Studies on urban mobility and use of ICT in relation to cities' sustainability. A bibliometric analysis

Abstract

The objective of this article is to present three groups of analysis in urban mobility studies, their relationship with the use of ICT and how the findings promote urban sustainability. 40 documents in the Scopus database were systematically reviewed. The research methodology used was mixed, where methods of qualitative analysis and bibliometric analysis were combined with the Vantage Point software. The results made it possible to establish which projects are at the forefront of the study of urban mobility. This article will contribute to future research and could be useful for discussions on public policy on urban mobility.

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Transactions on Transport Sciences | Vol. 3/20211
Transactions on Transport Sciences
Peer-Reviewed Open Access Journal
Vol. 3/2021 DOI: 10.5507/tots.2021.012
journal homepage: www.tots.upol.cz
Studies on urban mobility and use of ICT in relation
to cities’ sustainability. Abibliometric analysis
ANA ESCOBARa, JHON ZARTHAb, LUCIANO GALLÓNc
a. Sustainability Specialist, Tigo Colombia, Cra. 16 #11a Sur100, Medellin, Postcode 050022, Colombia
b. Professor, Universidad Ponticia Bolivariana, Cq. 1 #70-01, Medellin, Postcode 050031, Colombia
c. Professor, Universidad Ponticia Bolivariana, Cq. 1 #70-01, Medellin, Postcode 050031, Colombia
KEYWORDS: Urban mobility; Urban trac; Information Technologies;
Smart Cities; Sustainable Development
ABSTRACT: e objective of this article is to present three groups of
analysis in urban mobility studies, their relationship with the use of ICT
and how the ndings promote urban sustainability. 40 documents in the
Scopus database were systematically reviewed. e research methodology
used was mixed, where methods of qualitative analysis and bibliometric
analysis were combined with the Vantage Point software. e results made
it possible to establish which projects are at the forefront of the study of
urban mobility. is article will contribute to future research and could
be useful for discussions on public policy on urban mobility.
1. INTRODUCTION
With the demographic explosion experienced in the 20th
century and, with it, the densication of urban space, the
sustainability of cities has increasingly become an urgent
issue. e projections of the (United Nations, 2017) for 2050
conrm the population growth trend of the last fty years:
the planet will experience the demand of some 9.8 billion
people, according to (Leone et al., 2017), and about 70% will
be concentrated in cities (López Bernal, 2004).
Urban expansion and extensive construction in cities’ pe-
ripheries consume valuable resources of the planet such as
land and water. is generates economic costs in terms of
infrastructure and energy, social costs such as congestion
of transport networks, and increases segregation and spe-
cialisation of land use and environmental costs such as the
degradation of the environment. All this wards the city o
from the model of sustainable development and undermines
certain traditional characteristics, such as its compactness
and diversity (Camagni, Gibelli, &Rigamonti, 2002).
In accordance with the eleventh Sustainable Development
Goal (SDG), Sustainable cities and communities, by 2030 it is
necessary to make cities and human settlements inclusive,
safe, resilient, and sustainable. e sustainability of cities
encompasses various topics, including urban transport. e
issue of mobility of cities is an important factor that not only
generates discussions about urban infrastructure, but also
the industrial activities of acity, the habits of its citizens,
ways of life, the administrative management of the city, the
state of air quality, environmental pollution, urban growth,
the logic of urbanisation, among many other elements (Unit-
ed Nations, 2015).
Intelligent mobility is aglobal trend that has been devel-
oping since the 1990s (Lopez-Carreiro &Monzon, 2018) with
the aim of making processes more ecient within aspecic
urban space. e intelligent management of cities depends
not only on the evolution of Information and Communi-
cations Technology (ICT) and the interconnection of their
services on digital platforms, but on the integration of the
urban system, paying attention to the areas of sustainability
(economy, environment, and society) without losing sight of
the political and cultural contexts. Academic studies have
been devoted to understanding this situation and it is the
interest of this investigation to present the trends in sustain
-
able urban mobility1.
is article is divided into ve sections: e rst one pre-
sents areview of concepts related to the paradigms of sus-
tainability and urban mobility, as well as the evolution of
the latter towards intelligent and sustainable systems. e
second part describes the methodology carried out through
four stages. e results obtained are presented in the third
section. In the fourth, the discussion of the results is pre-
sented based on three axes of analysis. e document ends
with the presentation of conclusions for future studies on
sustainable urban mobility.
1.1 eoretical framework
1.1.1. Sustainability and urban mobility
Faced with the excessive and indiscriminate consumption
of energy, materials and natural resources, the approaches
of sustainable cities are seen as autopia for critics of ur-
ban models who consider that the acceleration of the global
ecological decline is due to their perpetuation over time.
Cities are seen as the most complex socio-ecological system
developed by human beings, which suer constant states of
vulnerability due to unpredictable factors that disturb them
and end up generating signicant social, economic, cultural
and environmental costs (Zhang &Li, 2018)there is alarge
overlap between the meaning of resilience and sustainability,
which threatens to weaken both concepts. In this study, we
discuss the dierence between urban resilience (UR.
It is necessary to understand how urban systems can be
maintained over time and evolve without losing the elements
1 For the research goals, we decided to use the term “urban mobility” instead
of “urban transport” because it is amore comprehensive term where other
types of mobility enter the city that include dierent levels of mobility. is
is the case of mobility on foot or of pedestrians in the public space of the
city. erefore, the design of the information search equations was based
on urban mobility.

Transactions on Transport Sciences | Vol. 3/20212
that dene it. Sustainability is understood as the capacity
of asystem to perpetuate itself during acertain period in
specic physical conditions that, however, change with time
according to its evolutionary dynamics. Sustainability over
time is an expected characteristic to preserve life or maintain
initial ecological conditions that enable the well-being of
future generations (Daly, 1990).
Sustainability of urban conglomerates is related to ‘active
process of synergetic integration and co-evolution between
the subsystems making up acity without compromising
the possibilities for development of surrounding areas and
contributing by this means towards reducing the harmful
eects of development on the biosphere’(Zhang &Li, 2018)
there is alarge overlap between the meaning of resilience
and sustainability, which threatens to weaken both concepts.
In this study, we discuss the dierence between urban resil-
ience (UR. is implies having the ability of the urban system
to decide to grow in its physical and material structure as
it seeks to make this growth atool for the achievement of
its social, economic and environmental objectives (López
Bernal, 2004).
With the growth of economy, the increasingly high con-
centration of human activities and the intensication of
population density in urban centres, urban mobility has
been characterised as one of the priority issues to be re-
solved in modern cities. e study of this phenomenon in
cities begins to be conceived in abroader and integrated
way with urban life forms, considering it essential to speak
of urban mobility more than means of transport. e in
-
dividuals of acity are not only responsible for interacting
passively and mechanically with the modes of transport
available and moving from one place to another without
any consideration, but rather they respond in aviable way
with their movement interacting with everything else that
is around them (Jensen, 2013).
is change in the vision of movement in acity allows
us to understand it better through its conguration as
anetwork of roads where it is essential to talk about the
dynamics of access and use of space, in such away that it
is possible to move within it to receive and transmit infor-
mation (Jensen, 2013). Urban mobility can be understood
as the set of movement ows within the city (Fistola, Rai-
mondo, &Rocca, 2017) congured of people, goods, infor-
mation and signs that circulate in networks of relationships
(Jensen, 2013).
erefore, urban mobility is the ability of individuals to
move from one place to another (Costa, Morais, &Bertolde,
2017) having the possibility of doing so in an organised and
coherent manner according to their physiological, intellec-
tual and socio-economic needs, and using existing transport,
public services and ICT infrastructure (Vidovic, Mandzuka,
&Brcic, 2017).
