Noesis project presentation

Ortelio Ltd
info@ortelio.co.uk
Novel Decision Support tool for Evaluating Strategic Big Data Investments in
Transport and Intelligent Mobility Services
Project presentation

http://noesis-project.eu
Contents
NOESIS objectives and overall vision01
NOESIS methodology02
Work plan03
Impact04
NOESIS project presentation 2
Consortium05

NOESIS in a nutshell
Lack of a robust
methodology for
understanding and predicting
the potential value and
benefits generated from big
data applications in transport
against the associated
investment risks.
To investigate the
implications of the
utilisation of big data in the
transport field.
NOESIS aims to identify the
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 problem Main challenge Solution

NOESIS vision
Provide a robust methodological framework (Decision Support tool) and data-driven evidence
to enable the deployment of a Big Data in Transport ecosystem in Europe, by addressing the
associated technological, institutional/legal, business, and policy challenges.
Decision Support tool which will be able to assess the value
generated (i.e. socioeconomic impact) 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 a
number of big data (machine learning) techniques.
Main outcome

NOESIS objectives (1/2)
To build and maintain the first organized collection of use cases for Big Data
applications and services in the field of transport and logistics.
To investigate the pattern(s) behind the success (and failure?) of big data
services/investments in terms of value generation.
To identify the methodological issues and to develop appropriate tools in order to
allow for effective data mining and data exploitation for transport related challenges.
To develop an Impact Assessment methodology for assessing the socioeconomic
impact of Big data applications.
01
02
03
04
WP3
WP2
WP5
WP2

NOESIS objectives (2/2)
To create new business opportunities for SMEs of the new Economy in providing
high quality and low cost, big data applications in transport.
To create a Responsible Code of conduct for big data management in transport.
To develop a methodology for assessing the socio-economic value of Big Data
applications, in order to boost social inclusion in service provision and opportunities
throughout Europe.
To provide know-how and methodological ideas to big data domains outside
transport for the transfer of the methodological approach.
05
06
07
08
WP4
WP2
WP5
WP6
WP5
To provide an integrated policy analysis tool in respect to big data implementation.09 WP4

Contents
Work plan03
Impact04
Consortium05

NOESIS methodology
Value CaptureLearning process (big data approach)Transport domain features and cases
Classification of Big
Data Transport
challenges
Transport
use cases
Big Data
products
Big Data in
Transport
Library
Learning Framework
Features /
scores selection
and values
Decision
Support Tool
Machine Learning
Supervised / Unsupervised learning
techniques
Score 1
Score 2
Score n
Impact
Assessment
Methodology
Pillar 1 Pillar 2 Pillar 3
Data Benefit
Analysis

Pillar 1 - NOESIS Big Data in Transport Library
03
Transport use cases (systemic description)
Expected value generated (“label”) for
each pair of transport use case and
associated Big Data application (Data
Benefit Analysis)
Annotated list of
Big data applications
Big Data applications in
Transport Library
04
02
01

Pillar 2 - NOESIS Learning (Big Data) Framework
Value GeneratedMachine Learning
Data Benefit
Analysis
Big Data in
Transport Library
Large set of
use cases
(“training
examples” /
“training set”)
Supervised Learning
Unsupervised Learning
Initial set of evaluation
criteria and KPIs that
assess and quantify the
benefits derived from Big
Data applications in
Transport Use cases
Insights on
value
generated
from big data
applications
for transport
projects

Pillar 3 - NOESIS Value capture mechanism
Translate the benefits
identified in Data
Benefit Analysis into
socio-economic value
Define potential
business models to
facilitate the adoption
of big data applications
for public and private
stakeholders
Impact Assessment
Methodology
Business models

Contents
Work plan03
Impact04
Consortium05

NOESIS work plan
WP1: Project Management
WP6 Dissemination, Communication and Exploitation
Task 2.1
Emerging
transportation
challenges in the
era of Big Data
and optimization
Task 2.2
Overview of Big
Data technologies,
data availability
and gaps: Big Data
products for
transport
Task 2.3
Identification of
transport related
features for Big
Data investments
and services
Task 2.4
Set-up of a
European Big
Data Transport
Library
Task 3.1
Decision Support
tool architecture
Task 3.2
Development
and validation of
the Decision
Support tool
Task 5.1
Impact
assessment of
big data for
transport
Task 5.2
Business and
organizational
models
Task 5.3
Technological and policy
roadmaps for Big Data
investments in Transport
Task 4.1
Legal barriers and
constraints
Task 4.2
Business and
organizational models
Task 4.3
Key lessons learnt and
transferable practices
WP7:Ethics
WP2: Transport Planning paradigm shift
and Big Data Use Cases
WP5: Social added value, business models
and sustainability
WP3: Big Data Learning Framework
WP4: Key lessons learnt from cross
transport sectors and contexts

Contents
Work plan03
Impact04
Consortium05

NOESIS impact
Help to take evidence-based decisions (for policy makers, transport companies)
Consolidate our knowledge on big data methodological and exploitation issues
Tackle the lack of a holistic evaluation framework that seeks to comprehensively
understand and assess the impact of the various big data
Identify critical issues to design a legal framework
01
02
03
04

Contents
Work plan03
Impact04
Consortium05

NOESIS consortium
Ortelio Ltd
United Kingdom
Technical University of Munich
Germany
Coventry University Enterprises Ltd
United Kingdom
University of Belgrade,
The Faculty of Transport and Traffic
Engineering Serbia
Leibniz Supercomputing Centre
Germany
Royal Institute of Technology in Stockholm
Sweden
Universidad Politécnica De Madrid
Spain
Macomi
Netherlands

Thank you!
NOESIS project has received funding by the Horizon 2020 research and innovation
programme of the European Union (No 769980).
info@ortelio.co.uk

Noesis project presentation

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