The document discusses using disruptive technologies like big data, data analytics, and edge computing to support decision making in urban transformation. It describes the URBANITE project which developed a data management platform, decision support system, and digital co-creation environment to help public administrations adopt these technologies. The platform was tested in four use cases focusing on mobility including improving bikeability in Amsterdam, integrating data for traffic planning in Helsinki, transforming a public square in Bilbao, and building multimodal transit in Messina.

1. Supporting the decision-making in urban transformation with the use of disruptive technologies
URBANITE Vision
Final Event, 14.06.2023
Grant Agreement No. 870338 URBANITE 1
OASC Annual Summit & General Assembly, 2023
14th June 2023

2. Context
• Mobility transformation is changing the landscape of urban planning
and the management of mobility in cities. Public administrations and
policy makers need means to help them understand these new
scenarios, supporting them in making policy.
• Disruptive technologies to support policy – makers come into place,
not without generating controversy, and presenting specific challenges.
In addition, new legal, ethical and a policy frameworks including
guidelines, procedures and tools must be designed, always keeping in
mind a user-centered approach.

3. Objectives
Adoption of a Data - driven and Evidence - based Decision making in
the urban transformation field, specifically on Urban Mobility.
Principles:
✓The use of data for better decision making
✓Involving related agents, stakeholders and public servants in the policy
formulation process, capturing the vision of all stakeholders.
Participative process and the creation of a community
✓ Adopting a user-centric approach: addressing the expectations, trust and
attitude from civil servants, citizens and local stakeholders in the use of
technologies as BigData, Data analytics, simulation or Edge Computing.

4. URBANITE Solution
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.
Decision-Support
System
SoPoLab
Data
Management
Platform
Recommendations
and
pathways
URBANITE Solution

5. Use Cases
Grant Agreement No. 870338 URBANITE 5

6. Amsterdam use case – A bikeable city
Amsterdam, the capital of the Netherlands, has
been growing rapidly in terms of inhabitants and
visitors; this growth leads to increased mobility
and traffic issues. The city has complex traffic
streams with massive amounts of bicycles
combined with cars and public transport. To
manage these traffic issues there is a need for
better data analysis in order to create
sustainable mobility solutions.
As cycling is one of the key mobility options in
the city, any innovation and intervention has to
take this into account. As Amsterdam wants to
continuously improve its network of bike lanes
and the bike experience of cyclists, the
municipality is keen on gaining new insights
through biking data.
Grant Agreement No. 870338 URBANITE 6

7. Amsterdam use case – A bikeable city
Objectives
▪ Improving level of comfort by managing bike "flows" in the
city and preventing bike traffic jams; the programme
promotes cycling as main transport mode mainly by:
▪ Improving level of comfort by managing bike “flows”
in the city and preventing bike traffic jams.
▪ Stimulating cycling in neighborhoods
▪ Amsterdam seeks to co-create policies with citizens and
recently adopted a “participatory paragraph” to
accompany all policies and proposals going to the city
council.
▪ Apply the city data strategy, about the storage and use
of data related to Amsterdam residents and the city in
general.
Grant Agreement No. 870338 URBANITE 7

8. Amsterdam use case – A bikeable city
Work
▪ Emphasise cycling as the smart mobility option;
can we visualise the impact on mobility and the
city if inner-city traffic would be (more) bicycle-
based?
▪ Data visualisation, integration/analysis tools, and
dashboards through which decision-makers and
policy makers gain new insights about mobility in
Amsterdam.
Grant Agreement No. 870338 URBANITE 8

9. Amsterdam use case – A bikeable city
Grant Agreement No. 870338 URBANITE 9
M.O.U.
Datacommons
Bicycle happiness
SMEs
Mobility operators
Public Administration
Private companies
Associations
Data

10. Amsterdam use case
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Bike OD Matrix Prediction
Bike trajectory analysis
Bike data (safety index)
Bike OD Matrix Prediction
Bike trajectory analysis
Bike data
(safety index)

11. Safety Index impact of a new neighborhood
Grant Agreement No. 870338 URBANITE 11

12. Helsinki use case – Integrating mobility data into traffic planning
Helsinki is the most populous city and a growing
capital of Finland. A particular example of these
changes is Jätkäsaari area. The shore area of
Jätkäsaari, literally meaning ‘Dockers’ Island’,
previously used for industrial and harbour
purposes, is gradually being transformed into a
district offering residential areas, workplaces
and services.
Jätkäsaari has a growing passenger and
transport harbour which is right adjacent to the
centre of Helsinki. A single main road leads
traffic flow in and out of Jätkäsaari. This road
feeds directly to the largest car commuting
junction (70.000 cars daily) from the city centre
to the western suburbs of Helsinki, creating
interference.
Grant Agreement No. 870338 URBANITE 12