1.1.2. Urban mobility: smart models
Due to the increase in the growth of urban centres, the high
demand for public and private transport eets, as well as
aspontaneous emergence of transport systems, alarge num-
ber of cities in the world experience deteriorated mobility
that has led to important socio-environmental problems
(Costa et al., 2017). Given the changes that are occurring
in the use of transport, in its demand and in the way of
travelling around the world, the study of mobility and in-
novation in sustainable systems becomes apressing action.
e factors that determine these changes are diverse: socio-
economic (lifestyles, suburbanisation, increasing use of ICT,
etc.), technical (development of new means of transport
such as e-cars and e-bikes, intelligent transport systems,
etc.), economic (energy and transport prices) and political
(Aguiléra &Grébert, 2014).
e concept of sustainable urban mobility implies think-
ing about the structural means, as well as the daily life of
the citizens, their cultural structures, and the actions they
carry out regularly. Sustainable urban mobility involves
integrating layers of analysis that allow us to see the multi-
dimensionality of urban transport from socio-economic and
spatial aspects such as age, gender, working conditions, the
type of physical conditions of citizens, types of intermodal
transport, travel expectations, routines and social habits,
as well as the type of situations that represent problems of
accessibility and mobility for some users (Costa et al., 2017;
Mataix, 2010).
us, sustainable urban mobility is the ability of cities to
meet the needs of society to move freely, have open access to
places in the city, be able to communicate, negotiate in urban
spaces and interact with others without sacricing other con-
ditions of daily life. So, this concept implies the development
of actions that contemplate the reduction of displacements
and their replacement by means of technological solutions,
as well as political measures that address the decrease of the
private automobile, the articulation of intermodal transport,
the use of land and the increase of social innovation (Costa
et al., 2017).
Sustainable urban mobility is also related to technologi-
cal advances and the incursion of ICT in the generation
of solutions for the city. Growth of urban pressure gener-
ates, in turn, pressure on urban transport systems and,
therefore, the demands for new solutions in mobility ser-
vices increase, especially in the mixture of the traditional
public service with new modalities that are achieving the
replacement of the private automobile (Kamargianni, Li,
Matyas, &Schäfer, 2016). In the last twenty years, travel
information systems went from presenting independent
solutions to developing integrated information systems.
Now, Advanced Traveller Information Systems (ATIS), use
ICT to present travel information to users of various modes
of transport, assist in reservation processes, planning and
use of intermodal services (Himmel, Zaunbrecher, Ziee,
&Beutel, 2016).
Specically, the use of the Internet has brought about
changes in urban mobility. Developments in ICT have gen-
erated transformations in the demand for transport services
and have opened several opportunities with the incursion of
certain tools and services. is is how ICTs have inuenced
the means of transport, infrastructure, routes and, in general,
the behaviour of its users (Snellen &de Hollander, 2017)there
are tensions between short and long term interests, public
and private interests and between eciency and equity. Ana-
lysing how new developments impact public values that are
considered relevant in the transport debate shows that there
is awide range of aspects to consider. We discuss four public
values. Accessibility is concerned with providing access for
all, making sure there are transport options available as well
as taking care that people have the capabilities to access
them. Aordability or (cost.
2. MATERIALS AND METHODS
With the purpose of conducting aliterature review in sus-
tainable urban mobility and its integration with ICTs, the
following stages were followed:
Stage 1: e sources of information were identied from
the Scopus database using these keywords: Urban Mobility,
ICT and Sustainability.
Stage 2: e search criteria were selected and focused on
the years 2014-2018. Due to the few data found by an equation
with three keywords, the word Sustainability was eliminated
from the search and amore comprehensive one was deter-
mined, leaving only ICT and Urban Mobility. e nal search

Transactions on Transport Sciences | Vol. 3/20213
equation was: TITLE-ABS-KEY (Urban mobility) AND ict AND
(LIMIT-TO (PUBYEAR, 2018) OR LIMIT-TO (PUBYEAR, 2017)
OR LIMIT-TO (PUBYEAR, 2016) OR LIMIT -TO (PUBYEAR,
2015) OR LIMIT-TO (PUBYEAR, 2014) OR LIMIT-TO (PUB-
YEAR, 2013)) AND (EXCLUDE (PUBYEAR, 2013)) AND (LIMIT-
TO (DOCTYPE, cp) OR LIMIT- TO (DOCTYPE, ar)). With this
equation it was possible to verify that the documents present
in the equation with the three keywords were included in the
equation nally dened.
Stage 3: Based on the search equation, 62 documents
were retrieved. All were chosen for their summaries to be
read. However, only the results of the scientic articles pub-
lished in journals were considered. Finally, 40 documents
related to the use of ICT and sustainable urban mobility were
analysed. Five articles were excluded from the initial list
of articles because they were not available in the academic
databases or on the Internet. e excluded documents were:
(Passalacqua, 2014; Kahlen, Lee, Ketter, &Gupta, 2018;
Rehm, Faber, &Goel, 2018; Alam, Fernandes, Almeida, Fer-
reira, &Fonseca, 2017; N. Li, Chan, Hsu, Fu, &Mao, 2017)yet
are responsible for up to eighty percent of global greenhouse
gas emissions and seventy-ve percent of natural resource
consumption. ey impact air and water quality, alter re-
source consumption patterns, and pose unique challenges
for the environment, energy, and infrastructure. Address
-
ing these challenges requires understanding the city as the
nexus of environmental context, built infrastructure, and
human communities, which can only be achieved by close
collaboration among educated professionals from awide
range of related domains. is paper describes the experi-
ences of auniversity course that was developed to prepare
students from diverse academic backgrounds to understand
the nature of and learn the required skills to address sus-
tainable urbanization challenges. e course, which was
partially online enabled by the latest information and com-
munications technology (ICT. For the process of analysing
the documents, an Excel le was created that contained the
following criteria: Name of the article, Author, Year, Country,
Journal, Journal SJR, Quartil de Scimago, CiteScore 2016,
SNIP 2016, Type of publication, Abstract, Keywords and
University of Aliation.
Stage 4: To generate the analysis, two types of matrices
were built in Excel: 1) aglobal matrix that included the cat-
egories year, countries, name of the article, authors, sum-
mary, keywords, and aliation. is was processed by the
software VantagePoint 10.0 to analyse the academic tenden-
cies in adescriptive way that arose from the results of the
literary revision. e list was cleaned for both keywords and
key phrases. 2) Adetailed matrix with year elds, document
name, authors, problem, objective, theoretical framework,
study proposal, gaps, research methodology, summary, coun-
try, and conclusions related to analysis.
3. RESULTS
e following results were found from the analysis of the
40 documents that are directly related to sustainable urban
mobility and the use of ICT. ese results are presented in
two parts: 1) articles published by year and country, and
2)the keywords, key phrases and its appearance in the years
consulted.
3.1. Articles published by year and country
Five articles were published in 2014, six in 2015, nine in
2016, 17 in 2017 and three in 2018. is shows that there
has been an exponential growth that, according to the trend,
could be achieved for the following years. Italy is for the
sample of this study the country that has generated the
most publications in the last ve years. With arepresenta-
tiveness of 28% of the publications, Italy generated publi-
cations throughout all the years analysed. en there are
countries in the European region: Germany, Portugal, the
United Kingdom, France, Spain, Greece, the Netherlands,
and Poland, which indicates that the advances in terms of
sustainable urban mobility are concentrated in this region.
However, there is apresence of countries in the North Ameri-
can region such as the United States, and South America
with Brazil and Mexico.
Figure 1 reveals the relationships between years and
countries. Italy is the only country that, in addition to hav-
ing the largest number of publications, is distributed over
the ve years selected. As can be seen, seven countries in
the European region developed publications in the year
2017.
3.2. Analysis according to keywords and phrases
For the analysis of urban mobility integrated to the use of
ICT, it was necessary to observe the emergence of keywords
and key phrases according to their representativeness in each
of the articles. Figure 2 and Figure 3 reect three essential
situations:
1.
e terms used in the search equation (Urban Mobility and
ICT) are predominantly present.
