qrowd - because big data integration is humanly...

QROWD - Because Big Data Integration is Humanly

Possible Innovation action

D9.5 – Business plans

Author/s Sabina Guaylupo, Isabel Ávila, Miguel

Saldaña, Pedro López

Due date 30.11.2019

Version 2.0

Dissemination level PU

Status Final

Project co-funded under Horizon 2020 Research and Innovation EU programme, grant agreement no. 732194

Ref. Ares(2019)7613894 - 11/12/2019


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Table of contents

ABSTRACT ................................................................................................................ 4

ACRONYMS .............................................................................................................. 5

EXECUTIVE SUMMARY ............................................................................................ 6

1.INTRODUCTION: “WHAT WE WANTED TO DO” (OBJECTIVES OF THE PROJECT) ................................................................................................................. 7

2.(“WHAT WE’VE GOT”): OU .................................................................................... 8

2.1 Non-Commercial results 8

2.2 Commercial/tangible outputs 11

2.2.1 Standalone components .......................................................................... 11

2.2.2 Integrated framework ............................................................................... 13

3.WHAT THERE’S AROUND/ WHERE WE ARE”): CONTEXT ............................... 14

3.1. Competitive environment 15

3.1.1. PEST ....................................................................................................... 14

3.1.2. European institutional projects and initiatives.......................................... 16

3.2. Demand 17

3.2.1. Smart Cities: consolidation and challenges ............................................. 17

3.2.2. Other potential customers ....................................................................... 21

3.3 Supply – alternative solutions 21

3.3.1. Commercial competitors.......................................................................... 22

3.3.2 Initiatives potentially competitors to QROWD ........................................... 24

4.(“WHAT WE CAN DO”): POSSIBLE STRATEGIES .............................................. 28

4.1 Business Model 28

4.1.1 Independent activities (Standalone components) ..................................... 28

4.1.2. Integrated approach ................................................................................ 39

4.2 Marketing Strategy 44

4.2.1 Market approach ...................................................................................... 44

4.2.2. Entry ........................................................................................................ 46

4.2.3. Sales ....................................................................................................... 46

4.2.4. Costs ....................................................................................................... 47

4.2.5. IPR .......................................................................................................... 47

5.CONCLUSIONS .................................................................................................... 46

6.ANNEXES ............................................................................................................. 49

Annex 1 Priority 1& 2Smart Cities 49


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List of figures

Figure 1 - QROWD framework of integrated components13

Figure 2 - QROWD market model14

Figure 3 - Criteria for segmentation of Smart Cities21

Figure 4 - Distribution of Mobility solutions, Smart City Expo 201923

Figure 5 - QROWD offering lean model canvas40

Figure 6 - Smart Cities priority groups41

List of tables

Table 1 - Non-commercial outcomes by partner9

Table 2 - Smart Cities geography19

Table 3 - Smart Cities by population groups20

Table 4 - Main IT integrators in the industry of services for Smart Cities22

Table 11 - TomTom’s main exploitable results29

Table 12 - Atos’ main exploitable results30

Table 13 - AI4BD’s main exploitable results31

Table 14 - InfAI’s main exploitable results33

Table 15 - UNITN’s main exploitable results34

Table 16 - SOTON’s main exploitable results36

Table 17 - Summary of QROWD partners’ exploitation paths37

Table 18 - Possible collaborations among partners39

Table 19 - Numbers of SC by priority group41

Table 20 - Smart Cities’ main challenges and needs42

Table 21 - Summary of combined offers44

Table 22 - Summary of IP Rights46


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ABSTRACT This document outlines the QROWD predicted business plans for both standalone components and as integrated set of solutions, beyond the project’s lifespan. The deliverable includes an overview of the project’s main results, an update of the environment and market research/analysis and a framework to display the business model, the marketing plans and their financial preliminary forecasts.


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ACRONYMS B2B: Business to Business (market model) B2G: Business to Government (market model) B2B2G: Business to Business to Government BDVA: Big Data Value Association BDV cPPP: Big Data Value Contractual Public Private Partnership DEP: Dissemination and Engagement Plan DOA: Description of Action (Annex I of EC Grant Agreement) HPD: High-Performance Computing ICT: Information and Communication Technologies LH: Lighthouse (project) MT: Municipality of Trento PA: Public Administration PMB: Project Management Board RP1: Reporting Period 1 RP2: Reporting Period 2 RFQ: Request for Quotation SC: Smart Cities UNITN: Università degli Studi di Trento


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EXECUTIVE SUMMARY The QROWD Business Plan (D9.5) presents the strategy for putting into practice the outcomes of the QROWD project, including both joint (consortium-wide) and individual (partner´s) exploitation activities. It comprises Business Plans guiding the commercial and non-commercial exploitation paths to ensure the acceptance of market players and stakeholders and the economic sustainability of QROWD´s tangible results. As primary outcomes of the projects, the different elements developed under the generic denomination of Advanced Road Information System, are about to be exploited by TomTom as an upgrade of its current offer. At the same time, Atos plans to take advantage of the progress achieved in skills for data acquisition and integration activities to improve its position in the market of information solutions for public institutions (Public Administrations, Smart Cities). Additionally, AI4BD has developed two solutions inspired from QROWD platform, named AIStudio -as a direct evolution from the project’s data platform- and CBR Coworker. In that sense, QROWD’s has the potential to provide an integrated approach of customized solutions for each smart city’s specific priorities and constraints. In that sense, Atos’ DAF (Data Acquisition Framework) can be pivotal for a joint offer of citizen-centric data services for cities. For that purpose, QROWD’s integrated commercial offering business model is built following the lean canvas model methodology and it consists on a business-to-government-to-consumer (B2G2C) model based on a Software-as-a-Service revenue stream and licensing model. Its target customers are the different segments of Smart Cities; and, to a lesser extent, other suppliers of individual solutions in the field of urban mobility. Furthermore, non-commercial QROWD exploitable outputs should be considered as drivers for creation of knowledge and setting of trends in data-driven solutions for cities and their citizens.


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1.INTRODUCTION: “WHAT WE WANTED TO DO”

(OBJECTIVES OF THE PROJECT)

QROWD project was envisaged to help to take advantage of Big Data and Internet of Things in European Cities, by new and multidisciplinary methods of data integration, combining human feedback channels with geographic, transport, meteorological, cross domain and news information. Therefore, the project’s QROWD’s main objectives were defined as: ▪ To create a better overview of the city traffic by enhancing cross-sectoral Big Data

stream integration for urban mobility from a wide array of sources: real-time data on individual and public transportation, weather conditions and infrastructure data

▪ To foster participation and feedback of various stakeholder groups ▪ To build a platform combining efficient algorithms with human computation and

feedback

In practice, the two main outcomes of QROWD were to be: ▪ Two data value chains in the sectors of urban mobility and public transportation

using a mix of large scale heterogeneous multilingual datasets; ▪ Cross-sectorial and cross-lingual technology, with algorithms and tools covering all

phases of the cross-sectorial Big Data Value Chain, based on a flexible and efficient combination of human and machine-based computation.


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2.(“WHAT WE’VE GOT”): OUTPUTS

2.1 Non-Commercial results The non-commercial outputs of QROWD project can be described in terms of knowledge and relationships. Both are expected to be used by all partners to strengthen the expertise of the partners in the project’s related fields, such as new collaborative research work, future projects or the improvement of current offerings. Also, knowledge from partners and all stakeholders has been used to co-create the current QROWD commercial and non-commercial (e.g. operative, managerial, dissemination or exploitation actions) outputs. Moreover, the project best practices, publications and tools will allow all partners to enhance their capacity building in the field of services for Smart Cities. The following paragraphs describe the main advances achieved in the project for knowledge and relationships: Knowledge: ▪ In terms of contents, foremost intellectual progress has been achieved in:

▪ Better understanding of needs and constraints of cities. Main advances have been reflected in the development of a refined framework for citizen-centric development of smart city services (see White Paper “Citizen-Centric Services for Smarter Cities”)

▪ Insights for more advanced approaches in data integration and analysis solutions, with special focus on crowdsourcing initiatives

▪ An important part of this knowledge is been translated into identifiable

documents, in terms of scientific papers. A detailed list of publication is available at deliverable D9.4, ”Outreach report v2”;

Additionally, a series of non-commercial exploitation opportunities can be obtained from this acquired knowledge:

▪ Prospects and ideas for new projects and pilots ▪ Teaching material and use cases

Relationships The so-called relational capital is an extremely important asset in activities of research, development and innovation. Therefore, it is important to consider some important outputs of QROWD, both inside and outside of the project: ▪ Internal relationships (collaboration among the members of the consortium). The

heterogeneity of the different partners has brought important benefits in several aspects


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▪ Transfer of Information and skills, thanks to the complementarity of members’ fields of expertise

▪ Joint exploration of possible opportunities for future projects ▪ Improvement of experience related to project management activities and

coordination tasks ▪ External relationships: QROWD has been an opportunity for members of the

consortium for gaining ground in Smart Cities’ rich ecosystem of municipalities, projects, partners, communities and events. Significant advances have been achieved in: ▪ Increased awareness and better positioning as experts in the environment of

Smart Cities ▪ Personal and institutional contacts with representatives and decision makers with

impact in their commercial activities. These two major resources, knowledge and relationships, are key for players in the data driven science environment to achieve paths of development and to eventually achieve breakthroughs in R+I+i. The next table contains a more detailed list of non-commercial outcomes gained by each partner:

Table 1 - Non-commercial outcomes by partner

Partner Main output Detail

TomTom Use cases (for itineraries)

Development of use cases suggesting to drivers multiple itineraries and depending their criteria (fastest routes but also scenic route, available POIs/services at the destination, feedback from previous visitors, etc.) they get suggestion (e.g. a Top 3 best option)

TomTom Understanding of final users' needs: drivers

Research on how better share information to drivers (cognitive load) by using schematic maps for trip planning but also during the driving time.