13. Objectives
▪ The goal is that traffic data becomes part of the
everyday life of traffic and urban planners,
experts and officials.
▪ Increase understanding how traffic congestion
in Jätkäsaari is linked with ferry traffic in the
area and by providing a data-driven approach to
advance effective planning and traffic-
management strategies.
▪ More generally, to overcome information silos:
plenty of data is available, but it is located in
different (virtual) places; this implies the need
of seamless access to transport and mobility
data.
▪ In case of Jätkäsaari pilot, the aim is to identify
impacts of ad hoc interventions with traffic
network and/or urban planning on traffic
situation.
Grant Agreement No. 870338 URBANITE 13
Helsinki use case – Integrating mobility data into traffic planning

14. Work
▪ Data visualisation, integration/analysis tools to
increase understanding of mobility in Helsinki. In
the project, a URBANITE data platform has been
jointly developed with the City of Helsinki's project
targeting to compose a data ecosystem to mobility
data.
▪ Cooperation between urban and traffic planners
and researchers, companies and academic
researchers is promoted and their opportunities to
adopt and comprehend new, disruptive
technologies is increased.
▪ Data related to car traffic, ferry traffic and city
bikes in use.
Grant Agreement No. 870338 URBANITE 14
Helsinki use case – Integrating mobility data into traffic planning

15. Grant Agreement No. 870338 URBANITE 15
Data-sources and stakeholders
Helsinki Regional Transport Authority (HSL)
Smart Junction
Schedules of ferry arrivals and
departures
Traffic Flow in the city and Harbour flow
(Heavy and car traffic)
Urban Environment
Division
Traffic Researcher, Traffic
Planner and Urban Planner
Helsinki Region
Transport HSL
Transport researchers
Helsinki use case – Integrating mobility data into traffic planning

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Bike OD Matrix
Prediction
Traffic prediction and global analysis
Noise analysis
Helsinki use case – Integrating mobility data into traffic planning

17. Congestion levels due new infrastructures
Grant Agreement No. 870338 URBANITE 17

18. Bilbao use case – Moyúa, a citizen-centric plaza
Grant Agreement No. 870338 URBANITE 18
➢ Important urban transformation in the last
25 years
➢Prioritization of pedestrian-friendly
environments
➢SUMP > “Pedestrian Mobility Strategy”
➢Sustainable mobility objectives.
65 %
of the movements
are made by foot

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Objectives
Transformation of Moyúa square, allowing its
exclusive use to:
Take decisions based on updated data. Make
decisions based on the most updated information
enable to predict impact of the applied measures.
Agile decision-making process. Fit better the real
needs of the city
Health and life quality indicators. Translate
measures impact into health and life quality
indicators
Bilbao use case – Moyúa, a citizen-centric plaza

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Data-driven decision mechanisms
Know the influence of the
socio-economic and cultural factors
Gather the necessary data
Bilbao use case – Moyúa, a citizen-centric plaza
Work

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Forums
SMEs
Mobility operators
Public Administration
Private companies
Associations
Bilbao use case – Moyúa, a citizen-centric plaza
✓ Traffic counts
✓ Biking public service traces
✓ Public Transport (Buses) transactions
✓ O/D Matrices (based on the Municipal WiFI)
✓ Futbol events
Data-sources and stakeholders

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Traffic OD Matrix
Characterization
Bike OD Matrix Prediction
Public Transport
O/D Matrix
Characterization
Bilbao use case – Moyúa, a citizen-centric plaza

23. Impact in emission of closing Moyua Squeare
Grant Agreement No. 870338 URBANITE 23

24. Messina use case – Building a multimodal city
Grant Agreement No. 870338 URBANITE 24
The metropolitan area of Messina is one of the most extended areas of the south of Italy
and the first in Sicily It counts over 620.000 citizens, whereas the city counts over 250.000
citizens It is located in the extreme north-eastern part of Sicily and is therefore the
geographically closest city to the Italian peninsula. Due to this location, it is subject to a high
flow of people, vehicles and goods that move daily from Sicily to other regions of Italy and
vice versa.
The port of Messina is one of the most
important in the Mediterranean area, both
for military and civil use, and is located in the
center of the city, in a strategic multimodal
interchange point, where naval, railway,
tramway, public and private transport lines
converge for urban, regional and
interregional transport.