2. e concept of sustainability emerges in avisible way in
several phrases and keywords such as Sustainable Urban
Mobility and Sustainable Cities, which condense the va-
riety of appearances in the articles analysed.
3. Concepts such as Smart City, Urban Mobility Simulation,
Intelligent Transport Systems, Smart Urban Mobility, Col-
lective Actions, Cooperative Intelligent Transport Sys-
tems, Socio-technical Systems, Systems Approach, and
Innovation arise as acorrelate of the key concepts of this
study.
e discussion on the academic trends in the study of ur-
ban mobility for the sample of selected articles is related to
ICT, sustainability of cities, models and sustenance of smart
cities, presentation of analysis systems, collective and par-
ticipatory actions, as well as innovation processes.
According to the years, it can be seen in Figure 4 how the
keywords used in the search equation, both Urban Mobility
and ICT, appear with greater force as the articles are more
recent. Emerging words such as Sustainable Urban Mobil-
ity and all those related to intelligent systems (Simulation,
Smart City, and Intelligent Transport Systems) have been
maintained over the years, demonstrating the consolida-
tion of these approaches in the studies and in the academic
literature. Furthermore, in Figure 5 it is possible to see the
relationship of the most inuential authors with the key
themes of this research where ICT, urban mobility, ITS, smart
city and Emobility are relevant to them.
Figure 1. Top 10 countries by year relation. Own elaboration.

Transactions on Transport Sciences | Vol. 3/20214
4. DISCUSSION
Urban sustainability and smart cities models are cross-sec-
tional concepts that appear in the studies on urban mobility
and ICT use within the last ve years. e discussion on the
integration of new digital technologies as fundamental tools
in the construction of sustainable societies opens aeld for
theoretical enquiry on how to move towards holistic propos-
als in which both technological solutions and models are
present and where communication and participation with
citizens is strengthened. Although there is no single deni-
tion of sustainable and intelligent cities, the academic trends
mentioned below help to understand precisely the transitions
required to grasp mobility needs for cities.
After the analysis based on the authors, the years, the
countries, the keywords, and phrases in the articles through
the Vantage Point tool, and the qualitative analysis of the 40
selected documents, three axes of discussion were chosen
that focus on the articles and the type of analysis developed:
1) modelling and simulation of vehicular trac, 2) incursion
of digital technologies in urban mobility, 3) intelligent and
sustainable urban mobility systems.
4.1. Modelling and simulation of vehicular trac
A total of seven articles were published between 2014 and
2017 that are characterised by generating solutions for ur-
ban mobility problems through the construction of algo-
rithms and mathematical models that propose to articulate
the systems of trac in an intelligent way. at is, through
simulations and modelling of situations with data from mo-
bile telephone users (D’Andrea &Marcelloni, 2017; Shields,
Doody, &Scully, 2017)GPS traces are pre-processed and
placed in the road map. en, the system assigns to each
road segment of the map atrac state based on the speeds
of the vehicles. Finally, it sends to the users trac alerts
based on aspatiotemporal analysis of the classied seg-
ments. Each trac alert contains the aected area, atrac
state (e.g., incident, slowed trac, blocked trac, data from
interconnected vehicles and GPS (Cárdenas-Benítez et al.,
2016), real-time monitoring sensors (Rakkesh, Weerasin-
ghe, &Ranasinghe, 2016), historical data on congestion
and road situations in the city (Elhatri, Tahifa, &Boumhidi,
2017) and trac congestion metrics (Batur &Koç, 2017)it
is vital to achieve and maintain social behavioral change
for shifting our modes of mobility from inecient, waste-
ful and motorized means to cleaner, greener, healthier and
more economic means such as walking, cycling and public
transportation in addition to smart use of land, intelligent
transportation systems, and clean and green vehicles. is
study is based on acritical review of literature in order to
establish aframework of social behavioral change policies,
particularly developed and tested for urban mobility and
trac congestion. First, various mega cities were compared
on dierent sustainability indicators to better understand
the case of Istanbul. en, selected policy potentials, namely
Travel Demand Management (TDM.
Urban mobility studies show, how trends towards the de-
velopment of solutions to the problems of trac and conges-
tion in the city have been evolving in accordance with the
dynamics of smart cities. Preliminary studies in the literature
reviewed conceive urban mobility as the movement of vehicu-
lar trac in cities (Geroliminis &Daganzo, 2008) where it is
necessary to contemplate relationships between vehicle ow
and density (Daganzo, Gayah, &Gonzales, 2011), and inter-
actions between physical conditions and human activities
(F.Z.Li, Fisher, Brownson, &Bosworth, 2005). e objective
is to predict the speed of vehicles and the distribution of traf-
c in the road network according to supply and demand and
routes of origin-destination (Daganzo, 2007) moving from
Figure 2. Cloud of keywords. Own elaboration.
Figure 3. Cloud of key phrases. Own elaboration.
Figure 4. Top 10 keywords by year relation. Own elaboration
Figure 5. Top 10 authors by keywords relation. Own elaboration

Transactions on Transport Sciences | Vol. 3/20215
approximate models to reality, especially in times of heavy
trac and congestion where the system is dynamic.
e foundation of this analysis focuses on the understand-
ing of how, where and when people move daily, especially in
densely populated areas, so that they can propose solutions
in the design of transport infrastructure and make its use
more ecient. is academic trend presents an interest in
the conguration of Intelligent Transport Systems (ITS) to ad-
dress and alleviate transportation problems and congestion.
In general, one ITS is based on location-based information: it
monitors and processes the location of acertain number of
vehicles used as probes to obtain information on estimated
travel time, driving conditions and trac incidents. In its
most recent form, this is done through the monitoring of
telecommunications networks and the data they obtain from
mobile phone users (Calabrese, Colonna, Lovisolo, Parata,
&Ratti, 2011; D’Andrea &Marcelloni, 2017; Steenbruggen,
Tranos, &Nijkamp, 2015; Zheng, Rajasegarar, &Leckie, 2015),
construction of aTrac Management System based on social
networks and the potential use of smart cars that allow detec-
tion and faster and more accurate trac congestion mitiga-
tion (Djahel, Doolan, Muntean, &Murphy, 2015).
4.2. Incursion of ICT in urban mobility
Some studies provide frameworks of analysis for ICT use in
urban mobility and the potential development of new con-
ceptualisation and 16 publications evidence the incursion
of ICT in urban mobility. Of these, six lines of analysis were
found: 1) Eects of urban mobility, 2) Impact of ICT on urban
mobility, 3) Diagnosis of urban transport and its trends in the
future, 4) Systemic analysis with emphasis in socio-technical
systems, 5) Conceptual proposals for the understanding of
urban mobility, and 6) Design of indicators to measure sus-
tainable urban mobility.
4.2.1. Eects of urban mobility
Kozievitch et al. present an investigation based on adata
analysis of Geographic Information Systems (GIS) that char-
acterises urban noise produced by transport systems. Limita-
tions on the study’s use of data from sensors in terms that
ignore the motivations of people moving around the city
raises the need for various sources of information data to
give amore precise graphic sense to the characterisation
of the phenomenon of urban noise. With the help of tools
to generate data integration, the analysis generated helps
to explore how it is possible to obtain ageo prole to make
decisions according to the dynamics of specic spaces of the
city, land use and decide on the type of future uses according
to the problematic situations regarding mobility in the city
(Kozievitch, Gomes, Gadda, Fonseca, &Akbar, 2016).
4.2.2. Impact of ICT on urban mobility
e analysis of the impacts of ICT on urban mobility gener-
ates an academic trend with six articles that ask how the
incursion and integration of ICT in urban mobility gener-
ates positive changes. Broadly speaking there is aconsensus
among the studies on the benets and potentialities of ICT
in the generation of solutions for urban mobility.
