Systematic Literature Review on Smart Mobility: A Framework for Future “Quantitative” Developments

Abstract

This work aims to reorganize theoretical and empirical research on smart mobility through the systematic literature review approach. The research goal is to reach an extended and shared definition of smart mobility using the cluster analysis. The article provides a summary of the state of the art that can have broader impacts in determining new angles for approaching research. In particular, the results will be a reference for future quantitative developments for the authors who are working on the construction of a territorial measurement model of the smartness degree, helping them in identifying performance indicators consistent with the definition proposed.

Keywords

smart mobility and transport smart transition urban and territorial models sustainability planning conceptual framework systematic literature review cluster analysis

Introduction

This research is aimed at the study of the role played by smart mobility in the planning field through a process of literature review in order to propose a new definition of smart mobility. Below, we present a synthetic theoretical framework on the general concept and on the components in which the smart city has been deconstructed with a focus on mobility sector. This framework is important to anticipate the benefits of smart mobility, what are the problems that surround the concept, and why there is necessary to clarify the term and definition, especially in the planning field with reference to sustainability objectives.

Smart city involves the integration of emerging ideas and technologies in order to produce city’s evolution prospects (Song 2005; Kunzmann 2014) and sustainable effects in environmental, economic, and social spheres (Kurushina and Kurushina 2014; Kitchin 2015). In order to explore the concept, Cohen (2015) defined three generations of Smart Cities (Kamolov and Kandalintseva 2020):

– Smart Cities 1.0, as technology-driven cities where technology companies providing solutions and services are the main beneficiaries (Trencher 2019; Garau, Desogus, and Zamperlin 2020).

– Smart Cities 2.0, as technology-enabled managed cities where authorities are increasingly focusing on technological solutions that help to improve the quality of life of the population.

– Smart Cities 3.0, as highly intelligent integrated cities that are characterized by a high degree of interaction between the government and the population and by civil participation in urban processes (Alexopoulos et al. 2019; Szarek-Iwaniuk and Senetra 2020).

Our smart city concept is very close to the third generation because of its focus on “citizen co-creation” that is one of the most important perspective in the mobility sector. The goal that unites similar models of Smart Cities is the desire to improve the well-being conditions of society (Caragliu, Del Bo, and Nijkamp 2011; Ballas 2013) involving civil participation in an integrated research of local problems (Chourabi et al. 2012; Joshi, Saxena, and Godbole 2016; Fernandez-Anez, Fernández-Güell, and Giffinger 2018).

To solve the complexity of the concept, a widespread practice in the literature considers the deconstruction of the smart city (Wolfram 2012; Fontana 2014). In particular, we refer to a study (Anthopoulos 2015) aimed at understanding the smart city concept on the basis of a revision of the main literature on the subject (Giffinger et al. 2007; Chourabi et al. 2012; Desouza and Flanery 2013; Lee, Phaal, and Lee 2013; Neirotti et al. 2014; Piro et al. 2014; Wey and Hsu 2014; Yigitcanlar and Lee 2014). The author leads to the definition of a conceptual framework consisting of application domains. Mobility is one of the fundamental factors among the relevant domains identified. The author highlights how transportation concerns the utilization of the information and communication technology (ICT) for transportation management as well as intelligent transportation products and mobility in general.

According to the literature, smart mobility plans to implement actions and reduce pollution and waste (Bueno-Delgado et al. 2019; Litwin et al. 2019; Yenneti et al. 2019), while increasing transport efficiency (Li et al. 2019). For this reason, smart mobility is deeply connected to the performance of sustainable mobility, contributing to improving the quality of life (Olaverri-Monreal 2016). It is based on the creation of economies of scale on the movement of people and goods (Muratori et al. 2020) and aims to optimize and save time and costs using technology. In particular, a natural impact of the digital revolution concerns the deep transformation of the world of logistics (Mazzarino and Rubini 2019). However, the improvement of transport and the reduction of traffic accidents are some of the current key challenges that planning models try to face.

In summary, the challenges for Smart Sustainable Mobility system are the following:

– action on the mobility demand, to eliminate unnecessary travels and make them easier and more accessible;

– management of mobility flows (Ning et al. 2017), to reduce congestion, downtime, inefficiencies, and risks; and

– innovative infrastructures design, in order to make them more interactive and functional through the use of suitable technologies.

