Towards a Technological Platform for Transparent and Flexible

Assessment of Smart Cities

Dessislava Petrova-Antonova, Sylvia Ilieva and Irena Pavlova

Department of Software Engineering, Sofia University, 125 Tsarigradsko shose Blvd., Sofia, Bulgaria

Keywords: Assessment Platform, Performance Indicators, Smart Cities.

Abstract: The concept of smart cities is widely accepted as a powerful tool to improve living standards in all city

dimensions. Smart cities aim to provide better quality services in the field of health, transport, energy and

education in order to increase the comfort of their citizens. Whether in the planning or implementation phase,

a key success factor for building smart cities is measuring the productivity of the decisions and obtaining an

assessment of the final results. Most cities perceive the smart city concept, many of them are working on

strategies for its implementation and more and more of them take concrete actions for deployment of “smart”

solutions. Two questions arise from this: “What are the challenges to become a smart city?” and “What the

city undertakes to become smart?”. Their answers required assessment of the of city’s “smart services” and

the social effect of deployment of “smart solutions” during the transformation from “smart” plan to “smart”

process. In such a context, this paper proposes an architecture of technological platforms for assessment of

city’s “smartness”. Its primary goal is to provide a transparent and flexible indicator framework that supports

quantitative progress evaluation of smart city strategy implementation, feedback on efficiency of current

policies, timely and informed decision making and increased understanding of future city challenges. The

main building components of the platform, namely repository, web APIs and web user interface, are described.

Additionally, a classification schema of indicators covering six main thematic areas is proposed.

1 INTRODUCTION

European cities are forerunners in the transition

towards a low carbon and resource efficient economy.

A fast-growing percentage (currently 72%) of the EU

population lives in urban areas, using 70% of energy.

Quality of city life and the attractiveness of cities as

environments for learning, innovation, doing

business and job creation are now key parameters for

success in the global competition for talent, growth

and investments. Key challenges for realizing the

vision of “Smart and Sustainable Cities” are to

provide solutions to significantly increase cities'

overall energy and resource efficiency through

actions addressing the building stock, energy

systems, mobility, climate change, water and air

quality. Such actions should bring profound

economic, social and environmental impacts,

resulting in a better quality of life (including health

and social cohesion), competitiveness, jobs and

growth.

EC defines Smart Cities as places where the

traditional networks and services are made more

efficient with the use of digital and

telecommunication technologies, for the benefit of

their inhabitants and businesses (EC, 2013). In Smart

Cities, digital technologies translate into better public

services for citizens, better use of resources and less

impact on the environment. Big Data has become

crucial for fulfilling the vision of smart cities - sharing

information is the key enabler in the transition of a

city becoming smart (Gulisano, 2004). The ability to

harness real-time, highly granular data across a wide

range of city operations and services is changing the

way citizens manage and experience the urban

environment. For this reason, the benefits offered by

Big Data are a key element of many smart city

strategies.

Availability of data and the access to data sources

in cities are paramount. There is a broad range of data

types and data sources: structured and unstructured

data, multi-lingual data sources, data generated from

machines and sensors, data-at-rest and data-in-

motion. Value is created by acquiring and combining

data from different sources and providing access to it

with low latency while ensuring data integrity and

preserving privacy. Pre-processing, validating,

augmenting data and ensuring data integrity and

374

Petrova-Antonova, D., Ilieva, S. and Pavlova, I.

Towards a Technological Platform for Transparent and Flexible Assessment of Smart Cities.

DOI: 10.5220/0007230203740381

In Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2018) - Volume 1: KDIR, pages 374-381

ISBN: 978-989-758-330-8

accuracy also add value. At the same time, this

heterogeneity of sources and types creates a number

of challenges associated with Big Data use in a Smart

City such as volume, velocity, variety, veracity and

value.

Powerful data techniques are needed, to allow

collecting, storing, analyzing, processing, and

visualizing vast amounts of city related data.

Handling highly variable and real-time datasets

requires new tools and methods, such as powerful

processors, software and algorithms, that go beyond

traditional "data mining" tools designed to handle

mainly low-variety, small scale and static datasets,

often manually. Key aspects such as real-time

analytics, low latency and scalability in processing

data, new and rich user interfaces, interacting with

and linking data, information and content, all have to

be advanced to open up new opportunities and to

sustain or develop competitive advantages.