Cohen-Blankshtain &Rotem-Mindali indicate three dimen-
sions in their study on which ICT have an eect on sustain-
able urban mobility: 1) ICT have the potential to aect travel
demand directly, 2) ICTs also have the potential to change
the transport system by introducing new technologies into
existing transport technologies and 3) ICT can directly aect
urban forms (land use, demand for certain types of land use and
accessibility to certain places) (Cohen-Blankshtain &Rotem-
Mindali, 2016). Pronello et al. study how public and private
transport users may have awillingness to pay for an Advanced
Traveller Information System (ATIS). ey understood that
although the integration of ICT in the urban mobility system
can generate positive impacts, the change in behaviours of
travellers’ behaviour does not depend solely on these initia-
tives. So, new technologies alone are not powerful enough to
change the behaviour of travellers. Authorities must also carry
out co-ordinated actions to improve transport networks where
alternatives exist for the use of cars and introduce policies to
reduce their usage (Pronello, Duboz, &Rappazzo, 2017).
Tadis et al. concluded that ICT interventions in trans-
port networks can be benecial in terms of CO2 emissions
and cost reductions, and may therefore become an impor-
tant tool for local authorities and policymakers (Tadis et
al., 2017). Serna et al. address the way in which information
from social networks and User-Generated Content (UGC) can
be used to analyse urban mobility. is study identies how
the information collected in social networks can complement,
enrich (or even replace) the data traditionally obtained from
origin-destination surveys (Serna, Gerrikagoitia, Bernabé,
&Ruiz, 2017)in fact one of the challenges posed by booming
urban populations is the question of mobility. Traditional
travel survey methods used to study urban mobility are very
expensive, and the data collected are of poor quality. is is
mainly explained because of the diculty of getting arepre-
sentative sample of the population, and the lack of motivated
participants. erefore, travel surveys are carried out less and
less frequently, and the result is that good travel data is not
available to develop mobility and travel behaviour studies.
Information and Communication Technologies (ICT.
e convergence of ICT with electric mobility solutions is
essential to generate an experience that satises its users and
really changes the behaviour of citizens. In their analysis of
electric bicycles aimed at high school students, Arsenio et al.
highlight how users preferred an electric bicycle integrated
with ICT systems such as: sensors for their safety and pro-
tection, aGPS to see the shortest routes, bicycle to bicycle
connections, an application to see the number of calories
burned and acooperative system of electric bicycles (Arsenio,
Dias, Lopes, &Pereira, 2017)further research is needed to
understand users’ preferences and the range of factors that
can contribute for people to shift from car use to low carbon
vehicles such as e-bikes. is paper is built on the Be4Schools
R &D project implemented in the city of Águeda which is
considered the rst smart city in Portugal. It comprised the
former study in the country that examined the willingness
of students (aged 15-21 years.
Tyeld &Zuev explain how China has been implement-
ing policies so that its citizens prefer electric mobility. e
authors demonstrate how the transition from conventional
mobility to electric mobility in this country has not been
successful because it requires not only changes in the auto-
mobile industry, but also the construction of an ecosystem
that presents socio-technical transformations, fundamen-
tal techniques and their coordination with public policies,
cultural areas, and advances in the digital industry. ey
emphasise the creation of ecosystems for the realisation of
this transition where the incursion of ICT and the advances
that Google and other companies in the digital industry have
been developing in terms of autonomous and connected ve-
hicles have agreater impact than advances made by tradi-
tional companies in the automotive industry where there is
not necessarily aconvergence of digital media and tools in
electric vehicles (Tyeld &Zuev, 2018).
4.2.3. Diagnosis of urban transport
and its trends in the future
In the literature review several case studies diagnose the
state of urban mobility in some cities, mainly European. Two
such studies diagnose and evidence urban transport trends
worldwide.

Transactions on Transport Sciences | Vol. 3/20216
Arimah presents astudy that identies how African cit-
ies have adeteriorated road infrastructure and poor pub-
lic transport services. but present advanced conditions for
telecommunications services, especially mobile telephone
systems. However, there is no integration between the two
types of services. e solutions proposed tend to the imple-
mentation of Bus Rapid Transit (BRT) services and railways
that can promote more inclusion in transport and dignify
mobility within cities (Arimah, 2017). Aguiléra &Grébert
suggest that between mass transportation and private car
use awide range of solutions exist that can be implemented
through innovations and basic criteria of sustainable urban
mobility (Aguiléra &Grébert, 2014).
4.2.4. Systematic analysis with emphasis
on socio-technical systems
ree studies emphasise an understanding of the city and ur-
ban mobility from the theory of systems and Socio-Technical
innovation (ST) wherein the mobility of cities appears as an
interdependent and interrelated element alongside other
urban activities, exerting dierent connecting functions on
the urban space.
Marletto approaches urban mobility based on the study
of the theory of dynamic and complex systems, achieving
asystemic representation based on asocio-technical map of
relationships. He proposes an analysis of the current scenario
and three projections of scenarios to 2030:1) Auto-city, 2)Eco-
city and 3) Electricity, based on three variables 1) business
models, 2) propulsion of technologies and 3) power. ST inno-
vation approaches are used to show how the future of urban
mobility will depend on competition between coalitions of
innovative actors who support alternative transport systems
(Marletto, 2014).
Kourtit et al. propose the urban plaza as an analytical
framework that integrates the urban space. Making use of
asystemic vision, the plaza would interconnect four funda-
mental pillars: 1) economy and innovation (economic capital
and creative entrepreneur); 2) mobility (infrastructure, lo-
gistics, connectivity, and communication capital); 3) society
(social and cultural capital); and 4) ecology (environmental
capital). Asustainable urban mobility is seen here as amo-
bility that ensures an intelligent interconnection capable
of generating city networks and providing communication
and information exchange. is study assumes that the in-
cursion of ICT does not necessarily change the mobility of
cities, but it does allow for the generation of changes in
behaviour patterns, in the travel experience itself and in
the perception of their costs (Kourtit, Nijkamp, Franklin,
&Rodríguez-Pose, 2014).
Fistola et al. conceive mobility in an integrated way; ICT
appear as an element that is part of the urban system not
only as acomplement but as an adopted element of the city.
e systemic approach to the city allows us to visualise the
existence of three urban subsystems with which the activities
of mobility interact and all the other functions in the same
space that is the city: 1) the physical subsystem (material
component); 2) the functional subsystem (intangible com-
ponent); 3) the socio-anthropic subsystem (people who live
inside the city) (Fistola et al., 2017).
4.2.5. Conceptual proposals for the understanding
ofurban mobility
Schwanen explains how applications can contribute to atran-
sition towards the sustainability of urban mobility if they are
less understood as instruments for carrying out certain ac-
tions, and they come to be perceived as complete objects that
exceed the relationships of which they are apart. is study
presents aconceptual framework that helps to understand
the relationships between mobile phone applications and
physical mobility. e framework emphasises the analysis
of human behaviour and social practices related to mobile
phone use (Schwanen, 2015).
4.2.6. Design of indicators to measure sustainable
urban mobility
ree of the articles that enter this trend have advanced in
the construction of indicators to measure sustainable urban
mobility and understand how the paradigm of smart and
sustainable cities impacts on the development of viable and
ecient solutions.