Planning mobility systems means reusing what already exists in a different way in order to have more rational and effective networks with respect to the needs and emergencies of the territory, giving more useful and personalized services to citizens and businesses. Mobility conditions urban and territorial development in terms of production, resources efficiency, job opportunities, new investors, and tourism (Sharida, Hamdan, and Mukhtar 2020). However, if on one side contemporary urban lifestyles and business practices are increasingly dependent on mobility, on the other side, the negative impacts of mobility on natural and social environments are growing dramatically (Bertolini 2012). So, sustainable solutions are required as key elements for modern transportation systems (Jiménez Herrero 2011; Pinna, Masala, and Garau 2017) to mitigate the increasing demands for mobility and the potentially negative environmental, economic, and social impacts (Seuwou, Ubakanma, and Banissi 2020). Mobility cannot be considered smart if it is not sustainable. In fact, smart mobility is an integrated system comprised of several projects and actions all aimed at sustainability in urban development (Pinna, Masala, and Garau 2017). This specification is necessary to clarify that when we will talk about smart mobility below, we will consider the sustainability paradigm as assumed.

The object of our study is the “Smart Sustainable Mobility” system as the synthesis of the concept of smart mobility and the sustainability goals in order to clarify its use and description by reviewing the international literature. In particular, we will explain the methodology used to conduct the systematic literature review (SLR) and the cluster analysis (CA; second section). We will describe the results from the SLR (third section). Based on the application of the CA technique on a group of selected literature, we will propose a shared definition of smart mobility (fourth section) which will be deepened in the discussion (fifth section). Finally, in the conclusion (sixth section), reference will be made to future operational research to be developed starting from data obtained.

Method

We conducted a SLR and a CA according to the process described below in order to analyze the evolution of the concept of smart mobility and highlight current research trends on the topic.

SLR

SLR is a methodology used to find and aggregate all relevant studies about a specific research question or topic of interest (Hamad and Salim 2014). Unlike the revision of the literature for a narrative or descriptive scope, this methodology involves documenting all the procedures undertaken (Ginieis, Sánchez-Rebull, and Campa-Planas 2012). Sharing what is claimed by some authors (Denyer and Neely 2004), this section describes the steps of the methodology used (Anwer and Aftab 2017). They are reported below.

The first step is define research questions. It consists of delimiting research issues and describing the general objective. It is important to formulate the research questions with care to avoid missing relevant studies or collecting a potentially biased result set.

Having identified the general topic, in the second step called find keywords to form query string, we identify the keywords to be used to form the query string. We start from the research questions and focus on terms that define the studies that we want to examine.

The research space to obtain the data is defined in the third step called define research space to get data.

In the fourth step called set criteria to include/exclude papers, we define the practical selection criteria of the documents that are temporal criterion, types of documents, language, and stage of publication.

The fifth step is extract literature using criteria. It aims to extract the literature using the set of inclusion and exclusion criteria of the document collection defined in the previous step.

The next step is access study quality. It concerns the analysis of the quality of the studies conducted.

The seventh step is synthesize required data and involves collecting, organizing, and summarizing the results of the included primary studies. With this objective, we chose the descriptive synthesis by identifying different types of classification of extracted information about the studies. The types of classification are the following:

– geographical, in order to highlight the spatial distribution of studies conducted on the research topic;

– based on the source type, in order to highlight the most common ways of publishing the material produced by the research;

– temporal, in order to highlight the evolution of research in terms of the amount of documents produced; and

– based on subject area, in order to highlight the field of application of the most current lines of research on the topic.

In the last step called document results and outcomes, we further the research by revising the selected literature. The selected literature is a group of publications chosen among those belonging to the wide documents collection described in the previous section. For the analysis, we use the support of the VOSviewer (1.6.14) software, a tool for creating maps of terms, which is particularly useful while working with a large quantity of data (Siderska and Jadaan 2018). In order to investigate current research trends and to answer the second research question regarding the topic of the research, the unit of analysis chosen is the keywords reported in the selected publications. The type of analysis is that of co-occurrence, and the counting method is full counting.

CA

The CA allows for the identification of a minimum number of groups so that the elements belonging to each of them are more similar to each other than those of other groups. The goal is essentially to bring together heterogeneous units—the 105 keywords—in several subsets—the clusters—in the most homogeneous and exhaustive form possible. From a general point of view, CA allows achievement of the following results (Fabbris 1990):

– typological research to identify groups of statistical units with distinctive characteristics that highlight the physiognomy of the observed system;

– the definition of homogeneous classes, within which it can be assumed that the members are mutually surrogate (Green, Frank, and Robinson 1967); and

– the generation of research hypotheses, in fact, in order to carry out a CA, it is not necessary to have any interpretative model in mind.