Interoperability of data sets and data-driven solutions,

as well as agreed approaches are essential for a wide

adoption within and across city authorities and

citizens.

A great tool for policy making, decision support

and performance assessment in fields such as

environment, economic, mobility, are indicators and

composite indexes. The indicators allow better

understanding of smart city challenges by

stakeholders and highlight the effective policies, best

practices and reasonable decisions. The composite

indexes can be unambiguously undestanded by the

policy makers and easily communicated to the

general public (Bohringer, 2007). Both indicators and

composite indexes should be developed with a clear

vision of how they interact with each other, otherwise

the policy decisions could decrease the opportunities

for long-term sustainability (Mayer, 2008).

All these demand rethinking technologies around

smart city solutions and bring the main objective of

project “Big Data Innovative Solutions for Smart

Cities” (Big4Smart, 2018), funding by the National

Scientific fund of Ministry of Education and Science

in Bulgaria. The primary goal of Big4Smart project is

to develop methodology, implemented by an open

technological platform, that support making informed

and timely decisions on big data for building smart

cities. This paper proposes an architecture of the

technological platform for Big4Smart project that

provides a transparent and flexible performance

assessment of smart cities through a range of

indicators covering all city aspects such as living,

people, transport, etc. The indicators give an insight

into the extent to which the city is becoming

“smarter” and outline the driving factors for

sustainable development.

The purpose of the indicators directly influences

their selection. Since they are used for assessment of

cities’ performance and to inform policy at the city

level, it is important to define them in national

context, taking into account the national conditions

and priorities. In addition, the availability of data

sources is a critical issue for successful calculation of

indicators’ values. The required data is provided

primary at national level by variety of institutions

such as national statistical offices, ministries and

government agencies, non-government

organizations, etc. Thus, although the Big4Smart

methodology aims to provide a smart city evaluation

concept in general, its underlying technological

platform should be developed in national scope,

namely taking into account the Bulgarian context.

The rest of the paper is organized as follows. The

current state of the research on the problem area is

described in Section 2. The architecture of the

Big4Smart platform is described in Section 3. Section

4 is devoted on indicator classification schema,

adopted to the Big4Smart platform. Conclusions and

directions for future work are outlined in Section 5.

2 STATE-OF-THE-ART

Several indicator frameworks related to performance

evaluation of smart cities are developed within

European Framework programs. Their main

drawbacks could be summarized as follows:

 Covering a specific city sector such as healthcare,

education, industry, etc.;

 Assessment of current performance state without

any insight into progress to "smartness".

To the best of our knowledge there is no indicator

framework for evaluation of smart city performance

in Bulgaria. In addition, the proposed Big4Smart

platform aims to assess the progress of cities by

covering variety smart city dimensions in six thematic

areas: smart mobility, smart nature, smart living,

smart people, smart economy and smart government,

described further in Section 4.

2.1 State-of-the-Art at European Level

There are a lot of undergoing FP7 and Horizon 2020

projects and research initiatives related both to Big

Data and Smart Cities. Table 1 lists several ones very

relevant to Big4Smart research. It is advisable to keep

all the given values.

Towards a Technological Platform for Transparent and Flexible Assessment of Smart Cities

375

Table 1: State-of-the-Art at European level.

EIP-SCC Market Place

Description: The EIP-SCC Market Place is an initiative

supported by the European Commission that aims to

develop and implement integrated smart city solutions,

accumulate knowledge and facilitate exchange of

information and focus on the intersection of Energy, ICT

and Transport.

Relation: Big4Smart is especially interested in the

activities of Integrated Infrastructures & Processes

(including Open Data), Sustainable Districts and Built

Environment and Sustainable Urban Mobility action

clusters that exploit Big Data to provide energy, transport

and ecology solutions in the urban context.

Big Data Europe

Description: Big Data Europe builds innovative

multilingual products and services based on semantically

interoperable, large-scale, multi-lingual data assets and

knowledge, available under a variety of licenses and

usiness models.

Relation: The methods and tools related to Big Data

analytics that are targeted at use of mobility data coming

from multiple sources, transport data exploitation, energy

grid data, etc. are of special interest to the work of

Big4Smart.

SMARTIE

Description: SMARTIE develops a distributed

framework to share large volumes of heterogeneous data

in smart-city applications.

Relation: Big4Smart looks into the distributed

framework and its operation based on where these

volumes of smart city information are flowing and where

they should be (pre-) processed and analysed.