Lopez-Carreiro &Monzon advance in the measurement
of sustainable urban mobility by introducing the concept
of Intelligent Mobility within this rst larger concept, thus
achieving conceptual consensus. eir study explores the
current indicators to measure the intelligence of the systems
in acity, which considers the need to address the dimensions
of sustainability and the index of technological innovation,
reaching asynthetic indicator of intelligent urban mobility
that serves to understand the dimensions and areas that are
stronger or weaker in the evolution of systems in the city
(Lopez-Carreiro &Monzon, 2018). Vidovic et al. delve into
the construction of urban mobility indicators through the
use of information obtained by the behaviour of ICT services
users. It is astudy that helps to understand various types of
indicators as well as sources to measure mobility in acity
(Vidovic et al., 2017). Battarra et al. present 12 indicators to
measure the eect of ICT on urban mobility and 16 more to
measure accessibility and sustainability, reaching atotal of
28 indicators. is study makes aspecial call regarding the
data that are necessary to start measuring the eciency and
results of ICT on urban mobility. eir results found that while
acity is not equipped and articulated in an integral way with
intelligent mobility and transport systems, the use of ICT
tends not to have apositive eect on urban mobility. e more
obsolete urban mobility systems are, the more dicult it is
to generate real eects through the use of smart applications
and systems (Battarra, Zucaro, &Tremiterra, 2017).
4.3 Design of intelligent and sustainable urban
mobility systems
e last of the analysis realized in the literature review is
related to the design of proposals and systems to optimise
urban mobility. is point collects more than half of the arti-
cles that entered the search equation, which is why it is quite
signicant for the discussion of this article. Other articles
focused on proposing analytical and comprehensive schemes
on urban mobility have already been analysed. As well as
constructing information architectures that work from the
modelling of data, this latter analysis provides specic so-
lutions including the integration of ICT in urban mobility.
Within this analysis four subdivisions were found: 1) Electric
vehicle platforms, 2) Co-operative platforms for smart mobil-
ity, 3) Platforms for urban mobility.
4.3.1. Electric vehicle platforms
e studies reviewed show an important convergence be-
tween the development of electric vehicles and the incursion
of intelligent technologies. Arena et al. observe the needs
of users and their proles to generate aproposal for shared
electric vehicles in Italy that allows specic congurations
and auser-centred performance. e centre of the proposal
is Green Move, aproject that allows consistent routes for the
needs of users and their proles, so that they have acustomis-
able service (Arena et al., 2015). e proposal of Mingrone et
al. is focused on asimilar way, highlighting the importance
of generating an ecosystem of services and benets for us-
ers that are part of the shared service of electric vehicles.
ese authors propose amodel by means of which the users

Transactions on Transport Sciences | Vol. 3/20217
can count on the possibility of renting an electric car and
returning it in any of the points of the system, besides hav-
ing advantages such as free parking, an interactive system to
obtain information about the mobility of the city and access
to areas of limited trac (Mingrone, Pignataro, &Roscia,
2015). Himmel et al. also present an urban mobility system
focused on the intermodality of means of transport to of-
fer acomplete experience to its users. is system is called
Mobility Broker and has aconguration of an app that helps
manage travel, as well as the planning of journeys and the
reservation of electric vehicles and electric bicycles. In con-
clusion, it shows how the user experience is very important in
changing urban mobility according to its direct relationship
with the system and its possibility for personalised adjust-
ment according to needs (Himmel et al., 2016). Andaloro et al.
highlight the need to propose acargo transport system that is
responsible both for the use of electric vehicles for deliveries
to the interior of cities, as well as for the clean generation
of energy (OR generation of clean energy). e construction
of the vehicle presented had aspecial selection of elements
so that its investment in technology was low cost (Andaloro
etal., 2015)where 68% of the EU population lives using 70% of
the energy, an integrated and sustainable urban approach is
needed. In order to meet the increasingly complex challenges
of urban areas new, ecient, and user-friendly technologies
and services, in particular in areas of energy, transport, and
ICT are required. In the transport sector electric urban mo-
bility and synergy between dierent transport systems (ITS.
Finally, Dimitrakopoulos emphasizes the construction of 5G
networks that allow the incursion of autonomous vehicles
that provide safety and comfort to users. His proposal works
through Vehicell, an intelligent interface that manages the
information of the city and chooses the best options for ef-
cient mobility (Dimitrakopoulos, 2017).
4.3.2. Co-operative platforms for smart mobility
is line considers that the participation of citizens and
stakeholders (municipalities, users, and transport provid-
ers) is akey element for the success of sustainable urban
mobility. For the proper functioning of transport, intelligence
is required both for transport and for users. is analysis
emphasises that, in the context of urban mobility, smart
mobility technologies can help people access and exploit
multimodal mobility options and make the most of available
mobility alternatives. However, mobility technologies do not
only aect mobility practices and user behaviour. ey can
also improve transport and mobility planning in cities. e
key aspects are the exchange of data, the generation of data,
the integration of dierent types of data and the participation
of citizens in the collection of data (crowdsourcing) (Lenz
&Heinrichs, 2017).
In the emergence of systems for intelligent urban mobility,
developments focused on the creation of user co-operative
networks that interact from dierent levels. In some cases,
co-operation allows sending information that is condensed by
an administrator to have up-to-date information in real-time
that helps in making decisions about urban mobility. Leone et
al. propose amobility system based on aco-operative network
of visual sensors that communicate through awireless net-
work compatible with devices and elements of IoT (Internet
of ings) in which images can be captured by integrated
cameras to capture information about the city. e co-oper-
ative network of visual sensors is responsible for collecting
and adding events related to an intelligent transport system
providing advanced services to users (Leone et al., 2017).
Other cases raise participation levels whereby the user can
interact with others, comment, publish, make recommenda-
tions, and develop their own projects. Severengiz et al. ad-
dress the creation of asustainable manufacturing community
(SMC) through aweb platform where people can contribute
and access others’ contributions to develop various projects.
e SMC communities’ approach is characterised using ICT
and applications that allow global citizenships to be cong-
ured based on the transfer of knowledge. Global thinking
processes are strengthened without losing local needs. Its
bases are found in the construction of open and free knowl-
edge as essential elements of the SMC (Severengiz, Seidel,
Steingrímsson, &Seliger, 2015). Ferreira et al. contribute
from the PASMO project, an open living laboratory for the
development of Smart Cities and Smart Communities, open
to companies, municipalities, and research institutions. e
project has astrong focus on smart mobility and co-operative
ITS applications, based on aset of dierent protocols and
communication standards (Ferreira et al., 2017).
Pitt et al. focus on how an integration of the cyber-phys-
ical systems can be made with the socio-technical systems
where users and consumers can become collaborators and
participants in the management of these systems. ey pro-
pose asystem of polycentric governance from acommunity
space that opens the possibility of citizen participation and
energy consumption in such away that self-government is
established for self-management of acity’s energy-related
processes. Although this study focuses on energy manage-
ment, it also delves into concepts of intelligent management
of urban mobility (Pitt, Diaconescu, &Bourazeri, 2017).
4.3.3. Platforms for urban mobility
is latest nding brings together all those urban mobility
platforms that integrate smart services assuming the need for
acomprehensive approach to sustainable cities. e progress
of the Integrated Real-time Mobility Assistant project (IRMA)
is important in this analysis. IRMA presents integrated mo-
bility solutions for users, orientating green, shared and pub-
lic transport services under adigital infrastructure (Motta,
Sacco, Ma, You, &Liu, 2015). e approaches of experiment-
ing with the TETRis model2 in the creation of an intelligent
environment for urban mobility by means of social sensors
are interesting. is model provides tools for both citizens
and city managers in such away that everyone has access
to information in real-time, can make alerts about situations
that arise in the city while maintaining close contact between
citizens and government (Citrigno, Graziano, Lupia, &Saccà,
2014). It is worth highlighting previous solutions about urban
mobility atlases that oer complete catalogues of mobility
behaviours in acity to explore the changing circumstances
of acity (Batty et al., 2012).
Marchetta et al. allows the articulation of an ecient urban
network with information shared by its users and by institu-
tional actors through an interactive map that provides aset of
services for urban mobility (Marchetta, Natale, Pescape, Salvi,
&Santini, 2016)a new vision of urban mobility, is areality.
