Presentation of URBANITE Project on The European Big Data Value Forum (EBDVF).

1. Supporting the decision-making in urban transformation with the use of disruptive technologies
URBANITE Vision
Leire Orue-Echevarria, PhD, PMP (TECNALIA)
05.11.2020
Grant Agreement No. 870338 URBANITE 1

2. URBANITE Solution
Decision making in the urban transformation field
using disruptive technologies and a participatory approach
Grant Agreement No. 870338 URBANITE 2
Public
Administations
Local
Authorities
✓ Use data for better decision making
(analysis, simulation and prediction)
✓ Engage citizens and civil servants in the
policy making process increasing trust and
capturing the vision of all actors
✓Get careful guidance on the adoption and
implementation of disruptive technologies
(i.e. big data, artificial intelligence, cloud
computing, algorithms)
WHO For
WHAT

3. Pathways to provide public
administrations guidance on the
adoption of disruptive
technologies and data in their
policy making processes.
powerful analytics tools that
combine multiple data sources
with advanced algorithms,
simulation, recommendation
and visualization.
a platform supporting
the entire data processing chain
from collection, aggregation,
provisioning to using the data.
a digital co-creation
environment and a set of
approaches to help co-design
and co-create policy guidelines
with all involved actors.
Grant Agreement No. 870338 URBANITE 3
Modular – Adaptable – Interoperable – based on open standards
Decision-Support
System
SoPoLab
Data
Management
Platform
Recommendations
and
pathways
URBANITE Solution

4. URBANITE Solution
Main Features (1/2)
Make the most out of data
Prepare the data and make it usable with the URBANITE data curation components:
data quality checks, transform unstructured information into high quality data sets, address privacy
issues with anonymization and pseudonymization, guarantee data interoperability.
✓
Make the data management process more efficient
Handle the entire process: fetch data from various heterogenous sources , transform, fuse and map
it and store it in dedicated databases ready for its use.
Learn from short- intermediate- and long-term trends to improve urban mobility
e.g. learn from the trends of peak hours in which a street is blocked or from the use of a certain
transportation system (bikes, public transport, taxi etc.). Data analysis results will be visualized to
show traffic density, traffic flows, points of interest etc.
Anticipate behaviours and delimit unforeseen consequences
Simulate the effect of different traffic situations (through the use of artificial intelligence algorithms),
e.g. simulate the effect of opening a pedestrian street at certain times, of locating electric charging
stations or bike sharing points in certain areas.

5. URBANITE Solution
Main Features (2/2)
Identify potentially problematic or otherwise important events
These events would have a high price if discovered in the real life. Identify events with cutting edge
detection methods and validate mobility policies in a virtual environment with simulation
techniques
Create public policies and services “with” people and not just “for” them.
Put people at the centre of urban mobility policy making, making sure policies are based on shared
values and principles and address effective needs of the citizens and relevant stakeholders.
Foster cross-departmental collaboration by creating an urban ecosystem
Optimize urban management by involving public administrations, private transport companies and
citizens.
Boost and guide an efficient and successful digital transformation
Get guidance on the adoption and implementation of big data, artificial intelligence and algorithms in
urban mobility decision making.

6. a digital co-creation
environment and a set of
approaches to help co-design
and co-create policy guidelines
with all involved actors.
SoPoLab
▪ Find unmet needs – participants in the co-creation activities can suggest, report or vote
needs and social/local issues and bring them to the attention of policy makers.
▪ Support idea generation - supports Public Administrations in the collection of
spontaneous and guided ideas with an always-on and wherever available digital tool.
▪ Support co-creation workshops - enter documentation and any information useful for
the running of the workshop and enter ideas generated during the workshop to continue
idea generation and evaluation online.
▪ Resource sharing - different kinds of resources are available such as policy briefings,
multimedia content, methodologies, best practices that can support the successful
application of co-creation activities and the use of disruptive technologies.
▪ Keep track of the whole co-creation process - it provides the full history of ideas,
accompanying documentation, evaluations and voting.
▪ Select the best ideas together with all the stakeholders involved through idea voting
and idea management.
▪ Keep the community updated, improve transparency on the whole co-creation process
boost satisfaction, collaboration and engagement.
Grant Agreement No. 870338 URBANITE 6
SoPoLab
Main Features

7. a platform supporting
the entire data processing chain
from collection, aggregation,
provisioning to using the data.
Data Management Platform
▪ Data harvesting i.e. fetching data from various heterogenous sources,
transformation and mapping of data and metadata, and storing them in
dedicated databases.
▪ Data curation (preparation) - before the actual publication i.e. data
quality checks, data transformation and annotation, data
anonymization and pseudonymization.
▪ Data aggregation and storage i.e. storage of data and its semantic
description (metadata), aggregation and deduplication of the data that
originates from distinct sources and retrieval of the stored data via a
provided API.
Grant Agreement No. 870338 URBANITE 7
Data
Management
Platform
Main Features

8. powerful analytics tools that
combine multiple data sources
with advanced algorithms,
simulation, recommendation
and visualization.
Decision-Support System
▪ Big Data analysis methods;
▪ Intuitive and understandable visualizations;
▪ Traffic simulations of both current situation and hypothetical
situations (traffic density, traffic flows, points of interest etc.);
▪ Presentation of different proposals of action to choose from during
the decision–making processes;
▪ Detection/Prediction of potentially problematic or important events;
▪ Validation of mobility policies through predictions and simulations.
Grant Agreement No. 870338 URBANITE 8
Decision-Support
System
Main Features

9. Pathways to provide public
administrations guidance on the
adoption of disruptive
technologies and data in their
policy making processes.
Recommendations and Pathways
▪ Guidance on what to do and what not to do in the use of disruptive
technologies for decision–making processes.
▪ Tailored to the context and the needs of public administrations, co-
created with civil servants addressing their specific needs and doubts.
▪ Details on benefits, risks and potential of the disruptive technologies in
the mobility sector (big data, artificial intelligence, cloud computing,
algorithms).
▪ Attitude of civil servants towards the use of new and disruptive
technologies (big data, artificial intelligence, cloud computing, algorithms).
▪ Level of trust of citizens and other stakeholders as a result of the use of a
wide spectrum of disruptive technologies.
▪ Lessons learnt, recommendations and best practices from the application
of disruptive technologies in different domains, beyond mobility and
urban transformation.
▪ Suggestions on tool and techniques to use in co-creation sessions
Grant Agreement No. 870338 URBANITE 9
Recommendations
and
pathways
Main Value