Open Cities

Description: EU CIP Open Cities project that aims to

validate Open Innovation methodologies to the Public

Sector Future Internet Services for Smart Cities. It uses

platforms in Crowdsourcing, Open Data, Fiber to the

Home and Open Sensor Networks in seven major

European cities.

Relation: Big4Smart investigates the Pan European

Open Data Platform developed within Open Cities, in

order to use the various sets of data for the project

methodology validation.

FIWARE

Description: FIWARE is an EU driven middleware

platform for development and global deployment of

Future Internet applications. FIWARE Lab deploys a

geographically distributed network of federated nodes

leveraging on a wide range of experimental facilities.

FIWARE provides specific enablers for data and smart

cities management.

Relation: FIWARE Lab and infrastructure will be used

by Big4Smart to test the project methodology and for the

use cases to be developed, exploiting Open Data

published by cities and other organizations that is made

available in the Lab. Big4Smart investigates the provided

y FIWARE Big Data and Smart City related enablers.

FINESCE

Description: FINESCE (Future INtErnet Smart Utility

ServiCEs) is the smart energy use case project under FI-

PPP EU FP7 that contributed to the development of an

open IT-infrastructure related to the energy sector. The

project organized and run a series of field trials in 7

European cities

Relation: Historical Smart Energy datasets from the

FINESCE trial sites are available as open data and will

be used in Big4Smart methodology validation.

Furthermore, Hybrid Cloud Data Management

component that provides interface with private and

public data storage platforms is of special interest for

Big4mart.

2.2 State-of-the-Art at Bulgarian Level

In the recent years Big Data and Smart Cities

challenges have become a research topic for

Bulgarian academy, public administration and

industry. Research endeavours are not isolated at

national level but are taken in collaboration with

leading EU and world research teams and

organizations. Even though the obtained results are

promising, they are still providing just limited

solutions related to specific aspects and do not realize

a more holistic approach and methodology that is

targeted by Big4Smart. Table 2 shows a summary of

the current research initiatives in the area and

description on how Big4Smart plans to leverage

beyond.

Table 2: State-of-the-Art at Bulgarian level.

SMARTER

TOGETHER

Description: H2020 SMARTER TOGETHER aims at

large-scale replication and at in-depth knowledge transfer

about setting up of Smart City business models and

citizen-centric innovation contributing to positive

societal dynamics.

Bulgarian partner: Sofia city

Relation: Big4Smart investigates the SMARTER

TOGETHER Data Platforms, the integrated new datasets

from energy and mobility, as they all provide Open APIs

that can be easily extended of data analysis.

mySMARTLife

Description: mySMARTLife H2020 project is

developing and testing integrated innovative solutions in

the ‘lighthouse cities’ focuses on high performance

district (smart homes, smart buildings, renewables,

district heating and cooling); smart grids and mobility

(electric vehicles, smart charging infrastructure).

Bulgarian partner: Varna city

Relation: The project deploys an extensive monitoring

and evaluation programme to assess the effectiveness of

mySMARTLife actions and interventions. Contacts have

already been established by the team to apply Big4Smart

methodology in the mySMARTLife integrated planning

and decision-making process.

Sharing Cities

Description: Sharing Cities is a H2020 project offers a

framework for citizen engagement and collaboration at

local level, thereby strengthening trust between cities and

citizens.

Bulgarian partner: Bourgas city

Relation: Big4Smart investigates the developed by

Sharing Cities technologies to manage data from a wide

range of sources, including sensors, and will built upon

them.

Description: H2020 SmartEnCity, aims to develop a

systemic approach and strategies for transforming

European cities into sustainable, smart and resource-

efficient urban environments.

Bulgarian partner: Asenovgrad city

Relation: Big4Smart investigates the mechanisms

for data analysis for integrated planning of measures

to reduce energy demand and maximize renewable

energy supply.

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Table 2: State-of-the-Art at Bulgarian level. (cont.)

PLEEC

Description: By combining best practices, FP7 PLEEC

(Planning for energy efficient cities) develops a general

model for energy efficiency and sustainable city

planning.

Bulgarian partner: Ruse city

Relation: The model developed by PLEEC is based on

an intensive analysis of vast amounts of heterogenous

data, which is of special interest to Big4Smart

developments.