To implement smart mobility scenarios adeep integration
among citizens, private and public transportation systems
and ICT is required. With the S2-Move project we propose an
architecture able to collect, update, and process real-Time
and heterogeneous information from various sources (tab-
lets, smart-phones, probe vehicles. Di Martino &Rossi also
advance with aconvergent system of multimodal solutions
for users that, in an ecient way, take into account the ori-
gin of the journeys, the destination and the knowledge of
the users to achieve experiences focused on them and their
needs (Di Martino &Rossi, 2016). Falco et al. contributes to
2 ‘TETRis focuses on innovative services for Smart City/Smart Territory via
the denition of technological tools and intelligent platforms which enable
local organizations to acquire, represent and manage data and information
gathered from sensors and devices by means of several communication sys-
tems deployed in the vest of add-value services’ (Citrigno et al., 2014) (p. 1).

Transactions on Transport Sciences | Vol. 3/20218
this eld with the concept of Infostructures proposing data
networks and open codes for urban mobility in cities with
public transport infrastructure diculties and high depend-
ence on private vehicles. is is done in such away that the
possibility of creating solutions together and proposing new
dynamics within the city empowers citizens and makes them
part of the construction of social welfare. e ICTs here are
important because they allow acloser relationship with the
user and allow them to have all the necessary information to
make decisions about the means of transport and routes they
can take to reach acertain destination (Falco et al., 2018). Ka-
zhamiakin et al. integrate an element to these advances with
their interest in generating changes in the behaviour of users
from the integration of playful systems typical of gamication
trends, sustaining how smart cities not only present an intel-
ligent technical infrastructure, but also that its citizens need
to articulate with these developments (Kazhamiakin, Marconi,
Martinelli, Pistore, &Valetto, 2016). Oskarbski &Kaszubows-
ki are concerned with digital tools that help with the integra-
tion of the cargo transport system that allows the generation
of solutions based on trac modelling to plan routes, moni-
tor in real-time and forecast congestion and trac incidents.
Further possibilities include dynamic vehicle routeing with
real-time information and aprogramming guide system and
parking for cargo vehicles, and use of panels with messages
on the tracks (Oskarbski &Kaszubowski, 2016).
Finally, eCOMPASS, innovation proposed by Dibbelt et al.
is focused on eciency in mobility as well as in the reduction
of emissions from vehicles, including algorithms that help
dene routes adjusted to real-time data and user experience
(knowledge of the city and route preferences) (Dibbelt et al.,
2017).
4.4 Gaps in the literature
− Within the studies consulted there is adiculty in inte-
grating indicators, ways of measuring the behaviour of
mobility in cities, with citizen participation and the way
in which decisions are made about the use of public space
through digital technologies. is means that the investi-
gations reported in the initial part show adesign of solu-
tions that does not manage to integrate into its analysis
important social variables, such as participation and the
contextualized use of means of transport.
−
We did not nd arepresentative interest in generating
analyses related to the size and complexities of cities, so
that the concept of compact city is not included as away of
explaining the urban system and its mobility in aholistic
way that not only depends on of urban transport but of
the citizen exercise of the use of public space.
− ere are also no studies that are questioning urban mo-
bility through the displacement of the labour force from
various points and proposing solutions such as the mas-
sication of telework and its adaptation to urban dynamics.
− Although the recommendations of organizations such as
the UN (2015) on the analysis of urban mobility are related
to broadening the horizon towards various variables typi-
cal of life in the city, still most of the articles found focus
on urban transport and in its modalities excluding other
complementary views. In the end, the articles that open
their explanation towards participation, administration
and decision-making on the public, environmental pollu-
tion, among others, from the use of digital technologies,
are important.
5. CONCLUSIONS
Based on the articles reviewed and the analysis of urban
mobility, the studies undertaken by Italy—especially uni-
versity centres such as the Politecnico di Milano where the
convergence of ICT and the concept of sustainability in solv-
ing problems—are noteworthy. Urban mobility is aline of
research that takes strength in the academic eld from vari-
ous disciplines.
With the presentation of the three axes of analysis dis-
cussed in this document, the main academic tendencies
and developments on urban mobility were identied. In
the rst analysis on modelling and simulation of vehicular
trac, the rationale lies in the use of tools that allow the
measurement and prediction of the vehicular behaviour of
cities through dierent means that have evolved as impor-
tant advances occur in ICT and in the methodologies for
the processing of large databases. ese studies show that
there is awide applicability of the data provided by mobile
phone users or obtained through sensors that can provide
data for urban planning purposes, tourism management
and to inform citizens to reduce ineciencies of urban sys-
tems. However, these solutions seem scattered and with-
out an evident articulation with holistic sustainable city
projects that imply the use of ICT with along-range vision
and without necessarily evidencing their positive impact
on urban mobility.
In the following axes, the academic studies are addressed
in the analysis of the incursion of ICT in urban mobility. It
is possible to observe how the meaning of ICT permutes to-
wards abroader understanding of social transformations
typical of smart cities. So much so that discussions based on
systems theory are included for the understanding of socio-
technical phenomena and to give abroader framework of
analysis where urban mobility is an activity that is related
to others in the city system.
In the last axes on alternatives that have been designed
for the evolution of urban mobility, there is awide eld of
work that deals with convergent solutions according to the
specic contexts of cities and the prole of their users. is
type of proposal allows us to understand how urban mobility
requires heterogeneous approaches in which participation
and co-operativism among citizens facilitates the generation
of innovative solutions. In addition, it is an academic trend
that integrates sustainable urban mobility management al-
ternatives where ICT not only provides the means to obtain
data, but also helps to generate acoherent integration be-
tween the traditional systems of cities, new digital systems,
and the recognition of the user as an essential actor in the
construction of sustainable solutions.
Conicts of Interest:
e authors declare no conict of interest.
REFERENCES
Aguiléra, A., &Grébert, J. (2014). Passenger transport mode
share in cities: Exploration of actual and future trends with
aworldwide survey. International Journal of Automotive
Technology and Management, 14(3–4), 203–216.
https://doi.org/10.1504/IJATM.2014.065290
Alam, M., Fernandes, B., Almeida, J., Ferreira, J., &Fonseca,J.
(2017). Integration of smart parking in distributed ITS
architecture. In ICOSST 2016 - 2016 International Conference
on Open Source Systems and Technologies, Proceedings
(pp.84–88). https://doi.org/10.1109/ICOSST.2016.7838582
Andaloro, L., Napoli, G., Sergi, F., Micari, S., Agnello, G.,
&Antonucci, V. (2015). Development of anew concept electric
vehicle for last mile transportations. World Electric Vehicle
Journal, 7(3), 342–348.
Arena, M., Azzone, G., Colorni, A., Conte, A., Luè, A.,
&Nocerino,R. (2015). Service design in electric vehicle sharing:
Evidence from Italy. IET Intelligent Transport Systems, 9(2),
145–155. https://doi.org/10.1049/iet-its.2013.0034

Transactions on Transport Sciences | Vol. 3/20219
Arimah, B. (2017). Infrastructure as aCatalyst for the Prosperity of
African Cities. In Procedia Engineering (Vol. 198, pp. 245–266).
https://doi.org/10.1016/j.proeng.2017.07.159
Arsenio, E., Dias, J. V., Lopes, S. A., &Pereira, H. I. (2017).
Assessing the market potential of electric bicycles and ICT
for low carbon school travel: Acase study in the smart city
of Águeda. In Transportation Research Procedia (Vol. 26,
pp.119–130). https://doi.org/10.1016/j.trpro.2017.07.013
Battarra, R., Zucaro, F., &Tremiterra, M. R. (2017). Smart mobility:
An evaluation method to audit Italian cities. In 5th IEEE
International Conference on Models and Technologies for
Intelligent Transportation Systems, MT-ITS 2017 - Proceedings
(pp. 421–426). https://doi.org/10.1109/MTITS.2017.8005709
Batty, M., Axhausen, K. W., Giannotti, F., Pozdnoukhov, A.,
Bazzani, A., Wachowicz, M., … Portugali, Y. (2012). Smart cities
of the future. European Physical Journal: Special Topics, 214(1),
481–518. https://doi.org/10.1140/epjst/e2012-01703-3
Batur, İ., &Koç, M. (2017). Travel Demand Management (TDM)
case study for social behavioral change towards sustainable
urban transportation in Istanbul. Cities, 69, 20–35.
https://doi.org/10.1016/j.cities.2017.05.017
Calabrese, F., Colonna, M., Lovisolo, P., Parata, D., &Ratti, C.