With regard to the last result, the CA, unlike other multivariate statistical techniques, does not make any “a priori” assumptions on the existing fundamental types that can characterize the collective starting point. It was precisely for this characteristic that we chose this technique. In our research, the exploratory role of the so-called latent structures was decisive in order to infer the most probable partition without external conditioning. CA is a purely empirical classification method, and it is primarily an inductive technique.

Results from SLR

As anticipated, the research issue is the Smart Sustainable Mobility system in order to highlight the current research trend lines related to the concept of smart mobility and subsequently classify them in clusters.

In the first step “define research question,” we structured the question that is as general as possible in order to select all relevant information to measure the stated objectives. The research aims to analyze and evaluate the literature by summarizing the sources and showing the state of current knowledge in relation to the following research questions (RQs):

RQ1: “Which are the main lines of research on smart mobility?”

RQ2: “Is it possible to give a definition of smart mobility that includes these aspects?”

In the second step “find keywords to form query string,” the keywords identified are “smart mobility,” “smart mobility system,” “smart transport,” “smart transport system,” and “intelligent transportation system.” The query string is created using the Boolean operator “OR.” The resulting query string (QS) is the following:

QS: “smart mobility” OR “smart mobility system” OR “smart transport” OR “smart transport system” OR “intelligent transportation system”.

The query string is applied to the fields article title, abstract, and keywords.

In the third step “define research space to get data,” we plan to access the Scopus database. Although the use of a single source of research may appear to be limiting, as will emerge later, we believe that the selected publications constitute a sufficiently exhaustive starting point in order to distinguish the guidelines within which place future research.

In the fourth step “set criteria to include/exclude papers,” we consider the criteria mentioned in the Method section. The first criterion is temporal and concerns the date of publication. In particular, the document search is limited to publications subsequent to January 1, 2007, the year in which Giffinger proposed the definition of smart city. Furthermore, documents of all types have been included—conference papers, articles, conference reviews, book chapters, reviews, and books—provided they are written in English and at the final stage of publication, thus excluding the articles in press. We chose to only review peer-reviewed scientific published articles in our analysis. While acknowledging the importance of gray literature (reports, working papers, government documents, white papers, and evaluations), they are not included because the standard of quality review and production of this literature can vary considerably. Furthermore, gray literature may be difficult to discover, access, and evaluate because it is produced by organizations outside of the traditional commercial or academic publishing and distribution channels such as Scopus database.

In the fifth step “extract literature using criteria,” applying the illustrated criteria, the Scopus search engine has traced 11,204 records published since 2007.

In the sixth step “access study quality,” we statistically analyzed the keywords indicated by the authors and studied the contents of the abstracts as the only section available for all documents, even for no open-access publications, to evaluate their consistency with our research objective.

In the seventh step “synthesize required data,” for each type of classification of the information on the literature, we describe the relative quantitative data in order to represent the results obtained.

First, we analyze the geographical distribution of research expressed as the number of documents published in the period 2007–2020 by authors affiliated to the specific country/territory. It is clear that the smart mobility topic is particularly widespread in Europe. Germany, United Kingdom, France, and Italy boast the largest number of publications. The topic is also widespread in the Asian (China), American (United States), and African continent (Egypt). The initial analysis on this group of documents showed that the authors follow specific research lines that suit to the particular conditions of urbanization, population density, and degree of computerization of the territories.

Beyond geographical distribution, the most common types of sources are journals (46.29 percent) and conference proceedings (44.16 percent). Indirect conclusions can be drawn from this information. The publication of peer-reviewed research shows reliability. The organization of seminars and conferences, demonstrated by the conference proceedings, instead measures the actuality and attention shown for the topic. In support of this, the distribution of the number of publications per year shows the evident increase of interest in the topic among academics between 2007 and 2019. Compared to the total number of publications, the number of documents published between 2017 and 2019 represents approximately 47 percent of the sample. Figure 1 shows the evolution graph of the number of publications per year.

Figure 1.

Evolution graph of the distribution of the number of publications per year.

The most popular subjects covered by the publications are computer science, engineering, mathematics, and social sciences. This identification provides a partial answer to the first RQ which is confirmed by examining the contents of the publications. In particular, it is evident that the main research lines pertain to the study of processes that involve interaction with computer data in order to store and communicate digital information (computer science) as well as to deduce theoretical reference models (mathematics) that have strategic importance in the planning, design, and management of mobility systems (engineering). Important cross-disciplinarily issues are the objective of environmental sustainability as well as satisfaction of users’ requests and needs (social science).