DaPaaS

Description: The project combines data-as-a-service

theories with use of open and linked data to improve

linked open data access. The goal is to reduce the barriers

of insufficient resources and allow citizens and public

bodies to contribute to the open data and expand the

linked open data cloud.

Bulgarian partner: Sirma and Ontotex

Relation: The project is more focused on linked open

data and its management, rather than on the analysis of

Big Data. Nevertheless, the Big4Smart investigates the

developed DataGraft tool, which accelerates and

simplifies the linked open data publication, consumption

and reuse cycle.

3 Big4Smart ARCHITECTURE

The architecture of the Big4Smart platform is

presented in Fig.1. It has three main building

components, namely repository, web APIs and web

user interface.

Figure 1: Big4Smart Architecture.

Big4Smart Repository stores datasets needed for

calculation of indicators’ values, metadata for

indicators as well as indicators’ values themselves.

Both automatic and manual data collection is

supported. The automatic data collection is based on

open datasets and external APIs that provide access

to such datasets. In the Republic of Bulgaria National

Reform Programme aligned to the strategy “Europe

2020” is included an initiative for establishment of

open data (Strategy “Europe 2020”). At European

level, the open data are regulated by the Directive

2001/29/EC of the European Parliament and of the

Council of 26 June 2013 amending Directive

2003/98/EC on the re-use of public sector information

(Directive 2003/98/EC). The law of access to the

public information carries this directive in Bulgarian

legislation. In this regard, Republic of Bulgaria open

data portal is functioning since 2014. Thus, a source

of data for calculating the indicators are the existing

open datasets. A large share of useful data is stored

internally in cities’ departments. Often, such data is

not easily localizable and sometimes it is not in

machine readable format. In addition, not all

published open data satisfies the common accepted

principles of open data and specific data needed for

calculation of indicators’ values could be missing.

Thus, a Web User Interface for structuring new

datasets is provided.

The Big4Smart web APIs consists of three groups

of services:

 Data Collectors – read datasets needed for

indicator calculation;

 Indicator Compiles – calculate values of

indicators;

 Indicator Viewers – support indicator

visualization.

Data Collectors are responsible for gathering data,

which can be available open datasets, non-open

datasets, provided by stakeholders, data form

additional sources such as online questionnaires,

existing smart city platforms, etc. Since the

calculation of single indicator could requires

aggregation of data from multiple datasets, the data

collectors adopt the Linked Data approach for

interlinking and attribute mapping between datasets.

The linked data sets allow heterogenous data to be

combined in a unified coherent source and usage for

“smart”, data-driven decision making.

The calculation of indicators’ values is provided

by Indicator Compilers services of Big4Smart web

APIs. Depending of the type of the output from

calculation, there are three types of indicators:

 Number – an absolute numerical value, for

example the concentration of carbon dioxide

emissions in µg/m3.

 Rate – a value, typically calculated in percentages,

for example the percentage of the population

affected seriously by crime or traffic accidents.

 Value on a scale – an integer value obtained

through qualitative assessment, for example an

assessment of the extent to which citizens may

participate in environmental decision-making in

scale of 0 to 10. A widely adopted approach is

application of Likert scale described qualitatively.

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The calculation of indicators’ values is performed

by preliminary defined formulas and/or algorithms

that are implemented as operations of data compiles

services. The calculation process includes the

following steps, shown in Fig. 2:

1) Specification of indicator(s), which value(s)

should be calculated through web user interface,

for example calculate Access to public transport;

2) Checking needed data for calculation, for

example collect the number of inhabitants with a

transportation stop within 500 m and the total

population by Indicator Compilers;

3) Checking available data for calculation by Data

Collectors;

4) Retrieving data for calculation by Data Collectors

(if step 3 is successfully performed, otherwise

sending notification to the end user);

5) Passing data to Indicator Compilers;

6) Calculation based on related algorithms by

Indicator Compilers, for example (number of

inh/total population) x 100;

7) Visualization of indicator(s)’ value(s) by

Indicator Viewers.

The quotative values of the indicators provide a

possibility for their visualization on the web user

interface. The visual presentation is supported by

Indicator Viewers services of Big4Smart web APIs,

providing different visualization models. It is

especially important for effective perception of

obtained assessments by the stakeholders. The

visualization provides not only graphical data

representation, but “smart” interaction with the users.

It is widely adopted by companies such as Google,

Facebook, Amazon, Apple, Twitter and Netflix to

support decision making.