TomTom Awareness of opportunities about data integration

Better understanding of opportunity of sourcing more datasets from citizens, users, etc. to understand better their needs to develop more customize services and to improve and develop relevant services/products.

TomTom

Understanding of final users' needs: public administrations

Understanding better the needs of a city like Trento in managing traffic. This helped in the development of prototypes (Route Monitoring, O/D Analysis, On-Street Parking (Parking Probabilities)), which are today commercialized products.

Atos Crowdsourcing Experience about the possibilities of initiatives based on crowdsourcing

Atos Insights about cities Better understanding of needs and internal process of cities in projects of mobility integration

Atos More familiarity with FIWARE

Skills acquisition about FIWARE projects

AI4BD

Better understanding of crowdsourcing projects and solutions

Better understanding of crowdsourcing projects and testing of existing solutions with crowdsourcing-enabled applications.


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InfAI

Understanding of the potentials and challenges of crowdsourcing approaches

A better understanding of the potentials and challenges of crowdsourcing approaches

InfAI

Technical knowledge in signal processing and statistical machine learning

A good overview of digital signal processing and statistical machine learning methods for analyzing sensor data

InfAI Technical knowledge in graphics

A better understanding of the potentials and challenges of applying knowledge graph embedding techniques for knowledge graph completion on spatial data

InfAI

Understanding of the needs of smart cities in terms of data integration

A better understanding of the needs of smart cities in terms of data integration and analytics to support decision making

InfAI Project development as basis of knowledge

Knowledge from software projects (DCAT-Suite, Sparql-Integrate, DL-Learner spatial extension) that can be the basis for future projects

UNITN Project skills Better understanding of the Experiment design and management with medium/big groups of people.

UNITN Knowledge about privacy issues

Better understanding of the privacy related and other bureaucratic procedures involved in doing experiments, specifically crowd-sourcing projects

UNITN Understanding of end users

Testing and scaling solutions that need to be used by end-users

UNITN Deliverables Research related results as documented elsewhere

MT Laboratory Testing innovative solutions

MT Understanding of urban mobility

Improved understanding of urban mobility for an informed policy-making process

MT Relationship with citizens

Improved citizen engagement strategies

MT Project management Better understanding of crowdsourcing projects

Soton Abilities in crowdsourcing

Greater scientific understanding of crowd sourcing mobility data

Soton Knowledge of information acquisition processes

Greater scientific understanding of hybrid human-AI data collection and validation chains

Soton Deliverables

Publications on the above are likely to follow in due course


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Inmark Knowledge about smart cities Better understanding of smart cities informational

challenges and opportunities

Inmark Relationships Acquisition and/or strenghtening of internal (project partners) and external relationships

2.2 Commercial/tangible outputs

2.2.1 Standalone components Throughout the extension of the project, a series of tangible elements have been developed and/or enhanced by the members of the consortium. As part of its open dissemination and strategy, information of QROWD’s main outputs were registed at the BDVA’s marketplace (http://marketplace.big-data-value.eu) as available services, where each element is described in a separate factsheet. Advanced road information services (TomTom) http://marketplace.big-data-value.eu/content/advanced-road-information-services

It consists on a set of functionalities that provide road information in order to help both users of the road infrastructures -commuters, tourists and transport professionals- as well as those in charge of planning and managing mobility related infrastructures and services to the citizens. Its main elements are:

▪ Touristic Network services: Analysis of travel time on strategic roads in the context of tourism.

▪ Parking services: informing drivers about available parking spots, to minimise burden of finding them while maximise the value of city mobility infrastructures.

▪ Historical Analysis Reporter: Provides traffic reports to Mobility managers, with specificities of traffic conditions in a concerned place

▪ Road Event Reporter: overview of the traffic in real time for traffic managers ▪ TomTom City: monitoring and comparison system of current traffic conditions

for city managers Modal split services (UNITN) http://marketplace.big-data-value.eu/content/modal-split-services

The QROWD Modal Split toolkit is a solution for online surveys about uses of different ways of transport. It is integrated in a mobile device, from citizen sensing using the (previously developed by the University of Trento) iLog app, which applies Machine Learning and Big Data to enhance precision and to minimise need of information from the user. QROWD Virtual City Explorer (University of Southampton) http://marketplace.big-data-value.eu/content/qrowd-virtual-city-explorer

http://marketplace.big-data-value.eu/

http://marketplace.big-data-value.eu/content/advanced-road-information-services


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It is a crowdsourced solution for mobility infrastructure maps, where crowdsource users identify city features of interest, such as bike racks, and tag the information by adding photographs and answering some basic questions. Acquired information is modelled onto The Virtual City Explorer (VCE) models the acquired information onto a virtual map. QROWDSmith crowdsourcing platform (University of Southampton) (under review for publication at BDV Marketplace)

It consists on standalone crowdsourcing platform designed to engage with crowdsourcing volunteers or workers, or citizens to run crowdsourcing activities aimed in data creation and curation for applications developed for mobility purposes. QROWDSmith platform makes use of gamification techniques such as leader boards, badges, and scores to enhance contributors’ engagement. QROWD data acquisition framework (Atos) http://marketplace.big-data-value.eu/content/qrowd-data-acquisition-framework

It is an Apache NiFi-based platform for data acquisition for Smart Cities, integrated into a FIWARE-compliant environment. The QROWD Data Acquisition Framework (QROWD DAF) is been released under Apache 2.0 license by the QROWD project and has been developed by Atos Spain in the scope of the project. It offers ways to developers to create data flows and pipelines to ingest both data-at-rest and data-in-motion from different data sources. The only constraint is that the data should be formatted according to the FIWARE data models. QROWD data integration and analytics platform (AI4BD Gmbh) http://marketplace.big-data-value.eu/content/qrowd-data-integration-and-analytics-platform

It is a solution which combines different techniques, tools and components in order to integrate data for analysis, allows the efficient management of the entire mobility/transport/logistics related data value chains: ▪ Integration of existing (heterogeneous) data sources affecting mobility ▪ Generation of new data ▪ Curation ▪ Analysis and visualisation of actionable information Analytics optimisation toolbox – AOT (InfAI) (under review for publication at BDV Marketplace)

It is a series of analytical tools for improving performance -efficiency and accuracy-in calculations required to deliver the QROWD mobility services: ▪ Crowd feedback-aware link prediction ▪ Analytics with crowd feedback ▪ Hybrid Analytics Workflow ▪ Spatio-temporal Analytics

2.2.2 Integrated framework


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Every standalone component can be combined in different configurations, in an open structure that allows additional sources of information and services for cities and citizens. The following figure is a schematic illustration of how QROWD’s elements are integrated in a full framework:

Figure 1 - QROWD framework of integrated components

A detailed description of interaction of components is available at deliverable D8.3,

“QROWD platform”.


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3.WHAT THERE’S AROUND/ WHERE WE ARE: CONTEXT

The conception and evolution of QROWD is embedded in a set of circumstances surrounding a situation and without which the project cannot be properly understood. Therefore, it is necessary to check the evolution in the last years for having a clear overview of the current situation. A comprehensive model is needed to clarify who the players to be involved are, as well as the relevant relationships and focal points or market gaps. At the beginning of the project, an analytical framework was devised in order to acquire a better understanding of the driving forces behind the development. This framework, known as “QROWD market model” was used for competitive analysis in the project’s mid-term, in May-2018, and updated in for this deliverable. It has proved to be valid to explain both the more external (environment) and the direct (demand and supply/competition), as well as the key relationships and areas of opportunity. The QROWD market model is highlighted in Figure 2 below:

Figure 2 - QROWD market model

Therefore, the main elements of the model can be divided into:

▪ Environment: external forces, shaped by structural factors summarised in a framework for analysis common to other activities and known as PEST (Political, Economic, Social and Technological factors) as well as by more specific European-wide institutional projects and initiatives

▪ Demand side of the project: focused on Smart Cities, but also including other potential users of QROWD’s outcomes

▪ Supply side: offer of solutions for Smart Cities’ needs, from single technological components to integrated services


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3.1. Competitive environment

3.1.1. PEST Prior to the beginning of QROWD project, a series of long-term forces have been shaping the landscape of European local administrations.

