the use of data and creating business strategies that emphasize objective
data-driven analysis. The use of Internet of Things (IoT) devices is creating a
significant amount of transport related data as more and more people are connected
and are using such devices. This provides opportunities for Big Data application
developers and Big Data experts. The profile of skilled personnel should combine
analytical and statistical skills with appropriate level of business understanding.
NOESIS Policy Briefs - Page 13
Promoting cooperation among stakeholders
The following points are important for this:
The establishment of data collaboration communication and clear agreements
between partners. For instance, a Memorandum of Understanding (MoU) between
data providers and transport authorities can be established to develop new products.
The development of a data ecosystem platform to bring together large software
firms, SMEs and start-ups, the research and academia communities, and other
organisations interested in Big Data for transport. An important step for creating a
data ecosystem is to make IT interfaces explicit, well documented, less expensive
and easy to interrogate in order to acquire the data.
Main actors involved:
EU Institutions and Member States Governments at different levels could be a source
of public funds for promoting collaboration.
Investors, Venture Capitalist, and Incubators could also provide the funding by
collaborations with the government and policy makers as long as they foresee
significant returns in their investment.
Transport authorities could influence the development of certain big data solutions
and impose collaboration among stakeholders for the sake of coordination and the
wellbeing of society. It could for example require transport operators to share certain
data when they are operating transport services under their jurisdiction.
Transport operators, research and academia communities, start-ups and IT
companies. Transport operators could benefit from the financial instrument of
governments and investors for the implementation of big data solutions and establish
agreements with data providers, research community and IT companies. These
agreements may be the first step of data ecosystem platforms.
Policy recommendation 2
In the Big Data for transport sector there is a certain need for cooperation between
transport authorities, transport operators, research centres, IT companies, users, etc.
Transport operators have the opportunity to work together with IT companies and
the research community to develop intellectual capital. This way, academics and
companies will focus their research on transportation related problems. Transport
authorities, IT companies, researchers and users should work together to create
sustainable Big Data products and services.
NOESIS Policy Briefs - Page 14
Transferability of Big Data know-how
For this action, it could be proposed the following:
Promotion of data re-use across interoperable applications.
Promote data access through open data portals (especially for public authorities) and
understand the role open data will play in shaping future transport planning and
Encourage the development of sustainable business models for commercializing data.
Main actors:
Transport operators and authorities are encouraged to share their data and develop
sustainable business models.
IT companies (Start-ups and SMEs) are encouraged to develop new business models
based on already existing datasets.
Policy recommendation 3
Transferability refers to applying Big Data solutions designed in one context to a
different context (e.g., Lycett, 2013; Tallon et al. 2014). For example, a transport
authority that has been collecting GIS data from buses can sell these data to agencies
dealing with road maintenance to find locations that require repairs. Data that has
been collected in a very specific context may have a different value when it is used
by organisations in other contexts. Transferability is crucial for Big Data that may
leverage large volumes of varied data from many sources, considerably beyond
organisational boundaries.
NOESIS Policy Briefs - Page 15
Improve the quality of data
This action requires:
To promote certification mechanisms to ensure the standardization and quality of
Main actors:
Standardisation bodies could propose, develop, establish, monitor, and/or coordinate
voluntary standards or quality requirements for transport sector data. Those bodies
could be public or private sector, domestic or international organizations.
Transport authorities and operators are the organisations, that may require
certification or standardization for certain activities.
IT companies can start building databases and platforms according to the quality
requirements and standards defined above
Policy recommendation 4
This action includes the standardization of data and processes in order to facilitate
the use of data. Fostering the development of data standards will help to achieve
data quality, homogenization, accessibility and availability. Reach a wider audience
for any possible use of different transportation related applications could reduce the
cost of data collection and make intelligent transportation systems more feasible and
economical. Standardising interfaces and making them friendly and explicitly to
install and access can facilitate data management. This allows communication
network providers to work together to enable data scientists to cut their efforts in
gaining their raw data for transportation data analytics.
NOESIS Policy Briefs - Page 16
Opening data to boost the economy and research activities
Main actors:
European and Member States Governments are encouraged to help by stating clearly
how and what data is possible to open up among companies and governmental
bodies. As described previously, many actors are afraid of opening their data as they
do not know the repercussions.
Transport Authorities by gaining the right to distribute data among research groups
and students, great projects have spawned from opening up data. In the same sense,
transportation agencies could gain a much better insight by opening up their data to
let others do research on it, which they later can benefit from.
Research and Academia communities could help by utilising previously accessible data
to show how the results of utilising said data can benefit the organisations that have
opened their data. For instance, KTH in Sweden helped the public agency of Stockholm
to better understand how the expansion and alteration of the metro system is
affecting the how and where people decide to live (Tidebanan, 2019).
IT companies and start-ups similar as Research and Academia communities could use
open data to create their products.
Policy recommendation 5
Most transportation related data could and should be made publicly available to
organisations and consumers when there is no risk of data privacy being
compromised. This will encourage development of new tools and services and will
generate public excitement and participation. Transport organisations and other
public sector bodies can choose to open up data to stimulate innovation and provide
transparency. Mobility as a Service (MaaS) experts have suggested that public
transport operators, taxi companies, car clubs, bike share services and even
carmakers, should be obliged to make their data accessible to mobility companies.
Many large government agencies have the capacity and funding to gather huge
amounts of data. In addition, opening up those datasets can have major economic
benefits. For transport government/public organisations, the aim of the member
states should be to implement the revised PSI Directive quickly and fully. The recast
of the “Directive of the European Parliament and of the Council on open data and the
re-use of public sector information” is an important step, so that public sector
information can be reused. Especially in the field of transport and logistics, a great
potential for the use of public sector information can be identified.