25. Messina use case – Building a multimodal city
Objectives
▪ To build mobility services able to fulfil
the need of citizens, dwellers,
commuters and visitors, allowing
them to move around and through
the city seamlessly.
▪ Optimise mobility and integrating
multimodal transport services for the
city.
Grant Agreement No. 870338 URBANITE 25

26. Messina use case – Building a multimodal city
Grant Agreement No. 870338 URBANITE 26
▪ Develop public administration trust in the use
of innovative technologies.
▪ Extract added value from available data
(Analytics).
▪ Use of simulations and AI to obtain
suggestions for decision makers (DSS Decision
Support System) and definition of KPIs.
▪ Improved Data Sharing (Silos-Effect).
▪ Presentation of tools for Participatory
Democracy processes (Decidim).
▪ SOPOLAB (Social Policy Laboratory).
Work

27. Grant Agreement No. 870338 URBANITE 27
1) Static Data (POIs,Districts, Population, Mobility surveys, bus stops, Cycling
paths,camera locations, etc..)
2) Time series (Public transport OpenGTS, E.M. noise, vehicles count, weather,
acoustic noise, air quality, etc..)
Available Time Series data
Available Static data
The new cameras have
been geo-localized and
used to collect data on
traffic flows
Co-operation and data-enrichment with
existing systems available to the
municipality
Messina use case – Building a multimodal city
Public Administration
Private companies
Data-sources and stakeholders

28. MESSINA EDGE COMPONENT:
1) DATA IMPORTER
2) MESSINA DATA STORAGE
3) DATA PROCESSOR
The data collected and the virtual machines used were created on the systems of the CED, Department of the
Municipality of Messina
Messina use case – Building a multimodal city

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1
2
3
1
1
3
2
Messina use case – Building a multimodal city

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Private
Dashboard 1
Public
Dashboard 3
Private
Dashboard 2
The Urbanite platform allows you to create customized dashboards containing data and analysis and
make them shareable privately or publicly.
Messina use case – Building a multimodal city

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Conclusions. Challenges
The development of a local (mobility) data ecosystem is a complex process, in
which challenges appear at each crucial step. These challenging steps are:
• Identify the need for data and use / development of disruptive technology
• Gain awareness of existing data
• Access existing data
• Ensure quality, cleanness, completeness, and accuracy of data
• Meet interoperability standards

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Conclusions. Recommendations
Design Process Priorities
• Participatory development
• Including ‘end-users’ (decision-makers) throughout the entire design process is the only way to ensure
the relevance of technical solutions.
• Start the development and implementation of disruptive technologies by identifying the problem
owner, who should be data literate and educated on the potential and limitations of technology.
• Participatory development should also include stakeholders from outside of the municipality.
• Identification of a shared mission
• A shared mission is helpful in practical matters around collaborative development, and it helps to ensure
that the technology furthers the mission of democratic institutions to protect public values including
safety, privacy, and human agency.
• Open (standards / source / process)
• Openness should be applied throughout development and in many different respects: open standards,
open source, and open processes

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Conclusions. Recommendations
Design Process Priorities
• Modular and iterative development
• Modules should be able to function and serve a unique function on their own, but also be compatible
and complimentary to a larger data ecosystem.
• Modular development is supported by working iteratively and in sprints with smaller data sets.
• Visualisations and mock-ups are useful in mid-term feedback session
• Education
• Education ought to occur in multiple directions during the entire participatory development process.
• Decision-making capacity requires a deep and nuanced data literacy, the development of which is aided
by active participation in co-creation sessions alongside others with diverse multidisciplinary expertise
Supporting tools
• Tools support decision-makers not replace them
• Explainability. Models and its output can be explained in a way that “makes sense” to a human being at
an acceptable level.

34. Conclusions. Question of Trust and Confidence
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Trust about the use of disruptive technologies is a prerequisite for their application in matters of society and
governance. Trust applies on many levels in this regard and from the perspective of citizens and decision-makers
alike, for example:
• trust the data is accurate and reflective of peoples’ lived experiences;
• trust that decision-makers are knowledgeable about the nuances and limitations of the technology they are
using, and are comfortable that with using it;
• trust that decisions are made by humans (not technology);
• trust that privacy and other public values are ensured by the technical systems’ design;
• trust that the technology is open and transparent;
• trust that outputs of AI are explainable valid;
• trust that (human) decisions informed by AI outputs are explainable and valid;
• and more.
The goal is not to develop trust amongst society (about the use of disruptive tech); but rather to build
technology and protocols that merit public trust because of their openness and alignment with public values

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Relevant information
Open-Source code (GitLab) URBANITE Webpage

36. SoPoLab
36
This project has received funding from the European Union’s Horizon 2020 research and
innovation programme under grant agreement No 870338
Web: www.urbanite-h2020.eu
Twitter: @urbaniteh2020
LinkedIn: www.linkedin.com/groups/69691
Slideshare: www.slideshare.net/URBANITEProject
GitHub: git.code.tecnalia.com/urbanite