▪ Steady growth with risk of growing imbalances Despite a stagnation of overall population in Europe for the last decade, the numbers of inhabitants in its cities is still growing, as part of a steady trend1. There are some interlinked factors which contribute to put pressure, as well as opportunities on European cities, since do not imply only a mere growth of population but also a change in urban structure:

▪ International immigration, both from EU and non-EU countries ▪ Slow yet steady loss of the remains of population in the countryside (“rural

emptying”) ▪ Gentrification, as younger more affluent segments of population move into

inner cities, increasing property prices and transforming services and habits ▪ Disconnection centre-outskirts, as unmet communication and cohesion

needs arise

These challenges are not only exclusive of Europe, but common for cities in both rich and developing countries. However, the European case has some specific features such as limited space, a large architectural and heritage to be preserved and an ageing population, that must be kept in mind. Across the world, the idea of Smart Cities as an attempt to tackle these challenges of urban Growing urbanisation, demographic pressures. Concerns about lack of space, energy consumption and rising social inequality are especially important in the European approach; therefore, innovative, efficient and inclusive (“smart”, in short) solutions are demanded. ▪ Economic uncertainty and financial restrictions (yet with room for action): The last

years have witnessed a widespread stiffening in funding for urban development as part of the austerity measures implemented in Europe during and after the late financial crisis. These constraints are unlikely to disappear, given the worsening perspectives for the global economy in the following years. At the same time, success stories have taken place in different European cities2. Again, it seems to be opportunities for ingenious initiatives on a city-based level.

▪ Concerns about the environment: Cities represent the confluence of two major

environmental problems: degradation by the city itself (loss of natural environment, waste) and concentration of traffic pollution and CO2 emissions. Along with other fields of concern (plastic of sea, effects on global warming, and so on) environmental quality levels in cities is a pivotal point in the set of demands from stakeholders.

1 https://ec.europa.eu/knowledge4policy/foresight/topic/continuing-urbanisation/worldwide-urban-population-growth_en 2 https://smartcities-infosystem.eu/newsroom/news/2-urban-innovation-practice-success-stories%C2%A0


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The already mentioned problems of scarce space in European urban landscape, combined with growing demographic pressures, are aggravating factors.

▪ Technological factors: Technology evolution, and particularly ICT, is

undoubtedly the major transformative force in the beginning of this century. Its application into innovative, heterogeneous solutions for urban problems has the potential to become a breakthrough for urban services and policies. The following are some of the most powerful and influential drivers of technological change:

▪ IoT, as an evolution and catalyst of then previously observed yet increasingly growing effect of smartphones and their apps for every kind of purpose.

▪ HPC combined with a focus on open data approach, in projects which take advantage of exa-scale computing possibilities along with data management open source approaches.

▪ Interest in collaborative, participation citizen-centred services, as part of PaaS and SaaS business models.

▪ Concentration of technological skills and innovative ideas in urban environments, giving rise to the so-called “innovation hubs” (x, xx), leading to Rising competition among territories, regions and cities specially in high technology sectors and start-ups.

3.1.2. European institutional projects and initiatives The following facts and trends are contributing to the creation and growth of a market for smart solutions to urban mobility and transport challenges faced by Local Public Administration (PA) leaders and their city management teams. ▪ EU-led initiatives for Smart Cities: The EU, through its funding agencies and the

programmes implemented by them, play a key role in shaping the reality of urban development in Europe. Smart Cities were included as a main objective in the Europe 2020 strategy, with a set of initiatives from several EC Directorate General: DG RTD, DG Connect, DG Move, DG Energy in various programmes (from FP7 and H2020 to the European Green Cars Initiative and the Energy-Efficient Buildings Programme). Some of the most important initiatives are: ▪ Bundesverband Smart City 22, ANCI23 and RECI 24: Cities in Germany, Italy,

and Spain are organized in national SC associations. ▪ The UIA-HOPE.UIA stands for Urban Innovative Actions, an EU to provide

resources to projects for improving urban environment. The related project is HOPE (Healthy Outdoor Premises for Everyone).

▪ The CIVITAS25 initiative cities as a network of cities for cities dedicated to cleaner, better transport in Europe and beyond. Most of the 250 cities that have signed the CIVITAS Forum Declaration have taken part in at least one of the initiative's five phases to achieve a significant change in their modal split.

▪ Cities (53) that have signed the Green Digital Charter, committing to work together to deliver on the EU’s objective of expanding the use of digital technologies that improve the life of their citizens and address the challenges of growth, sustainability and resilience


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▪ Special attention must be given to Lighthouse projects (LH), where 270 million have been allocated by the EU. The participants of these project are a collective of early adopter cities and will be taken into account for market segmentation purposes

▪ Cities participating in other EU projects that focus on “smart”, whether specifically

for mobility, energy-saving purposes, or crowdsourcing. Examples of these are: BUILDSMART, CITyFiED, NEED4B, NEXT-BUILDINGS, Ready, SINFONIA.

▪ Other projects and initiatives: In addition to direct action from EU programmes,

other related initiatives have been a major force in structuring the network of Smart Cities and the myriad of R&D, social innovation and other development programs. Among some of these projects are:

▪ Cities associated to the OASC, the Open & Agile Smart Cities association21:

117 cities from 24 countries in Europe, Latin America and Asia-Pacific have officially joined the initiative. This is a city-driven, non-profit organization with the overall objective to create a Smart City market. OASC provides the network for cities to share best practices, compare results, and avoid vendor (and city) lock-in while advocating for de facto standards

▪ Connected Cities ‘RISE Research ▪ Eurocities ▪ Regional initiatives, such as City Region Deal in Scotland or the Nordic Smart

City network ▪ The International Association of Public Transport ▪ The European Innovation Partnership on Smart Cities and Communities ▪ The Mobility as a Service (MaaS) Alliance ▪ The Catapult centres

▪ Technological priorities and strategies: the European Union has been promoting

different initiatives in order to foster the adoption and improvement of several key technologies. Some of the most important are: ▪ Artificial Intelligence: its development has been supported by the EU through

programs various programs, such as AI4EU, HumaneAI or BrainMatter ▪ IoT, by means of initiatives like Create-IoT and The Alliance for Internet of

Things Innovation (AIOTI) ▪ FIWARE: consolidation of this Open Source platform, with extra focus on

solutions for Smart Cities

3.2. Demand

3.2.1. Smart Cities: consolidation and challenges The concept of Smart City, understood as the urban entity that is able to use information and communication technologies to improve infrastructures, provide better services and therefore to enhance its citizen’s quality of living, has become a widespread term and initiatives around the concept have blossomed in the last few


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years. However, the label of Smart City is not a solution by itself, and truly being one is only achieved by a thorough and complex process political will, commitment of resources and a steady effort by most city stakeholders along time. In addition, important challenges still remain ahead in the road for becoming a Smart City: ▪ Tackling problems of space shortage, overall ageing population and worsening

environment with shrinking budgets and time pressure ▪ Facing different and often opposed interest from tourists, newcomers and long-life

neighbours, whether in different or in the same city areas, and in different situations ▪ Integrating legacy IT systems and new solutions, while coping with the

opportunities of IOT ▪ Coping with often extra-sensitive political and complicated decision processes ▪ issues and at municipalities’ governing bodies ▪ Assuming complex and partially interlocked procurement processes ▪ Keeping with technological defies like data privacy, maintenance or compatibility

of standards Thanks to the QROWD project, some valuable insights have been gained in terms of a better understanding of cities’ reality and of more focused answer to these problems: ▪ Centralization “Big-Brother-like” patterns do not seem to work in such complex

environments ▪ Agile and flexible cooperation networks among cities can be an opportunity, but

can easily turn into ill-defined and time-consuming tasks with no real value ▪ While smartness is a common and -in many cases- boasted quality across cities,

in fact there are necessary abilities and lines of action (data-driven policies, technical skills, room for decision and resources to coordination of projects related to SC) which are still little internalised

▪ People participation can become a boost for smart city projects, but its effective management poses additional challenges (political, technological or term-related)

▪ Concerns about keeping independence from large suppliers ▪ Growing perception that the concept of smart city can lead to widen the gap

between larger, richer urban areas and those with less financial capabilities to face complex projects and high budgets.

Therefore, a customised approach based on consulting and project development seems suitable for meeting the cities needs and possibilities. For that end, taking advantage of IT opportunities and leveraging on previous and ongoing projects opens the way to collaborations from external partners. Two broad lines of services can be envisaged to enhance the cities’ existing IT infrastructures: ▪ Complementary services and tools or replacement of older ones ▪ Additional layers of data integration, analysis and/or visualisation


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Smart Cities identification and segmentation A Directory of Smart Cities was created at the beginning of QROWD project and maintained along project lifecycle to measure and monitor the potential demand. Main sources for identification and further segmentation of have been kept throughout, namely three: ▪ EIP-SCC20, the Marketplace of the European Innovation Partnership on Smart

Cities and Communities. The EIP-SCC is supported by the EC and brings together cities, industries, SMEs, investors, researchers and other SC actors. More than 7000 municipalities are listed, though some data are duplicated and numbers are actually lower. For the purposes of demand quantification, only cities with a minimum number of inhabitants or at least two initiatives are included in the directory

▪ Cities with recurring “Modal split” surveys to optimise their transportation infrastructure and public services. For that end, it is important not only to carry modal split surveys on a regular basis, but to show a clear will to share and to make use of the resulting data in order to improve the community of SC as well as the city itself. Therefore, data are obtained from “The Modal Split Tool” at EPOMM (European Platform on Mobility Management).