8. In the eighth step “document results and outcomes,” we filter the most significant and relevant documents relating to the period after January 1, 2018 (approximately 250 documents). The time frame, although very limited, made it possible to obtain a substantial number of documents while ensuring the analysis of the most current and interesting publications in light of the close correlation with the technological aspects referred to previously.

Of the 5,236 keywords, 105 meet the minimum threshold of seven co-occurrences. For each of the selected keywords, the total strength of the co-occurrence links with other keywords was calculated. Finally, we select keywords with the greatest total link strength. Table 1 shows the number of co-occurrences and links of the twenty-five keywords with the most connections.

Table 1.

Co-occurrences and Links of the Twenty-five Keywords with the Most Connections.

ID	Keyword	Occurrences	Total Link Strength
1	smart city	175	211
2	smart cities	124	167
3	mobility	117	161
4	internet of things	92	128
5	iot	71	115
6	smart mobility	120	106
7	big data	38	61
8	urban mobility	43	59
9	security	33	56
10	fog computing	29	46
11	cloud computing	22	40
12	smart grid	30	38
13	sustainability	29	38
14	electric vehicles	32	35
15	routing	17	35
16	transportation	15	32
17	internet of things (iot)	28	31
18	machine learning	30	31
19	privacy	17	31
20	vanet	19	29
21	blockchain	18	28
22	data mining	13	28
23	electric vehicle	18	28
24	manet	13	28
25	aodv	10	26

Regarding the documentation of the results, we build a conceptual map of terms in order to summarize and schematize the smart mobility topic by building networks between the different concepts emerged. Figure 2 shows the existing relationships between those keywords.

Figure 2.

Map of research trends developed using the VOSviewer software and based on co-occurrence of the author’s keywords referring to the selected literature.

In particular, the visualization of data in graphic form makes the information more immediately readable and the presentation of the results synthetic. The nodes in the map coincide with the keywords reported in the analyzed documents, while the connections between the nodes represent their mutual co-occurrence in small groups of publications. The central part of the map includes the most frequently occurring keywords. Also, the size of the nodes representing each of the appearing terms and the font size in which the name of a given node is written correspond to the frequency of co-occurrence of a given term (Gudanowska 2017). As regard the connections, the stronger the connection, the higher the frequency of the co-occurrence.

Results from CA

While analyzing the map, it can be observed that the resulting network is dense and characterized by numerous connections. In light of this complexity and in order to highlight the emerging aspects and their mutual relationships, we apply the CA technique that allows the construction of automatic classification systems (Jardine and Sibson 1971).

The support of the VOSviewer software, in addition to the development of the trend map of the search based on the co-occurrence of the keywords, allows identification of clusters, groups in which the most frequently occurring keywords are found together.

In particular, the seven clusters identified are reported in detail in Table 2. We have labeled each of them with a macro-theme that summarizes emerging issues including the recurring keywords. Recognizing how the technological advancement represents a common topic to the totality of the documents from the reading of the abstracts, the macro-themes focus on more general conceptual aspects in order to reach subsequently a definition of smart mobility as shared as possible.

Although the data shown in Table 2 refer to documents published starting from 2018, we have applied a further selection criterion on the documents with the sole purpose of associating the macro-theme with each cluster. In particular, we deepen the study of publications after January 1, 2019, published in journals classified by the National Agency for the Evaluation of Universities and Research Institutes as scientific or class A for Area 08—civil engineering and architecture—in the period 2018–2020. The application of the criterion is aimed at including the most recent documents and excluding documents from different disciplinary sectors or those that are too far from the urban and landscape planning and design sector. A total of 102 articles published in 30 journals (17 scientific journals and 13 class A journals) were filtered out of the total. We have identified the macro-themes associated with clusters by analyzing the content of the 102 articles and validate them by comparing the opinions of each member of the review team.

Table 2.

Clusters Identified by the Analysis of 105 Keywords that Meet the Minimum Threshold of 7 Co-occurrences by Using the VOSviewer Software Referring to the Selected Literature.

