The NOESIS project, funded by the EU's Horizon 2020 program, aims to evaluate big data investments in transport over 24 months, involving eight partners with a budget of €1.5 million. The research identifies significant gaps in data accessibility, collaboration, and infrastructure, while also highlighting opportunities for predictive modeling and open data initiatives to enhance service efficiency in transport. Recommendations for effective big data strategies emphasize the importance of building partnerships, ensuring data privacy, and promoting skilled personnel development.

1. Ilias Trochidis
Ortelio Ltd
it@ortelio.co.uk
Big Data in Transport – Gaps and opportunities

2. NOESIS project has received funding by the Horizon 2020 research and innovation programme
of the European Union (No 769980).
NOESIS (Novel Decision Support tool for Evaluating Strategic Big Data
Investments in Transport and Intelligent Mobility Services)
- Action: CSA
- Duration: 24 months project (Nov 2017 – Oct 2019)
- Consortium: 8 partners
- EC funding: 1.5M
- Website: https://noesis-project.eu

3. www.ortelio.co.uk
The outcomes of this research are based
on:
 Extensive research conducted in the
literature where there is a number of
scientific articles and blog posts
mentioning Big Data in transport use
cases.
 NOESIS Big Data in Transport Library (the
Big Data in transport use cases that have
been reported by NOESIS partners).
 EU funded projects in transport.
Big Data in Transport - Gaps and opportunities
CIVITAS Forum 2019, Graz 3 October 2019 3
https://noesis-project.eu/toolbox

4. www.ortelio.co.uk
A growing number of technology companies and start-ups
have started developing Big Data tools focused in the
transportation sector. New business models have
emerged.
A growing number of transport organisations have started
utilizing Big Data tools in order to:
 offer better services to customers,
 improve efficiency,
 reduce organisational costs,
 understand how their system is performing,
 assess the impact of a policy/program/project,
 identify and respond to issues in real-time,
 make shorter-term predictions based on incidents, weather etc.,
 to make smarter prioritization of policies and investments.
Big Data in Transport landscape
CIVITAS Forum 2019, Graz 3 October 2019 4

5. www.ortelio.co.uk
Data size grows tremendously every day, but not all transport organisations
use it to derive insights and make decisions.
Potential of Big Data not yet fully realized
CIVITAS Forum 2019, Graz 3 October 2019 5
What are the gaps and opportunities when
developing Big Data in Transport solutions?

6. www.ortelio.co.uk
 Lack of data or data difficult to find: In transportation,
data collection and dissemination is often fragmented
and inconsistent.
 Lack of collaboration and sharing: Partnerships and
synergies are needed with public or private data
providers and with other transport authorities.
 Data ownership: related to the previous one, this gap
deals with the fact that many organisations do not
share their data.
 Potential of open data not fully realized
Gaps in the Big Data in transport domain
CIVITAS Forum 2019, Graz 3 October 2019 6

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 Lack of skilled technical personnel to
make data accessible: There are important
technical challenges to make datasets
available and to develop insights from
these datasets.
 Lack of data infrastructure: The secure
storage, management and analysis of Big
Data requires investment both in
hardware and in software.
Gaps in the Big Data in transport domain
CIVITAS Forum 2019, Graz 3 October 2019 7

8. www.ortelio.co.uk
 Lack of standards: There is a lack of standards to data formats and
structures for many types of data relevant to transport.
 Data privacy: There is a concern by transport authorities that creating
data and making data available (even in anonymised form) may violate
the GDPR (General Data Privacy Regulation).
Gaps in the Big Data in transport domain
CIVITAS Forum 2019, Graz 3 October 2019 8

9. www.ortelio.co.uk
 Business opportunities: Big Data can help organisations (SMEs, large
organisations, transport authorities) gain a competitive advantage.
 Collaboration: In the Big Data for transport sector there is a certain
opportunity for collaboration between transport providers, businesses,
research centres and software developers.
 MoU with data providers is needed: despite the challenges, integrating
datasets through public-private data sharing can offer clear advantages.
 Transferability: refers to the potential of transferring Big Data from one
context of application to be used in other contexts.
Opportunities
CIVITAS Forum 2019, Graz 3 October 2019 9

10. www.ortelio.co.uk
 Development of predictive models: until now most data analysis
efforts have focused on analysing the past to plan a better future. Now
there is growing need to develop predictive models.
 Skilled personnel: Opportunities for Big Data application developers
and Big Data experts.
 Opening data to boost economy: Transportation related data could and
should be made publicly available to organisations and consumers
when there is no risk of data privacy being compromised.
Opportunities
CIVITAS Forum 2019, Graz 3 October 2019 10

11. www.ortelio.co.uk
 More than 15,000 open data users are currently registered to TfL’s API
 More than 650 apps have been developed based on the open data
provided
 These apps are regularly being used by 42% of Londoners
 In 2017, research commissioned by TfL and conducted by Deloitte,
found that the provision of free, accurate and real time open data is
helping London’s economy by up to £130 million a year
Open data example
CIVITAS Forum 2019, Graz 3 October 2019 11

12. www.ortelio.co.uk
 Fall in love with the problem and not with the solution
 Create value for customers
 Develop a Big Data strategy and a vision that identifies and capitalizes on new opportunities
 Build relationships, create partnerships and establish trust with key stakeholders (e.g.
transport providers, businesses, research centres and software developers etc.)
 Incorporate data privacy and security mechanisms
 Implement results quickly as the competition is big but bear in mind that returns on data
investments might take time: innovation in transport requires time, and commitment
 Understand that different skills needed
 Real-time data analytics and predictive models is the future and promises to provide better
decision making
 Analyse data from multiple, disparate sources and beware of low quality data
Business opportunities / recommendations
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13. Thank you!
NOESIS project has received funding by the Horizon 2020 research and innovation
programme of the European Union (No 769980).
www.ortelio.co.uk
Ilias Trochidis
it@ortelio.co.uk
http://ortelio.co.uk
https://noesis-project.eu
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