▪ OASC community (see 3.1.2., “European institutional projects and initiatives”) Its latest update comprises 2113 municipalities, up from 1936 identified at the time of release of D9.3 (May-18). This means a slight but consistent increase across countries and initiatives, showing that the concept of SC itself is already well established. The criteria adopted for this definition, and therefore the sources selected for quantification, imply that most SC are European (though not necessarily from EU-member states) or have links with European initiatives. Thus, 1813 of the cities belong to the EU.

Table 2 - Smart Cities geography

Country Number of SC % of SC (Number)

Population % of total population

Italy 326 18% 25.041.965 12%

Spain 212 12% 25.341.622 12%

Belgium 190 10% 8.747.048 4%

France 171 9% 22.715.331 11%

Germany 150 8% 28.540.545 13%

Greece 113 6% 7.076.566 3%

Poland 69 4% 13.087.392 6%

Sweden 68 4% 5.277.529 2%

United Kingdom 65 4% 25.459.879 12%

Portugal 58 3% 5.525.478 3%

Netherlands 54 3% 7.642.725 4%

Romania 52 3% 7.131.592 3%

Denmark 51 3% 5.064.093 2%


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Country Number of SC % of SC (Number)

Population % of total population

Hungary 34 2% 4.644.234 2%

Bulgaria 26 1% 3.626.444 2%

Finland 24 1% 2.873.997 1%

Lithuania 24 1% 2.844.796 1%

Ireland 22 1% 3.480.250 2%

Croatia 21 1% 1.857.371 1%

Austria 20 1% 2.971.776 1%

Czech Republic 18 1% 2.978.978 1%

Slovenia 17 1% 1.160.185 1%

Slovakia 9 0% 1.143.607 1%

Cyprus 7 0% 363.197 0%

Estonia 5 0% 538.917 0%

Latvia 5 0% 233.618 0%

Malta 1 0% 111.287 0%

Luxembourg 1 0% 6.444 0%

TOTAL 1.813 100% 215.486.866 100%

Table 3 - Smart Cities by population groups

Population (inhabitants) Number of SC %

Under 100,000 1.295 71%

100,000 - 500,000 448 25%

500,000 - 1 million 50 3%

More than 1 million 20 1%

TOTAL 1.813 100%

Given the large number of cities in the directory, it is evident the need to establish a system of segmentation in terms of interest for exploitation objectives. For that end, the key underlying assumptions were the following:

• Projects of mobility in Smart Cities are complementary to existing infrastructures and initiatives

• In case of possible tenders and requests, cities with more degree of sophistication mean higher levels of competence from other solutions (as well of updates of the existing ones). However, they also higher likeliness of launching more initiatives, as well as an opportunity for best practices through state-of-the-art solutions.

Consequently, the segmentation model seeks to identify early adopters, at different levels of sophistication. Three criteria have been used for spotting and classifying these leading cities: ▪ Participation in LightHouse projects: 93 different Urban Entities (not necessarily

whole cities, but in some cases large districts of megacities like Istambul, Turkey) and further segmentation have been kept as the timespan of QROWD, namely:

▪ Membership in OASC (114)


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▪ Recurring Modal Split (451 cities)

Figure 3 - Criteria for segmentation of Smart Cities

The concurrence of a city in one or -preferably- in more than one of these groups can be considered as a signal of advancement and management capabilities, so all of them will receive the consideration of early adopter Smart Cities, with different degrees of intensity. The possible combinations of initiatives will be the bases for prioritization and will be explained into further detail in the section 4, “Possible strategies”, of this document.

3.2.2. Other potential customers The commercial outputs of QROWD project are technical components that can be integrated in existing or new projects by other suppliers of Smart Cities. Therefore, practically every existing solution is compatible with QROWD’s independent elements, such as e.g. VCE, Qrowdsmith, iLog or the Touristic Network. Therefore, industry players already acting or willing to compete in the SC marketplace (see next section, 3.3 “Supply”). can become either distribution channels for the QROWD offering or integrators, leveraging QROWD Open Source solutions to create their own commercial services.

3.3 Supply – alternative solutions

It seems troublesome to establish a homogeneous definition and categorization of the offer for mobility solutions to Smart Cities, given the diversity, novelty and amount of the available options, many of them originally not designed specifically for this purpose. There are two drivers that help to clarify the sector, giving some light on the current situation and possible evolution of the supply: (1) Technological development with increasing heterogeneity; and (2) Rise of open data solutions, such as those based on FIWARE. The combination of these factors means more difficulty to get a clear understanding of competition and its related perils; but at the same time it means an opportunity for flexible strategies, with possible commercial rivals which for some


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projects and/or projects can turn into allies or even customers (see previous section 3.2.2, “Other potential customers”).

3.3.1. Commercial competitors A wide array of private companies provides solutions for Smart Cities, combining different types of approaches and devices. Three categories of players can be outlined, in order to simplify the analysis: ▪ IT integrators:

The most important technological companies in the world are increasingly expanding their offers to Smart Cities, based on their strong expertise and powerful resources in telecommunications infrastructures and networks, data integration, analysis capabilities, and so on. In table 4 a short list is showed with some of the most active companies in solutions for smart cities. Table 4 - Main IT integrators in the industry of services for Smart Cities

Company Country of HQs

Siemens Germany

Deutsche Telekom

Atos France

Vodafone UK

Indra Spain

LG Korea

NTT Japan

Huawei China

Oracle USA

Cisco

Microsoft

▪ Specialists:

Over the last two years, more and more firms have appeared offering different devices and specific solutions for specific problems related to smart cities. An important share of these proposals is related, totally or partially, to solutions of different kind for mobility issues. As an example, the recently held Smart City Expo (November 2019, Barcelona, Spain), counted on a category of Mobility for 87 of the 344 “solutions” showed at the event. Almost one half of the featured solutions can be classified as “devices”, such as sensors (i.e. such parking and passenger) or apps. In addition, direct “services” provided by the venders stood for almost 30% of solutions, with a few cases of SaaS (Software as a Service).


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Figure 4 - Distribution of Mobility solutions, Smart City Expo 2019

From a perspective of direct competition for QROWD, alternative offers can be found for every individual component:

▪ Generic solutions, like Wide or Waze, as competitors for VCE or Touristic Network

▪ Adaptable software, like most crowdsourcing platforms (potentially competing with Qrowdsmith) or analytical suites (Nommon’s Kineo)

▪ Specific tools, especially for Modal Split surveys, such as Smart Surveys or Sentiance (among other mobility solutions offered by this company)

Although these solutions compete with a given element of QROWD, they are generally compatible with the other components and therefore they do not necessarily mean a threat for a QROWD project with a broader approach.

▪ Platforms for mobility: An increasing amount of companies are devoting efforts to providing integration solutions. Though they focus in the approach of platform (sometimes using the term PaaS, “Platform as a Service”), it does not necessarily mean that they are specialised in ad hoc platforms for Smart Cities, but they often apply resources and expertise from other activities, such as procurement portals or ERP solutions. The following are some examples of firms with offers of platform-like solutions for smart cities:

▪ Hexagon ▪ Ubiwhere (part of Citibrain Consortium) ▪ Voilàp ▪ Paradox ▪ Nommon

This kind of companies compete mainly against the integration services of large IT companies mentioned in the previous section. At the same time, while rivalling in some

Analytics, 10%

Data gathering,

5%

Devices, 40%

Other, 3%

Platform, 6%

Programs, projects and

communities, 8%

Services-SaaS, 28%


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potential projects, they are more likely to become partners and/or customers to QROWD’s individual elements. Direct competition, by QROWD component A more detailed look into the most remarkable competitors in each solution derived from QRPWD project shows the heterogeneity of the supply for Smart Cities. Table 5 – Touristic Network

Table 6 – Modal Split

Table 7 – Virtual City Explorer


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Table 8 – Qrowdsmith

Table 9 – Data Acquisition Framework


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Table 10 – QROWD platform / Data Integration and Analytics Platform

3.3.2 Initiatives potentially competitors to QROWD As previously said, other EU-related projects can become either a direct rival bid or an opportunity for collaboration depending on factors like the degree of similarity of a given proposals or the expectations about commercial exploitability of another project. It is also expected to face possible competition not from In any case, the highest levels of concurrence, or at least coincidence, are expected in initiatives aiming to improve urban transportation from a citizen point of view, such as: ▪ LightHouse projects (https://smartcities-infosystem.eu/scc-lighthouse-projects) .

Although all these projects are of potential interest for alliances, the closest to QROWD are those directly related to mobility and citizen participation: ▪ SmarterTogether: a project which blends citizen engagement, a “Smart Data

management platform” and e-mobility solutions. Industry partners, namely Siemens, Toshiba and to a lesser extent, Spectrum Mobil

▪ Sharing Cities: a combination of citizen involvement and fostering of e-mobility (alternative to fossil fuel vehicles, like e-bikes). Siemens Group takes part in the consortium.

▪ My Smart Life: focused on fostering city participation in the cities’ decision process (“Advanced Urban Planning”). Deutsche Telekom is among its partners.