4 CLASSIFICATION SCHEMA OF

INDICATORS

The classification schema of indicators is elaborated

as a result of systematic literature review, covering

four major reference electronic databases provided by

IEEE, ACM, Elsevier and Springer (Petrova-

Antonova, 2018). Additionally, a manual web search

is performed using Google and Bing search engines.

The collected indicators are explored in terms of

property that is measured or observed, primary

purpose, approach of calculation, unit and type of

assessment. As a result, six thematic areas of

classification schema are identified: Smart Nature,

Smart Living, Smart Mobility, Smart Governance,

Smart People and Smart Economy.

The Smart Nature thematic area asses the city

impact on the environment and its environmental

resilience. The pollution, the supply and efficiency

usage of resources (energy, water, land, etc.) are

analysed, as well as the activities to build a green

environment are considered in this thematic area. The

cities face complex and multi-dimensional challenges

during smart transformation process. Therefore,

Governance mechanisms are required to facilitate the

creation and implementation of effective public

policies. Building of a smart city needs more than just

concentrating on a few specific problem areas in a

piecemeal approach to policy. It requires a set of

coordinated policies that eliminate differences

between various sector-specific policies and provide

feedback to city leaders in order to work more

productively with each other as well as with citizens

and businesses. The Smart Economy thematic area is

focused on sustainable economic growth. It takes into

account the innovation and entrepreneurship spirit of

the city. The labour market and the companies’

lifecycle demonstrate the dynamics of the economy.

Figure 2: Calculation of indicators.

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The international embeddedness is also analysed

to obtain vision about the recognition of the city

beyond the boundaries of the country. The Smart

Living thematic area is directly related to the citizens’

quality of life. Its dimensions show whether the cities

are comfortable places to live focusing on aspects

such as education, healthcare, housing, safety, etc.

The cities are more than a combination of

infrastructure and buildings assets. They are living

ecosystem at the centre of which the People are. The

smart cities need their residents to participate in the

smart initiatives in order to be successfully

implemented. The citizens need to adapt to new

solutions, showing creativity and providing value to

their community regardless of their diversity.

Knowledge, professional qualification and skills form

the main tool to improve the city performance. The

Smart Mobility thematic area is related to delivery of

efficient, safe, clean and reliable transport network

for people, data and goods. The reduction of traffic

accidents, environmental impact and demands on

time and energy directly affect the mobility quality.

The assessment of city mobility provides a valuable

feedback for reshaping mobility patterns and

planning mechanisms.

The thematic areas are further divided in several

categories, shown in Fig. 3.

 Smart Nature – Water, Pollution, Waste, Energy,

Land and Green environment.

The indicators related to water and energy are

considered from two points of view – supply and

consumption, and efficiency usage and management.

The indicators assessing the pollution are divided in

two groups regards to target of assessment: air

pollution and noise pollution. The indicators of waste

evaluate both solid waste and wastewater. The

indicators assessing the land usage and status cover

the change of land use, land degradation, land

desertification, arable and permanent crop land,

fertilizer use efficiency, use of agricultural pesticides

and area under organic farming. The indicators of

green environment asses the ambitiousness and

comprehensiveness of strategies to improve and

monitor environmental performance, the

management of environmental issues and

commitment to achieving international

environmental standards.

 Smart Governance – Transparent governance,

Participation in decision-making, Public and

social services, Sustainable and smart city

strategies and Governance effectiveness.

The transparency of governance can be assessed

regarding the satisfaction with transparency of

bureaucracy, fight against corruption and availability

of open government data. The electronic public

services answer better to citizens’ needs and enable

participation in decision-making and governance

transparency. The participation of citizens in

governance decision-making process provide

inclusive and participatory growth of cities. The

certification of the environmental management

systems by international certification standards is a

common practice at local level that indicates

availability of sustainable and smart city strategies.

The e-procurement transactions, development of

monitoring systems and public-private partnerships

facilitate governance effectiveness.

 Smart Economy – Employment, Economic

growth, Innovative spirit, Entrepreneurship and

International embeddedness.

The employment can be assessed through a wide

range of indicators such as total unemployment rate,

youth unemployment rate, female employment, etc.

The economic growth has variety dimensions such as

total investments, grants, total annual costs, payback

and return on investment, gross domestic product, etc.

The innovation spirit of the city is evaluated by SCIs

such as Research and Development (R&D)

expenditure in percentage of GDP, employment rate

in knowledge-intensive sectors and number of patent

applications per inhabitant.