(2011). Real-Time Urban Monitoring Using Cell Phones: ACase
Study in Rome. IEEE Transactions on Intelligent Transportation
Systems, 12(1), 141–151. https://doi.org/10.1109/
TITS.2010.2074196
Camagni, R., Gibelli, M. C., &Rigamonti, P. (2002). Urban mobility
and urban form: the social and environmental costs of dierent
patterns of urban expansion. Ecological Economics, 40(2),
199–216. https://doi.org/10.1016/S0921-8009(01)00254-3
Cárdenas-Benítez, N., Aquino-Santos, R., Magaña-Espinoza, P.,
Aguilar-Velazco, J., Edwards-Block, A., &Cass, A. M. (2016).
Trac congestion detection system through connected
vehicles and big data. Sensors (Switzerland), 16(5).
https://doi.org/10.3390/s16050599
Citrigno, S., Graziano, S., Lupia, F., &Saccà, D. (2014). Smart
applications for smart city: Acontribution to innovation. In
CEUR Workshop Proceedings (Vol. 1133, pp. 365–366).
Cohen-Blankshtain, G., &Rotem-Mindali, O. (2016). Key research
themes on ICT and sustainable urban mobility. International
Journal of Sustainable Transportation, 10(1), 9–17.
https://doi.org/10.1080/15568318.2013.820994
Costa, P. B., Morais, G. C., &Bertolde, A. I. (2017). Urban Mobility
Indexes: ABrief Review of the Literature. Transportation
Research Procedia, 25, 3645–3655. https://doi.org/10.1016/J.
TRPRO.2017.05.330
D’Andrea, E., &Marcelloni, F. (2017). Detection of trac
congestion and incidents from GPS trace analysis. Expert
Systems with Applications, 73, 43–56. https://doi.org/10.1016/j.
eswa.2016.12.018
Daganzo, C. F. (2007). Urban gridlock: Macroscopic modeling
and mitigation approaches. Transportation Research Part
B- Methodological, 41(1), 49–62. https://doi.org/10.1016/j.
trb.2006.03.001
Daganzo, C. F., Gayah, V. V, &Gonzales, E. J. (2011). Macroscopic
relations of urban trac variables: Bifurcations, multivaluedness
and instability. Transportation Research Part B- Methodological,
45(1), 278–288. https://doi.org/10.1016/j.trb.2010.06.006
Daly, H. (1990). Toward some operational principles of Sustainable
Development. Ecological Economics, 2, 1–6.
Di Martino, S., &Rossi, S. (2016). An Architecture for aMobility
Recommender System in Smart Cities. In Procedia Computer
Science (Vol. 58, pp. 425–430). https://doi.org/10.1016/j.
procs.2016.09.066
Dibbelt, J., Kehagias, D., Pantziou, G., Gavalas, D.,
Konstantopoulos, C., Wagner, D., … Zaroliagis, C. (2017). Eco-
aware vehicle routing in urban environments. In Proceedings-
IEEE Symposium on Computers and Communications
(pp.208–213). https://doi.org/10.1109/ISCC.2017.8024531
Dimitrakopoulos, G. (2017). Sustainable mobility leveraging on 5G
mobile communication infrastructures in the context of smart
city operations. Evolving Systems, 8(2), 157–166.
https://doi.org/10.1007/s12530-016-9166-4
Djahel, S., Doolan, R., Muntean, G.-M., &Murphy, J. (2015).
ACommunications-Oriented Perspective on Trac
Management Systems for Smart Cities: Challenges and
Innovative Approaches. IEEE Communications Surveys
and Tutorials, 17(1), 125–151. https://doi.org/10.1109/
COMST.2014.2339817
Elhatri, C., Tahifa, M., &Boumhidi, J. (2017). Extreme Learning
Machine-Based Trac Incidents Detection with Domain
Adaptation Transfer Learning. Journal of Intelligent Systems,
26(4), 601–612. https://doi.org/10.1515/jisys-2016-0028
Falco, E., Malavolta, I., Radzimski, A., Ruberto, S., Iovino, L.,
&Gallo, F. (2018). Smart City L’Aquila: An Application of the
“Infostructure” Approach to Public Urban Mobility in aPost-
Disaster Context. Journal of Urban Technology, 25(1), 99–121.
https://doi.org/10.1080/10630732.2017.1362901
Ferreira, J., Fonseca, J., Gomes, D., Barraca, J., Fernandes, B.,
Runo, J., … Aguiar, R. (2017). PASMO: An open living lab
for cooperative ITS and smart regions. In 2017 International
Smart Cities Conference, ISC2 2017. https://doi.org/10.1109/
ISC2.2017.8090866
Fistola, R., Raimondo, M., &Rocca, R. A. L. (2017). e smart
city and mobility: e functional polarization of urban
ow. In 5th IEEE International Conference on Models and
Technologies for Intelligent Transportation Systems, MT-ITS
2017 - Proceedings (pp. 532–537). https://doi.org/10.1109/
MTITS.2017.8005730
Geroliminis, N., &Daganzo, C. F. (2008). Existence of urban-scale
macroscopic fundamental diagrams: Some experimental
ndings. Transportation Research Part B- Methodological, 42(9),
759–770. https://doi.org/10.1016/j.trb.2008.02.002
Himmel, S., Zaunbrecher, B. S., Ziee, M., &Beutel, M. C. (2016).
Chances for urban electromobility: Field test of intermodal
travel system and eect on usage intention. Lecture Notes in
Computer Science (including subseries Lecture Notes in Articial
Intelligence and Lecture Notes in Bioinformatics) (Vol. 9747).
https://doi.org/10.1007/978-3-319-40355-7_45
Jensen, O. B. (2013). Staging mobilities. Staging Mobilities
(1stEditio). London. https://doi.org/10.4324/9780203070062
Kahlen, M., Lee, T. Y., Ketter, W., &Gupta, A. (2018). Optimal
Prepositioning and Fleet Sizing to Maximize Prots for One-
Way Transportation Companies. In ICIS 2017: Transforming
Society with Digital Innovation.
Kamargianni, M., Li, W., Matyas, M., &Schäfer, A. (2016).
ACritical Review of New Mobility Services for Urban
Transport. Transportation Research Procedia, 14, 3294–3303.
https://doi.org/10.1016/J.TRPRO.2016.05.277
Kazhamiakin, R., Marconi, A., Martinelli, A., Pistore, M.,
&Valetto, G. (2016). Agamication framework for the long-
term engagement of smart citizens. In IEEE 2nd International
Smart Cities Conference: Improving the Citizens Quality
of Life, ISC2 2016 - Proceedings. https://doi.org/10.1109/
ISC2.2016.07580746
Kourtit, K., Nijkamp, P., Franklin, R., &Rodríguez-Pose, A. (2014).
Ablueprint for strategic urban research: e urban piazza.
Town Planning Review, 85(1), 97–126. https://doi.org/10.3828/
tpr.2014.7
Kozievitch, N. P., Gomes, L. C., Gadda, T. M. C., Fonseca, K. V. O.,
&Akbar, M. (2016). Analyzing the acoustic urban environment:
Ageofencing-centered approach in the Curitiba metropolitan
region, Brazil. In Smartgreens 2016 - Proceedings of the 5th
International Conference on Smart Cities and Green ICT Systems
(pp. 78–85).