▪ Transforming Transport (www.trasnformingtransport.eu), a large EU-funded consortium of leaders in transportation, from ICT companies -Indra and TomTom among them- to carriers and infrastructures, as a means for joint innovation

▪ Organicity (www.organicity.eu), a programme launched by three cities (London,

Aarhus and Santander) for social innovation through citizen experiments

https://smartcities-infosystem.eu/scc-lighthouse-projects

http://www.trasnformingtransport.eu/

http://www.organicity.eu/


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▪ Urban Initiative Actions (https://www.uia-initiative.eu/en/uia-cities). There are five cities with projects about urban mobility: Albertslund, Ghent, Toulouse, Lahti and Szeged

▪ Projects sponsored by Finnova Foundation (http://web.finnovaregio.org/)

https://www.uia-initiative.eu/en/uia-cities

http://web.finnovaregio.org/


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4.(“WHAT WE CAN DO”): POSSIBLE STRATEGIES

4.1 Business Model

4.1.1 Independent activities (Standalone components) The QROWD partners’ individual exploitation intentions of the outputs that represent the project offering were explained in deliverable D9.3, “Exploitation Strategy” (Chapter 4). The individual exploitation plans, the knowledge as well as the enhanced QROWD components and services created by the project will be taken to the market through standalone modules and consulting services. The following section describes the updated individual partners’ exploitation vision and intended commercial paths for their exploitable results. TomTom It is important to bear in mind that TomTom’s main expected outcome for the project is to improve its capabilities for expanding its value offer, helping to apply advances like research in cognitive load , in order to simplify routes, and crowdsourcing, to its services (see deliverable D1.4, “Final TomTom pilot”). The main result of TomTom participation in QROWD is a set of functionalities known as “Advanced road information services”, intended to improve the quality of its travel services within a customized network (Urban/Region), in order to strengthen TomTom’s market position in the navigation sector The most visible result is the so-called “Touristic Network”, that is, a service that helps users to optimize decision about leisure options by a comprehensive and easy-to-understand sequence, including a schematised map. While a valuable service by itself, Touristic Network was linked to QROWD project as a part of an effort to improve mobility in the Trentino area. During the project, it was tested as to help to the user for an optimal choice of ski resorts in the surroundings of Trento. The test proved its success in terms of: ▪ Better tourist customer experience ▪ Possibility for city tourism authorities to suggest touristic hotspots to visit based on

current estimated travel time ▪ Traffic reduction, with less congestion and lower pollution The touristic network concept is not limited to the particular case of ski resorts, and possible future applications are already envisaged by TomTom (see deliverable D1.4, “Final TomTom Pilot”), such as restaurants. In addition to Touristic Network, learning from QROWD project has been applied by TomTom for the development of commercial products for MOVE portal, a traffic management tool for cities:


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Table 11 - TomTom’s main exploitable results

Traffic analysis service

Type Description Exploitation value/Value proposition

Licensing scheme and Status

Route Monitoring

Service Designed to address the growing road congestion challenges faced by Smart Cities worldwide. Route Monitoring is a service that helps traffic managers, event managers and emergency service dispatchers monitor travel times and delays on routes of interest – as they happen – and inform the public of delays and alternative routes through smartphones, in-car systems and variable message signs.

Reduces traffic congestion as citizens are better informed and the city can better control traffic.

Proprietary; Product release in November 14, 2017

O/D Analysis

Service This service provides valuable insight for urban and infrastructure planners by allowing them to see drivers’ preferred routes and trip behaviour trends, by identifying trip dynamics, and determine which areas of a city require additional infrastructure, information signage, new parking facilities or could be considered attractive for a targeted advertisement or a new retail location.

Improves drivers´ mobility experience; Improves efficiency in usage of city’s infrastructures; Better Parking facilities ROI

Proprietary; Product release in September 18, 2018

TrafficStats Service Traffic Stats provides insights into the traffic situation on the road network throughout the day. With the simplest set of inputs, users can create a customized query for a specific area or route, specific days and the time periods in the date range that are of interest. The results are computed, and a report is made available to view or download within just a few minutes. Even a complicated query containing a large area and a wide date range will never take longer than 24 hours to generate results.

Improves road safety; Reduces traffic congestion; Improves drivers´ mobility experience

Proprietary; Product improvement

Furthermore, TomTom is currently working in order to file a patent filing a patent about the concept of schematise map of Touristic Network. Nevertheless, there is not going to be a specific product directly derived from QROWD.

As for possible collaboration for projects for Smart Cities, it is necessary to bear in mind that TomTom is a global company with “all-territory approach”, rather than

https://move.tomtom.com/assets/Route%20Monitoring%20Product%20Info%20Sheet.pdf

https://move.tomtom.com/assets/Route%20Monitoring%20Product%20Info%20Sheet.pdf

https://move.tomtom.com/assets/OD%20Analysis%20Product%20Info%20Sheet.pdf

https://move.tomtom.com/assets/OD%20Analysis%20Product%20Info%20Sheet.pdf

https://move.tomtom.com/assets/Traffic%20Stats%20Product%20Info%20Sheet.pdf


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specific projects for specific area such a city or urban conurbation. Therefore, the company will take into consideration possible opportunities although not as a top priority.

Atos

The company’s main objective was the improvement in its ability to provide better services to the Public Administrations: in other words, to be able to improve its tenders and RFPs thanks to information-based extra features which increases the perceived value of its offer to municipalities and other Public Administrations. For that end, Atos has sought to work on interoperability projects in complex environments, often involving the usage of open data or information stored in data silos. In that sense, FIWARE-compliant results are a plus as they can be reused in other projects and improve its offering related to FIWARE enablers. In that sense, the previously mentioned non-commercial outputs (gaining skills in crowdsourcing, understanding of cities needs and expertise in FIWARE environments) are deeply interlinked with the tangible results of the project for Atos. Thus, the two main assets are expected to be the driving forces of exploitation paths: ▪ Dashboards (for the Municipality of Trento and for its citizens). The plan is to reuse

of the work done in these dashboards for other municipalities. To do so, the dashboards have been designed in a modular way providing standard access through REST APIs to QROWD services and open data stored in the backend. This will enable easy customization of functionalities such as modal split or reusing maps of existing city infrastructure with small customization effort.

▪ DAF (Data Acquisition Framework): it has been developed using Apache NiFi as main backend to define graphically data flows, and standard data models from FIWARE to store data in the FIWARE Context Broker. This allows the reuse of the framework for any project that performs data acquisition and interfaces with FIWARE enablers. Atos plans therefore to reuse and utilise these assets in future research projects and developments for its customers

Table 12 - Atos’ main exploitable results

Result Type Brief description/Main targeted users

Exploitation value/Value proposition

Licensing scheme

Municipality Dashboard

Service

Dashboard for City staff showing the city´s mobility data and the results of analytics.

Decision making support

Open Source

Citizen Dashboard

Service

Dashboard for citizens. It will be a subset of the Municipality Dashboard plus some extra services for citizens around mobility

Better informed citizens

Open Source


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Result Type Brief description/Main targeted users


Licensing scheme

Data Acquisition Framework

Framework

Supports city staff in the acquisition of data and associated methodology.

Improves data management

Open Source

Atos foresees three complementary internal paths for commercial exploitation: ▪ Direct actions by offers to existing customers ▪ Integrated offers through Atos’ commercial structure for Smart Cities, either globally

(general structure) or through national business managers ▪ As candidate project for being developed at “Atos Innovation ID “. The former two ways of commercialization can be materialised in in different types of schemes: ▪ Additions to existing projects ▪ Pilots ▪ New full-scale projects, through official public tenders In relation to possible collaborations with other partners, Atos is optimistic about partnerships for including tools to its Data Acquisition Framework. In particular, partnership with SOTON for VCE and QROWDSmith is perceived as relatively easy to manage, while i-Log would imply more need for coordination with UNITN and InfAI, as well as SOTON).

AI4BD

At the first stages of the project, AI4BD expected commercial results were to offer additional services to existing customers, namely: ▪ Law enforcement level: MINER offering where large amounts of documents can be

analysed and for the OGD part we see also a huge potential enabling better link discovery and disambiguation to data sets.

▪ LE and OGD: Qrowd Service, needed to build a community and train the NLP part for specific domain knowledge scenarios

AI4BD exploitation path was initially intended expected to focus on its main outcome, the QROWD platform, as seen in the 2.2.1 section, “Commercial/tangible outputs- Standalone components”

Table 13 - AI4BD’s main exploitable results

Short Name

Type Brief

description/Main targeted users


Licensing scheme


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QROWD Platform

Platform

Crowdsourcing-enabled big data integration and analytics platform for (urban) mobility managers

Allows the efficient management of the entire data value chain

Proprietary

From Ai4BD’s perspective, the QROWD platform has evolved into two different solutions: ▪ AIStudio, as a direct commercial version from the QROWD platform. It consists on

a crowdsourcing-enabled integration platform, which is currently integrated with all AI4BD products and able to integrate with partner products. AIStudio application can be integrated into a joint commercial offer as a crowdsourcing-enabled integration platform.

▪ Cognitive Business Robotics (CBR) Coworker: a separate solution based on similar

ideas and technological evolution from QROWD platform. The CBR Coworker is a fully automated AI engine for human resources clerical tasks, working as an layer on the companies’ ERP system.