NOESIS Policy Briefs - Page 17
Ensuring data protection compliance and supporting the
implementation of the GDPR
The following recommendations can increase the acceptance of data protection by
companies and organizations and, on the other hand, help them to work in compliance with
data protection law:
Resources for consultation to support companies and organisation in implementing
Further efforts should ensure to harmonised data protection framework in order to
lead to a uniform application of the GDPR. The European Data Protection Board
(EDPB) has a decisive coordinating role to play here.
To appoint a Data Protection Officer in the organization/company.
If GDPR is subject to an evaluation, to take into account expert opinions from research,
consulting and practice.
Main actors:
European and Member States Governments. On the one hand, supervisory authorities
of Member States should support organisation in the right implementation of GDPR.
On the other hand, EDPB should coordinate the harmonised data protection
application as unified as possible across member states and regions.
Public and private transport enterprises or organizations could appoint a DPO within
their organisation in charge of all data privacy, ownership, protection, and regulation
Policy recommendation 6
Data protection is often used as an argument that innovative projects cannot be
implemented. Although there are no exact numbers or studies, this argument only
seems to be a pretext. Even though the new data protection law is complex, as
outlined above, it should not be ignored.
NOESIS Policy Briefs - Page 18
Understand the benefits of big data solutions to better incorporate
them in transport
To implement this action, the following measures are recommended:
To promote the application of impact assessment methodologies to evaluate the
effects of big data solutions according to the goals of the organization and the society.
To develop detailed studies on the benefits, costs and risks of implementing big data
solutions to assist decision-makers.
Main actors:
Transport operators and authorities are encouraged to implement methods to assess
the value for money of big data solutions. This will help them to set a long-term vision
regarding the use of big data solutions.
Research and academia communities are recommended to study the impacts and risks
of big data solutions in transport in Europe by, for instance, taking as reference the
Policy recommendation 7
Understanding the benefits and costs of big data compared to traditional models is
key to justify the need to invest and finance new applications. The analysis of benefits
of using big data solutions in transport versus the costs of obtaining the data,
maintenance and operation, and their risks would permit to identify in which areas
of transportation the use of big data may be value for money. This analysis will help
transport organizations and investors to promote big data solutions in transport.
NOESIS Policy Briefs - Page 19
Encouragement of feasible business models and access to financing
To implement this action, the following measures are recommended:
Access to financing for start-ups should be facilitated such as venture capital and
other forms of new enterprise financing.
Promotion of alternative funding sources such as crowdfunding or PPPs
Using European financial instruments such as SME Instrument project (H2020) to
encourage cooperation between data driven start-ups and established large
corporate players.
Main actors:
Transport operators and authorities are encouraged to identify other financing
schemes to support their initiatives.
Businesses and start-ups are encouraged to develop new business models.
Governments/Public administration are encouraged to promote access to financing
for SME, PPPs.
Policy recommendation 8
Access to financing for start-ups should be facilitated such as venture capital and
other forms of new enterprise financing. Furthermore, alternative funding sources
should also be promoted such as crowdfunding. Various schemes representing
public-private partnerships (PPP) represent another funding source supporting new
business models for big data in transport.
NOESIS Policy Briefs - Page 20
Borgi, Tawfik & Zoghlami, Nesrine & Abed, Mourad. (2017). Big data for transport and
logistics: A review. 44-49. 10.1109/ASET.2017.7983742.
EU (2016) Best Practice: Open Up Public Transport Data. Available at:
Hoeren, T. (2019). Datenbesitz statt Dateneigentum. Erste Ansätze zur Neuausrichtung der
Diskussion um die Zuordnung von Daten, MMR 2019, 5-8 (5f.).
Jagadish, H., Gehrke, J., Labrinidis, A., Papakonstantinou, Y., Patrel, J., Ramakrishnan, R.,
Shahabi, C. (2014) Big data and its technical challenges. Communication of the ACM, 57(7),
86-94. Doi: 10.1145/2611567
Kennedy, H., & Moss, G. (2015) Known or knowing publics? Social media data mining and the
question of public agency. Big data and Society, 2(2), 1-11. Doi: 10.1177/2053951715611145
Kumar, A., & Prakash, A. (2014) The role of big data and analytics in smart cities. International
Journal of Science and Research IJSR, 6(14), 12-23.
Lycett, M. (2013) Datafication: making sense of (big) data in a complex world. European
Journal of Information Systems, 22(4), 381-386. Doi: 10.1057/ejis.2013.10
NOESIS D4.2 Data governance and institutional issues, 2019, Available at:
NOESIS D5.1 Data Benefit Analysis and Impact Assessment Methodologies (IAM) for
appraising big data solution in transport, 2019.
Soriano, F.R., Samper, J.J., Martinez, J.J., Cirilo, J.V., & Carrillo, E. (2016) Smart cities
technologies applied to sustainable transport. Open data management. In Telematics and
Information Systems (EATIS), 2016 8
Euro American Conference on (p. 1-5). IEEE.
Stepanov, I. (2019) Introducing a property right over data in the EU: the data producer’s right-
an evaluation. International Review of Law, Computers & Technlogy. Doi:
Tallon, P.P., Ramirez, R.V., & Short, J.E. (2014) The information artifact in IT governance:
toward a theory of information governance. Journal of Management Information Systems,
30(3), 141-177. Doi: 10.2753/MIS0742-1222300306
Wani, M.A., & Jabin, S. (2018) Big Data: Issues, Challenges, and Techniques in Business
Intelligence, in Big Data Analytics, 613-628. Springer Singapore.