Figure 3: Classification schema of indicators.

Towards a Technological Platform for Transparent and Flexible Assessment of Smart Cities

379

The self-employment rate and new businesses

registered are sample indicators for assessment of the

entrepreneurship in city development. The business

and commerce networks, and online presence of

businesses are another dimension of international

embeddedness.

 Smart Mobility – Public transport, Public

transport alternatives, Traffic management,

Innovative transport systems, Logistics and ICT.

The indicators of public transport alternatives assess

the usage of bicycles, private cars and walking as

means of moving. The indicators of innovative

transport systems are focused on development of

sustainable, safe and clean transport system. The

indicators of ICT are divided in three groups, namely

Connectivity, Informational awareness and

Digitalization.

 Smart People – Education and qualification level,

Social inclusion, Lifelong learning, Demography,

Personal propensity and Social cohesion.

The education and qualification of citizens are an

important social and territorial competitiveness

factor. The indicators of social inclusion assess the

civic engagement in decision-making and full and

equal participation of people in economic, social,

cultural and political institutions. The affinity to life-

long learning is essential to sustainable development

of the cities since the scale and quality of human

capital are directly related to the creation and

dissemination of new knowledge. The indicators of

personal propensity are related to individual

characteristics such as creativity, open-mindedness,

flexibility, cosmopolitanism, etc. leading to personal

success and innovation. The social cohesion is a

characteristic of society which depends on the

accumulated social capital and could be assessed for

example using indicators of gender discrimination

and the inequalities, poverty and Gini coefficient.

 Smart Living – Health, Education, Safety,

Household, Culture, Touristic attractivity and

Buildings.

The accessibility to basic healthcare and

encouragement of a healthy lifestyle are core

indicators of healthcare. Undoubtedly, the shares of

students completing the primary, secondary and

higher education are critical indicators of education

system. The indicators of household give evidence

about the breakdown of housing sector by property

type (owner occupied or rental, single occupant,

couples, family or multifamily occupant, etc.) and

measure the housing quality as a degree to which

inhabitants suffer from poor housing conditions. The

share of natural disaster related deaths and number of

citizens living in disaster prone areas as well as the

share of population affected by crime or traffic

accidents are critical indicators of safety. The tourism

intensity is a distinctive indicator of city appealing

since the tourists also interact with city services and

affect the city profile. The sustainability in new

buildings and in building renovation, the policies and

systems of energy consumption and sustainability of

buildings are sample indicators of buildings.

5 CONCLUSIONS

Due to urbanization the cities meet a lot of challenges

affecting both their economic performance and well-

being. Some of them, like higher prices of services

and goods might be directly measured, and others,

such as pollution, traffic congestion and limited

parking spaces, are difficult to quantify in term of

cost. This paper proposes an architecture of

technological platform of indicators to monitor and

assess the performance and sustainability of smart

cities. It presents the building components of the

architecture and the adopted classification schema of

indicators covering six thematic areas, namely Smart

Nature, Smart Living, Smart Mobility, Smart

Governance, Smart People and Smart Economy.

The validation of the platform will be performed

using real data collected from Bulgarian cities. Sofia

is considered as a pilot city for conducting validation

experiment. The ambitions of Sofia to become a smart

city are laid down to the Sustainable Energy Action

Plan 2012-2020 (SOFENA, 2012). Sofia

Municipality is a partner of European project Smarter

Together. Its objective is to replicate the key findings

from lighthouse cities Vienna, Munich and Lion in

targeted areas, implementing them in different urban

and institutional environments. A current project of

Sofia Municipality is “Integrated metropolitan urban

transport – Phase II” funded by Operational

Programme "Regions in growth" 2014-2020. The

2020 vision of Sofia is to become “The green and

smart capital of Bulgaria – a model for sustainable

development”. The foregoing as well as the support

of the project by the Sofia Development Association,

motivates the choice of Sofia as a pilot city for

validation of the proposed solution.

ACKNOWLEDGEMENTS

This work was supported by the National Scientific

Fund, Bulgarian Ministry of Education and Science

KDIR 2018 - 10th International Conference on Knowledge Discovery and Information Retrieval

380

within the project no. DN12/9 and project no. DN

02/11, and by the Scientific Fund of Sofia University

within project 80-10-162/25.04.2018.

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