For the next months, AI4BD has three lines of commercial development in mind:

▪ Individual contacts with mayors from cities in the company’s natural markets: Switzerland, Germany and Turkey. These contacts We have the contacts to some cities (mayors in Turkey and Germany, Switzerland). Apart from commercialising its own products, AI4BD would be willing to lead joint offers including other solutions derived from QROWD

▪ Launching the Executive Briefing Seminar, a virtual meeting for executives in

order to understand the value of AI and CBR Coworker

▪ Possible extension of the CBR Coworker Solution Proposal for Smart Cities together with other QROWD partners (especially TomTom and ATOS)

InfAI

From the beginning of QROWD, InfAI set non-commercial goals for the project in terms of knowledge and skill acquisition, in order to be applied in future projects related to crowdsourcing-enhanced technologies. In that sense, the main tangible (though not strictly commercial) of InfAI’s outputs obtained thanks to its involvement in the project are the following developments: ▪ DCAT-Suite: An integrated tool for retrieving, publishing and loading data

described by means of the Data Catalog Vocabulary (DCAT) ▪ Sparql-Integrate: A software tool to integrate heterogeneous data by means of

transformation plans expressed in standard SPARQL syntax and function


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extensions ▪ DL-Learner spatial extension: An extension of the supervised machine learning

framework 'DL-Learner' which allows to do OWL reasoning taking spatial relations (as for example those expressed in the Region Connection Calculus) into account, and which extends the current concept learning methods with the option to include spatial relations into OWL class expressions that are not explicitly stored in the data but can be inferred by the aforementioned spatial OWL reasoner

For those projects scientific publications are planned or already on the way. In addition, these software projects may be the basis for future research grant proposals. Moreover, the results of the project, materialised in the “Analytics optimization toolbox”, provide an opportunity to similar but increasingly more sophisticated projects, giving value added to potential customers while strengthening InfAI’s field of expertise: ▪ More advanced versions of InfAI’s Semantic Web tools, leading to higher efficiency

and quality in each single phase, improving the usage of Linked Data during its entire lifecycle.

▪ Future work in Big Data resp. Smart Data oriented projects, even helping InfAI to become a specialist for crowd-driven Big Data technologies.

Table 14 - InfAI’s main exploitable results

Short Name

Type Brief



Licensing scheme

Crowd Feedback-aware data integration

Tool

Link prediction engine for city staff, capable of iterative improvement of generated datasets using data quality assessment from the crowd

Supports data integration tasks, e.g. for completing the mobility infrastructure data, using data quality assessment from the crowd Open Source

Analytics with Crowd Feedback

Tool

In case a machine learning/analytics algorithm only performs poorly (e.g. due to insufficient training data) crowd/expert feedback is introduced by either city staff or citizens to improve the learning results.

Improve the performance of the analytics required to deliver the QROWD mobility services

Open Source

Spatio-temporal Analytics

Tool

Allows city staff to do analytics based on spatial and temporal relations like 'inside', 'near', 'after', 'before' and so on.

Improve the performance of the analytics required to deliver the QROWD mobility services

Open Source


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These tools can be deployed separately as improvements of existing information systems in cities or in larger projects in combination with other tools. In those projects, InfAI would act as a consultant providing ad hoc analytics services. Most likely projects would take place in implementation of i-Log, most likely in combination with UNITN, SOTON and Atos.

University of Trento UNITN will exploit results from the QROWD platform, most notably the QROWDDB and i-Log, which will provide the basis for technological solutions in ubiquitous computing and people-centric urban sensing scenarios. Plans for extension of i-Log to other municipalities must take into account the necessary efforts for re-engineering in case of significantly higher populations. On the other hand, of running the current research prototype version of i-Log currently can be kept at very affordable costs (i.e., variable costs for surveys of €5 per person per week, so an experiment with 100 people that goes on for 2 weeks would cost €1000). A feasible exploitation plan might target customer and users outside of the general Smart Cities sector, focusing on public and industry stakeholders. Some of the uses under study for the extension of i-Log are as follows: ▪ Wellness and Fitness: measuring of food intake, sleep patterns, activity levels for

researchers. A first pilot study based on food intake was successfully carried out and the results are now being analysed.

▪ Pollution: based on movement patterns and on detection of method of locomotion, estimate what is the daily carbon footprint of each participant. Currently under development a pilot on this topic its expected next year.

▪ Marketing: gauge the use of different services and resources using the app as part of market research. This possibility is still under consideration, as there are specific challenges (like GDPR) that need to be accounted for.

Table 15 - UNITN’s main exploitable results

Short Name

Type Brief description/Main targeted users


Licensing scheme

iLog

Mobile app and analytics back-end

Mobile application to (i) Collect sensor data from engaged citizens in both active and passive way (ii) Put citizens ""in-the-loop" for validation and curation of their own data (or collectively generated by them)

Allows City staff to get data and feedback from citizens about their mobility patterns; Enables /improves citizens engagement in data driven decision making

Proprietary


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Modal Split

Service

Allows CMM to run travel surveys using citizen's mobile phones instead of cumbersome CATI or paper surveys. (Requires i-Log)

Disruptive change in traffic management. Allows shifting from expensive, limited, years old static data to up-to -date cost-efficient data driven decision making, simulations, what-if analysis and policy formulation

Proprietary

Municipality of Trento

For the city of Trento, results from QROWD are assessed as an experience with lesson learned for mobility & transport problems, especially in: ▪ Improvement of the policy-making process and QROWD offers a pioneer solution

for the collection of data concerning movements in the city of Trento, binding together crowdsensing, gamification, big data, ML techniques, analytics and visualization tools.

▪ Knowledge extension, through a database with reference to location and availability of specific mobility infrastructure: bike racks and parking spots for: disabled people, freight load/unload, motorbikes…

Further development of iLog is under consideration, taking into account that the costs of running the service might exceed the initial evaluation of €30.000 (see deliverable D2.5, “Final Trento Pilot”), but it brings important benefits thanks to a of more frequent and faster survey system. There are not specific exploitation perspectives for in commercial terms, but the Municipality is open to other collaboration initiatives beyond the scope of QROWD project. University of Southampton

SOTON’s expected exploitation plans take into account its main outcomes form QROWD as a set of Open-source tools: ▪ Virtual City Explorer (VCE), a crowdsourcing task for collecting city data from street-

level photography ▪ QROWDSmith, a platform that aims to simplify the setup and deployment of crowd

tasks, such as the VCE ▪ Modal-Split User Interface (MSUI), a reusable user interface component for viewing

and modifying multi-modal journey data ▪ Some tools associated with the MSUI, most notably for comparing inferred and

corrected trip data. Therefore, the University aims to take advantage of this results in several ways: ▪ Through further projects: more specifically, VCE and QROWDSmith will be

exploited via the H2020 ACTION project (grant agreement number 824603) ▪ Commercial exploitation: it will be possible an exploitation of the developed open-

source tools will in the way of licensing and consultancy agreements with the University of Southampton (the IP holder). The University is able to provide services


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to: ▪ Customise or further develop the published software ▪ Install and/or host the published software on behalf of another organisation ▪ Advise and provide design services to support the deployment of the software

and/or execution of studies using it

Table 5 - SOTON’s main exploitable results

Short Name Type Brief



Licensing scheme

Virtual City Explorer

Tool

Offers city staff a way to generate maps of points or items of interest in a city that can be found in their Google Street View virtual representation.

It solves the problem of completing mobility infrastructure records at a fraction of the cost of sending Municipality workers to do surveying/inventories/counting.

Open Source

QROWDSmith Crowdsourcing platform

Allows city staff to define and run crowdsourcing tasks and citizen engagement challenges with gamification.

Dramatic improvement in engaging citizens and data contributors to solve city issues

Open Source

Modal-Split User Interface

Tool

User interface component for viewing and modifying multi-modal journey data

Reusable; includes tools associated for comparing inferred and corrected trip data. Open source

▪ Consultancy and design services to support crowdsourcing or citizen-sensing of transport or infrastructure data, to exploit the expertise gained through the QROWD project. This could include running stakeholder workshops to assist with project planning, design and execution.

Finally, in relation to possibilities for joint exploitation activities with other partners, SOTON sees room for agreements Ad-hoc or standing agreements to provide support or expertise, which would be negotiated between the University and other partners on request. The Apache 2 license that VCE, QrowdSmith and MSUI have been licensed under are permissive but – if necessary – the University could license those outputs under alternative conditions on request.


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Inmark The short-term priority is to collaborate with QROWD partners to engage early adopters in the EU, helping the other members of the consortium to develop commercial relationships in their own priority segments. The final goal for Inmark is to reinforce its offer in consulting services for municipalities and other Public Administration thanks to improved solutions for mobility, at the same time as improving its ability to provide valuable insights through research in market positioning and customer experience.