Lenz, B., &Heinrichs, D. (2017). What Can We Learn from Smart
Urban Mobility Technologies? IEEE Pervasive Computing, 16(2),
84–86. https://doi.org/10.1109/MPRV.2017.27

Transactions on Transport Sciences | Vol. 3/202110
Leone, G. R., Moroni, D., Pieri, G., Petracca, M., Salvetti, O.,
Azzarà, A., &Marino, F. (2017). An intelligent cooperative
visual sensor network for urban mobility. Sensors (Switzerland),
17(11). https://doi.org/10.3390/s17112588
Li, F. Z., Fisher, K. J., Brownson, R. C., &Bosworth, M. (2005).
Multilevel modelling of built environment characteristics
related to neighbourhood walking activity in older adults.
Journal of Epidemiology and Community Health, 59(7), 558–564.
https://doi.org/10.1136/jech.2004.028399
Li, N., Chan, D., Hsu, K., Fu, Z., &Mao, Q. (2017). ICT-Enabled
Cross-Cultural Education in Sustainable Urbanization.
InCongress on Computing in Civil Engineering, Proceedings
(Vol.2017-June, pp. 43–50).
Lopez-Carreiro, I., &Monzon, A. (2018). Evaluating
sustainability and innovation of mobility patterns in Spanish
cities. Analysis by size and urban typology. Sustainable
Cities and Society, 38, 684–696. https://doi.org/10.1016/j.
scs.2018.01.029
López Bernal, O. (2004). La sustentabilidad urbana. Revista
Bitácora Urbano Territorial, 1(8), 8–14. Retrieved from
http://www.redalyc.org/articulo.oa?id=74800801
Marchetta, P., Natale, E., Pescape, A., Salvi, A., &Santini, S.
(2016). Amap-based platform for smart mobility services.
In Proceedings - IEEE Symposium on Computers and
Communications (Vol. 2016-Febru, pp. 19–24).
https://doi.org/10.1109/ISCC.2015.7405448
Marletto, G. (2014). Car and the city: Socio-technical transition
pathways to 2030. TECHNOLOGICAL FORECASTING AND
SOCIAL CHANGE, 87, 164–178. https://doi.org/10.1016/j.
techfore.2013.12.013
Mataix, C. (2010). Movilidad Urbana Sostenible: un reto energético
y ambiental. Madrid: Obra Social Caja Madrid.
Mingrone, L., Pignataro, G., &Roscia, M. (2015). Smart urban
electric transport system: An innovative real model. In 2015
International Conference on Renewable Energy Research and
Applications, ICRERA 2015 (pp. 1457–1462).
https://doi.org/10.1109/ICRERA.2015.7418649
Motta, G., Sacco, D., Ma, T., You, L., &Liu, K. (2015). Personal
mobility service system in urban areas: e IRMA project.
InProceedings - 9th IEEE International Symposium on Service-
Oriented System Engineering, IEEE SOSE 2015 (Vol. 30,
pp.88–97). https://doi.org/10.1109/SOSE.2015.15
Oskarbski, J., &Kaszubowski, D. (2016). Potential for ITS/ICT
Solutions in Urban Freight Management. In Transportation
Research Procedia (Vol. 16, pp. 433–448).
https://doi.org/10.1016/j.trpro.2016.11.041
Passalacqua, A. (2014). Reluctant capitals: Transport mobility
and tramways in London and Paris 1830-1950. Town Planning
Review, 85(2), 203–216. https://doi.org/10.3828/tpr.2014.13
Pitt, J., Diaconescu, A., &Bourazeri, A. (2017). Democratisation
of the SmartGrid and the active participation of prosumers.
In IEEE International Symposium on Industrial Electronics
(pp.1707–1714). https://doi.org/10.1109/ISIE.2017.8001505
Pronello, C., Duboz, A., &Rappazzo, V. (2017). Towards smarter
urban mobility: Willingness to pay for an advanced traveller
information system in Lyon. Sustainability (Switzerland), 9(10).
https://doi.org/10.3390/su9101690
Rakkesh, S. T., Weerasinghe, A. R., &Ranasinghe, R. A. C.
(2016). Trac light optimization solutions using multimodal,
distributed and adaptive approaches. In 15th International
Conference on Advances in ICT for Emerging Regions, ICTer
2015 - Conference Proceedings (pp. 220–225).
https://doi.org/10.1109/ICTER.2015.7377692
Rehm, S.-V., Faber, A., &Goel, L. (2018). Visualizing Platform Hubs
of Smart City Mobility Business Ecosystems. In ICIS 2017:
Transforming Society with Digital Innovation.
Schwanen, T. (2015). Beyond instrument: Smartphone app and
sustainable mobility. European Journal of Transport and
Infrastructure Research, 15(4), 675–690.
Serna, A., Gerrikagoitia, J. K., Bernabé, U., &Ruiz, T. (2017).
Sustainability analysis on Urban Mobility based on Social
Media content. In Transportation Research Procedia (Vol. 24).
https://doi.org/10.1016/j.trpro.2017.05.059
Severengiz, M., Seidel, J., Steingrímsson, J. G., &Seliger,G.
(2015). Enhancing technological innovation with the
implementation of asustainable manufacturing community. In
Procedia CIRP (Vol. 26, pp. 52–57). https://doi.org/10.1016/j.
procir.2014.07.177
Shields, A., Doody, P., &Scully, T. (2017). Application of
multiple change point detection methods to large urban
telecommunication networks. In 2017 28th Irish Signals and
Systems Conference, ISSC 2017. https://doi.org/10.1109/
ISSC.2017.7983608
Snellen, D., &de Hollander, G. (2017). ICT’S change transport
and mobility: mind the policy gap! Transportation Research
Procedia, 26(Emerging technologies and models for
transport and mobility), 3–12. https://doi.org/10.1016/j.
trpro.2017.07.003
Steenbruggen, J., Tranos, E., &Nijkamp, P. (2015). Data
from mobile phone operators: Atool for smarter cities?
Telecommunications Policy, 39(3–4), 335–346.
https://doi.org/10.1016/j.telpol.2014.04.001
Tadis, P., Macedo, E., Coelho, M. C., Niculescu, M. C., Voicu, A.,
Barbu, C., … Bandeira, J. (2017). Exploring the impact of ICT on
urban mobility in heterogenic regions. In Transportation Research
Procedia (Vol. 27, pp. 309–316). https://doi.org/10.1016/j.
trpro.2017.12.030
Tyeld, D., &Zuev, D. (2018). Stasis, dynamism and emergence of
the e-mobility system in China: Apower relational perspective.
Technological Forecasting and Social Change, 126, 259–270.
https://doi.org/10.1016/j.techfore.2017.09.006
United Nations. (2015). Sustainable Development Goals.
Retrieved 12 March 2017, from http://www.un.org/
sustainabledevelopment/sustainable-development-goals/
United Nations. (2017). World Population Prospects: e 2017
Revision, Key Findings and Advance Tables. (No. ESA/P/
WP/248). Retrieved from https://esa.un.org/unpd/wpp/
Publications/Files/WPP2017_KeyFindings.pdf
Vidovic, K., Mandzuka, S., &Brcic, D. (2017). Estimation of urban
mobility using public mobile network. In Proceedings Elmar -
International Symposium Electronics in Marine (Vol. 2017-Septe,
pp. 21–24). https://doi.org/10.23919/ELMAR.2017.8124426
Zhang, X., &Li, H. (2018). Urban resilience and urban
sustainability: What we know and what do not know? Cities,
72, 141–148. https://doi.org/10.1016/j.cities.2017.08.009
Zheng, Y., Rajasegarar, S., &Leckie, C. (2015). Parking availability
prediction for sensor-enabled car parks in smart cities. In 2015
IEEE 10th International Conference on Intelligent Sensors,
Sensor Networks and Information Processing, ISSNIP 2015.
https://doi.org/10.1109/ISSNIP.2015.7106902