The following table gives an overview of the different exploitation paths to be followed by QROWD’s partners:

Table 6 - Summary of QROWD partners’ exploitation paths

Main objective Intended Usage

Link to the current activities

What type of results/ for which purpose

Tom Tom

To enhance the quality of its travel services within a customized network (Urban/Region), to further strengthen TomTom’s market position in the navigation sector

Commercial Service extension, Product improvement

Service portfolio

ATOS To improve the extra value of its offer to municipalities and other PAs through information-based services

Commercial in the scope of projects with customers and Innovation projects

Open data and big data offering, Smart Cities focus

Service portfolio

AI4BD Additional services to existing customers: AIStudio (commercial platform evolved from QROWD); MINER offering for law enforcement; LE and OGD

Commercial Product improvement

Service portfolio

InfAI Future work in Big Data responsive Smart Data oriented projects

Innovation projects

Service offer extension

Service portfolio

UniTN Use of results for technological solutions in ubiquitous computing and people-centric urban sensing scenarios, in other sectors outside the Smart Cities

Fitting needs of the organization


Platform extension


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SOTON To acquire knowledge and skills in fields related to QROWD: combination of AI+human participation, citizen-centered projects

Innovation projects


Service portfolio

MT Technological+ citizen-based initiatives while keeping the use of current, improved solutions (mainly VCE)

Fitting needs of the organization


Piloted services to extend the service portfolio

INMARK To strengthen its portfolio of consulting services for PAs trough mobility-based solutions

Commercial Service offer extension

Service portfolio

In overall, these individual exploitation paths are the consequence of having achieved the project’s objectives (see section 1, “Introduction: “What We Wanted To Do” (Objectives Of The Project”) and therefore they meet the expected impacts which were established at the beginning of QROWD project:

1. Better, simpler data analytics for a more efficient development of products and services

2. More Datasets, both within the consortium activities and external ones using open data technology

3. Service extension, in terms of increase of competitive services offered by the members of the consortium

4. For industrial partners (TomTom, Atos, Ai4BD, Inmark), a projected increase of revenue in directly related activities of more than 20% at the end of 2020

4.1.2. Integrated approach Collaboration between partners Given the complementarity and openness of both QROWD’s overall approach and its individual elements, a joint commercial offer can be envisaged as a made-to-measure set of solutions for Smart Cities and, to a lesser extent, to other players in the field of urban mobility services. In any case, QROWD must not be understood as a plain product, but as a concept that serves as inspiration for a portfolio of services through the collaboration of the consortium’s partners. Therefore, the integrated approach shown in Section 2.2.2 can be divided into more specific configurations through the combination of just two or three components. In that sense, the concept of “QROWD platform”, as described in homonymous deliverable D8.3 is commercially valid although technically there is not compulsory to use the platform component. The following table summarizes the set of possible joint offers:


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Table 7 - Possible collaborations among partners

TomTom

Atos

AI4BD Integration by AI4BD of other solutions

InfAI Modal Split Services

Integration by AI4BD of other solutions

UNITN

MT iLog services

SOTON VCE & Qrowdsmith; Modal Split

Inmark Development of exploitation projects

Development of exploitation projects



TomTom Atos AI4BD InfAI UNITN MT SOTON Inmark

▪ Atos / SOTON: Citizen-centric tools (Virtual City Explorer and Qrowdsmith)

Atos would access to open source developments with the support of SOTON, leading commercialization and implementation initiatives

▪ Atos / (UNITN + InfAI + SOTON): Modal Split solutions based on iLog. Collaboration is foreseen as more difficult than in Citizen-centric tool, as coordination needs are higher

▪ AI4BD / TomTom: Integration in AI4BD offer (AIStudio, CBR Coworker) of Advanced Road Information Services /MOVE Portal Ai4BD would offer MOVE to cities as an upselling option to its own services.

▪ AI4BD / Atos: Integration in AI4BD offer (AIStudio, CBR Coworker) of Data Acquisition Framework and dashboards DAF and dashboards would be additional features to AI4BD’s solutions (predictably AISTudio) for cities

▪ AIBD / (UNITN + MT + SOTON): Integration in AI4BD offer (AIStudio, CBR Coworker) of i-Log As in the previous cases, it would be an offer extension to AI4BD’s own services

▪ UNITN / MT: maintenance and development of i-Log ▪ Inmark / Atos: development of exploitation projects

Inmark can help Atos in activities such as market research and commercial support

▪ Inmark / Ai4BD: development of exploitation projects A collaboration similar to Atos can be arranged, more focused in markets other than Ai4BD’s area of influence (Switzerland, Germany and Turkey)

▪ Inmark / UNITN: development of exploitation projects When interest about Modal Split is detected

▪ Inmark / SOTON: development of exploitation projects


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If opportunities/interest for citizen-centered services (VCE, Qrowdsmith) are noticed

Towards a joint offer Although the commercial output of QROWD is not a single service but a combination of possible offers, there are shared features in the value proposition. Therefore, it is interesting to define a common approach to reinforce the coherence and attractive of projects derived from QROWD. For that purpose, an analytical tool known as the Lean Model Canvas is used. It describes the value offer from a perspective of answering unmet needs of the customer segments (right part of the canvas) thanks to “solutions” which cope with specific problems (left side of the canvas).

Figure 5 - QROWD offering lean model canvas

▪ Customer segments Main targets: Advanced Smart Cities (Early adopters) A segmentation criterium followed across the project lifespan has already been presented in section 3.2.1 – “Demand-Smart Cities”. Cities that are members of the Open and Agile Smart Cities initiative, participate in any of the Lighthouse projects and have at some point in time performed a Modal Split analysis are the first and foremost recipients of our efforts (Priority 1). A second group of SC (Priority 2) includes the remaining LH cities, plus those in OASC and with modal Split. In a quite similar way to the previous group, the other cities members of OASC (but


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without neither Modal Split nor participation in LH projects) lie in Priority 2b. One half of this subsegment of cities (34 out or 68) are not European. Priority 3 group of Smart Cities comprises those that have only modal split survey systems (355 cities). Finally, the cities from the that participate in one “smart” project are considered Priority (1551 remaining cities). Figure 6 and Table 8 depict the segmentation model and its results. Cities included in Priorities 1 & 2 are presented in Annex 2.

Figure 6 - Smart Cities priority groups

Table 8 - Numbers of SC by priority group

TOTAL EU NON-EU

PRIORITY 1 OASC + Lighthouse + Modal Split 21 19 2

PRIORITY 2-a 120 107 13

Lighthouse + OASC (No Modal Split) 5 5 0

Lighthouse + Modal Split (No OASC) 30 29 1

Rest Lighthouse 37 28 9

OASC + Modal Split (no Lighthouse) 48 45 3

PRIORITY 2-b Rest OASC 68 34 34

PRIORITY 3 (Groups 3 + 4) 2044 1489 555

(3) Rest Modal Split 355 22 333

(4) Rest SC 1689 1467 222

In addition to these segments of municipalities, additional potential customers are expected to be found among other suppliers of solutions for cities and PAs. Existing providers of individual solutions will be identified at specific city level, giving room for the eventual collaboration in other projects.


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▪ Problems: A set of main common pains have been found in almost every local authority contacted along the project’s timespan:

▪ Increasing traffic and pollution problems ▪ Financial constraints ▪ Uncertainty about technological evolution ▪ Lack of citizen commitment

Though these problems are far from being new, current. Actually, there is some degree of agreement about additional pressure due to expectations about how technology is supposed to solve them. In that sense, an additional set of challenges and have arisen, which imply extra needs:

Table 9 - Smart Cities’ main challenges and needs

Challenges Needs

Slow progress in initiatives Fast, easy-to-implement projects, shortening time to action

Fear of not keeping up with the pace Incremental, feasible actions

Lack of room for comprehensive programs

Quick wins to build support and confidence

Political conflicts because of different approaches

Public involvement and building of consensus

Inadequate response by citizen Appealing initiatives

Citizen concerns about privacy and ethics in data

Strict control and privacy management

▪ Solutions From a demand point of view, answers to mentioned problems can be considered as main guidelines of a joint commercial offer.

- Problem: Increasing traffic and pollution problems - Solution: services to enhance the citizens’ optimization of mobility options, by

integrated, online information

- Problem: Financial constraints and lack of expenditure control - Solution: Scalable projects employing open source technology with well-

defined cost estimations


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- Problem: Uncertainty about technological evolution - Solution: cutting edge technological services while compatible with further

developments

- Problem: Lack of citizen commitment - Solution: Collaborative approach, with people involvement in all stages of

service implementation (Awareness, co-design, crowdsourcing, feedback) Additionally, the solution must be totally compliant in terms of privacy and data safety. ▪ Key Metrics Performance indicators can be established at project level, in terms of metrics which help to measure the positive impact on a Smart City. KPIs are set around three vectors: ▪ Number of end users: related to the city’s size of different segments. Several

specific indicators can be set, such as participants per initiative, users per month, number of interactions per participant, etc.

▪ Time reduction: in mobility activities, mostly travels (by private and public vehicles) and parking,

▪ Cost saving: as a result of a decrease of fuel consumption, with an additional positive impact in pollution reduction.

4.2 Marketing Strategy

4.2.1 Market approach The marketing strategy develops the value proposition through tangible actions across customer segments. Therefore, it proposes different sales initiatives by differentiated channels, and it takes into account specific issues of Intellectual Property Rights in the commercialization of the different components of QROWD. ▪ Value proposition The commercial offering is based on an incremental approach across three possible lines of services, or by three main vectors. Each one is led by one partner: TomTom, Atos and AI4BD, and evolves from own company’s service (“initial offer”) to the most related services (“First extension”).


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Table 210 - Summary of combined offers

Initial Offer 1st extension Additional services /2nd

extension

Solution Provided/

led by Solution

Provided by

Solution Provided by

MOVE portal

TomTom

Data Acquisition Framework (DAF)

Atos

Citizen-centric tools

SOTON

Modal Split kit

UNITN + SOTON + InfAI

AI Studio/ QROWD Platform

AI4BD

DAF Atos


SOTON

Modal Split kit



AI4BD

Modal Split kit



SOTON


AI4BD


AI4BD

MOVE TomTom DAF Atos

DAF Atos


SOTON

Modal Split kit


In every possible path, there a series of common advantages to the different proposals:

▪ Scalable, non-exclusive The incrementalistic approach of the three proposed vectors of development Incrementalism is a suitable answer for some of the cities’ main problems in this kind of projects: financial constraints, need for quick wins, fear of technology obsolescence, etc.

▪ Citizen-centric Another key feature of the offers is in terms of benefits for the end user, whether if they are tools that interact directly with the user (TomTom suite, dashboards, VCE+ QROWDSmith, Modal Split kit) or if they gather and integrate information in order to optimize mobility options (DAF, AIStudio for cities) integrate provide

▪ AI + crowdsourcing QROWD’s most distinctive trait is the combination of Artificial Intelligence with


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ongoing validation and refinement by citizens. This approach, developed and refined through the lifespan of the project, is inherited and applied in the different commercial offers.

Advantage The resulting commercial offers benefit from a set of shared strong points, as a result of the combination of individual partners’ assets and of the outputs obtained through collaboration in QROWD project. ▪ Power of brand image associated to the offers, specially from main industrial

partners of QROWD: TomTom and Atos ▪ Existing relationships with municipalities, both at partner and at consortium level. ▪ Experience in previous projects. Apart from individual projects from partners, the

use case of Trento is an excellent model for other smart cities

4.2.2. Entry Direct relationship with potential customers is meant to be the most important sales channel to access the market, combining efforts in:

▪ Direct sales of initial offers by solution leaders, according to each partner’s individual exploitation path.

▪ Cross-selling

Commercial initiatives must bear in mind the complexity and heterogeneity of potential customer sand their projects, as well as the specific circumstances of each partner. Therefore, flexibility is a key factor, and the pace of commercialization efforts must be adjusted by each route leader.

4.2.3. Sales Sales activities and related tasks, such as contacts and invoicing procedures, will be managed by route leaders and will take place according to the nature of each project and the specific terms of partnership agreements in case of collaboration with other members. Sources of revenues are foreseen in different kinds of activities and services, most presumedly in:

▪ Implementation projects ▪ Licenses ▪ Consultancy and research services

Some specific KPIs can be calculated for commercial activities and project management in order to measure its degree of success:

▪ Number of proposals presented; estimated budget with breakdown by


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solution/partner ▪ No of projects signed ▪ No of solutions implemented ▪ Accumulated Budget (€) approved/implemented ▪ Number of end users

4.2.4. Costs In the same way that revenues, costs of QROWD-derived services must be reckoned on a partner and project basis. The main components of expected expenses are the following: ▪ Technical infrastructure ▪ Sales activities ▪ Development, maintenance and consulting hours

4.2.5. IPR

The intellectual property rights of each solution must be considered in the implementation of projects (see Table 11). Therefore, bilateral agreements and contracts with final customers (PAs)

Table 22 - Summary of IP Rights

Short Name IP Owner Licensing scheme

Touristic Network services TOMTOM Proprietary

On-Street parking TOMTOM Proprietary

Historical Analysis Reporter TOMTOM Proprietary

Road Event Reporter TOMTOM Proprietary

TomTom City TOMTOM Proprietary

Data Acquisition Framework ATOS Open Source

Municipality Dashboard ATOS Open Source

Citizen Dashboard ATOS Open Source

QROWD Platform AI4BD Proprietary

Crowd Feedback-aware data integration (*) INFAI Open Source

Analytics with Crowd Feedback INFAI Open Source

Spatio-temporal Analytics INFAI Open Source

iLog UNITN Proprietary

Modal Split UNITN Proprietary

Virtual City Explorer SOTON Open Source

QROWDSmith SOTON Open Source

Modal-Split User Interface SOTON Open Source


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5.CONCLUSIONS

In this deliverable we have presented the results of QROWD project, in terms of both non-commercial (i.e., knowledge acquisition and reinforcement of relationships) and commercial outputs. A series of solutions have been developed thanks to the project, that are already being offered to advanced Smart Cities, as well as other Public Administration; an additional update of market research has been carried out in order to confirm growing demand and interest about mobility solutions for SCs. Individual exploitation paths are envisaged, together with a set of collaboration agreements under a common approach of providing value to cities thanks to service adaptation and understanding of needs acquired through QROWD project.


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6.ANNEXES

Annex 1 Priority 1& 2Smart Cities

Priority 1 Smart Cities

City Country Population

Vienna Austria 1.731.236

Helsinki Finland 622.240

Oulu Finland 185.961

Tampere Finland 213.217

Bordeaux France 720.000

Miskolc Hungary 161.265

Cork Ireland 269.972

Limerick Ireland 195.175

Eindhoven Netherlands 210.333

Rotterdam Netherlands 600.000

Utrecht Netherlands 334.295

Stavanger Norway 130.754

Trondheim Norway 185.000

Gdańsk Poland 460.354

Lisboa Portugal 479.884

Porto Portugal 238.954

Valencia Spain 810.064

Bristol UK 441.300

Glasgow UK 612.000

London UK 7.800.000

Manchester UK 520.000

Priority 2 Smart Cities


Graz Austria 269.997

Linz Austria 204.846

Salzburg Austria 153.377

Antwerpen Belgium 512.000

Bruxelles Belgium 1.048.491

Gent Belgium 247.000

Leuven Belgium 95.463

Ostend Belgium 70.994

Seraing Belgium 63.813

Asenovgrad Bulgaria 59.953

Burgas Bulgaria 226.000

Smolyan Bulgaria 40.941

Sofia Bulgaria 1.378.000

Varna Bulgaria 356.481


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Vidin Bulgaria 48.071

Tianjin China 13.245.000

Kaposvár Croatia 63.742

Rijeka Croatia 144.043

Brno Czech Republic 377.973

Litoměřice Czech Republic 24.101

Písek Czech Republic 29.800

Praha Czech Republic 1.246.780

Aarhus Denmark 336.411

Copenhagen Denmark 777.218

Sønderborg Denmark 76.400

Tartu Estonia 97.666

Voru Estonia 12.367

Espoo Finland 243.900

Kerava Finland 35.215

Turku Finland 177.504

Vaasa Finland 66.988

Vantaa Finland 197.636

Amiens France 132.727

Lyon France 1.300.000

Nantes France 580.502

Nice France 520.000

Dresden Germany 548.800

Essen Germany 573.115

Hamburg Germany 1.814.597

Heidelberg Germany 144.948

Köln Germany 1.019.300

Leipzig Germany 560.472

München Germany 1.464.962

Paderborn Germany 150.580

Alexandroupolis Greece 72.750

Kozani Greece 71.388

Trikala Greece 51.862

Dublin Ireland 506.211

Galway Ireland 75.529

Herzliya Israel 93.116

Bassano del Grappa Italy 42.947

Cagliari Italy 157.000

Firenze Italy 370.051

Genova Italy 661.887

Lecce Italy 95.520

Messina Italy 246.000

Milano Italy 1.300.000


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Palermo Italy 656.000

Parma Italy 186.000

Trento Italy 117.285

Venezia Italy 271.000

Yokohama Japan 3.748.482

Skopje Macedonia 600.000

La Valetta Malta 6.444

Almere Netherlands 193.156

Amersfoort Netherlands 146.592

Amsterdam Netherlands 731.289

Enschede Netherlands 158.553

Groningen Netherlands 202.250

Bodø Norway 51.558

Fredrikstad Norway 80.207

Bydgoszcz Poland 363.020

Lublin Poland 339.682

Poznań Poland 538.633

Warszawa Poland 1.680.000

Águeda Portugal 49.456

Fundão Portugal 29.213

Palmela Portugal 62.820

Alba Iulia Romania 66.369

Cluj-Napoca Romania 318.000

Focasni Romania 79.315

Suceava Romania 465.327

Poprad Slovakia 51.304

Koper Slovenia 25.319

Alicante Spain 334.678

Barcelona Spain 1.628.090

León Spain 129.552

Málaga Spain 568.305

Murcia Spain 439.889

Palencia Spain 81.198

Pamplona Spain 198.000

Sabadell Spain 207.338

San Sebastián - Donostia Spain 183.308

Santa Cruz de Tenerife Spain 221.000

Santander Spain 182.700

Santiago de Compostela Spain 95.671

Sestao Spain 27.841

Sevilla Spain 703.206

Valladolid Spain 325.000

Vitoria - Gasteiz Spain 239.361


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Göteborg Sweden 506.100

Örebro Sweden 140.000

Stockholm Sweden 829.417

Umeå Sweden 117.500

Genève Switzerland 191.360

Lausanne Switzerland 130.000

Antalya Turkey 2.043.432

Kadiköy Turkey 521.005

Nilüfer Turkey 350.000

Tepebasi Turkey 315.000

Aberdeen UK 213.810

Derry UK 150.140

Dundee UK 148.260

Edinburgh UK 486.120

Ipswich UK 147.522

Leeds UK 751.500

Milton Keynes UK 241.500

Nottingham UK 278.700

Stoke-on-Trent (Staffordshire) UK 249.008

Kiev Ukraine 2.935.239

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