Cluster	Thematic Area	Items	Keywords
1	Computing for urban safety and efficiency	18	(1) autonomous vehicles; (2) blockchain; (3) cloud computing; (4) edge computing; (5) fog computing; (6) governance; (7) intelligent transport systems; (8) internet of things; (9) internet of things (iot); (10) mobile cloud computing; (11) mobility as a service; (12) open data; (13) privacy; (14) reliability; (15) security; (16) shared mobility; (17) smart contracts; (18) trust
2	Solutions for reducing energy consumption and pollution	18	(1) aodv; (2) clustering; (3) crowdsensing; (4) crowdsourcing; (5) energy consumption; (6) energy efficiency; (7) intelligent transportation system; (8) interoperability; (9) iot; (10) manet; (11) mobile ad hoc network; (12) qos; (13) routing; (14) routing protocols; (15) smart environment; (16) smartphones; (17) vanet; (18) wsn
3	Sensors and advanced digital technologies to support mobility management	17	(1) 5g; (2) authentication; (3) bluetooth; (4) data collection; (5) gps; (6) healthcare; (7) ict; (8) its; (9) mobility management; (10) sdn; (11) sensors; (12) smart devices; (13) smart home; (14) technology; (15) vanets; (16) wireless sensor networks; (17) zigbee
4	Sharing to meet the demand of human mobility	15	(1) autonomous vehicle; (2) big data; (3) data mining; (4) deep learning; (5) human mobility; (6) machine learning; (7) mobility patterns; (8) public transportation; (9) smart card; (10) smart card data; (11) smart city; (12) smart transportation; (13) social media; (14) sustainable development; (15) urban computing
5	Sustainable planning for quality services	15	(1) e-mobility; (2) electric mobility; (3) electric vehicle; (4) electric vehicles; (5) energy management; (6) modeling; (7) optimization; (8) quality of service; (9) renewable energy; (10) smart charging; (11) smart grid; (12) smart grids; (13) sustainability; (14) transportation; (15) urban planning
6	Simulation and modeling to monitor mobility	12	(1) artificial intelligence; (2) assistive technology; (3) cloud; (4) evaluation; (5) mobility; (6) rehabilitation; (7) simulation; (8) smart parking; (9) smart wheelchair; (10) urban mobility; (11) v2x; (12) wheelchair
7	Accessibility and connectivity of transport networks	10	(1) accessibility; (2) connectivity; (3) intelligent transportation systems; (4) mobile computing; (5) public transport; (6) smart cities; (7) smart mobility; (8) sustainable mobility; (9) transport; (10) vehicular networks

The first cluster computing for urban safety and efficiency highlights how the application of some technologies based on information technology in transport guarantees better levels of safety and efficiency of the overall mobility system. The importance of the cluster is demonstrated by the presence of some terms that characterize it at the center of the occurrence map (Figure 2). Among these, the “internet of things” and “security” are placed respectively in fourth and ninth place among the words with the most connections (Table 1).

According to the literature, numerous studies aim to identify innovative solutions for the improvement of traffic mobility from different points of view. Reference is made, for example, to the study on on-demand mobility services (Faisal et al. 2019; Freudendal-Pedersen, Kesselring, and Servou 2019; Marletto 2019; Pucihar et al. 2019), to the development of systems that can integrate connected vehicle data and traffic sensor information (Yang et al. 2019), and to the use of high-definition smart cameras with wireless communication (Ho et al. 2019) to simultaneously address the need to improve the safety and mobility of urban arteries. These systems aim to reduce the potential accident rate, monitor traffic, and calculate the length of the queues at intersections as a function of time to improve arterial progression.

The second cluster solutions for reducing energy consumption and pollution concerns the strategies aimed at limiting the negative impact of all the modes of transport on the natural environment meaning that “clean” technologies are becoming increasingly important and widespread (Litwin et al. 2019).

The literature explores the potential benefits of low-emission transitions in terms of complementarity, temporality, scale, actors, and responsibilities (Sovacool et al. 2020) and analyzes integrated mobility-energy systems in order to represent and model future mobility systems and their interconnections with other energy systems (Muratori et al. 2020). After more than a century of oil dominance, the rapid technological advancement of alternative fuels, automation, and information technologies is creating new mobility options, business models, and policies at all levels of government (Muratori et al. 2020). Carbon emission control (Contreras and Platania 2019), new approaches to urban air quality assessment (Budde et al. 2019), and interoperability (Sotres García et al. 2019) represent only some of the strategies for developing smart contexts. With this expression, we consider a place where the correspondence between urban environment and ICT is strong (De Santis et al. 2014).

The third cluster sensors and advanced digital technologies to support mobility management refers to the information systems for monitoring and controlling the territory as a prerequisite for strategic planning and sustainable management of local resources. The literature demonstrates how the possibility of obtaining operational data and information can only improve planning processes and in particular traffic management (Lakshmanaprabu et al. 2019). Some research argues that traffic management systems, with the support of 5g and vehicular networks, represent the key to tackling mobility problems (De Souza et al. 2019). Smart sensors and actuators collaborate to collect information (Leung, Braun, and Cuzzocrea 2019; Sobral, Galvão, and Borges 2019) and interact with the user (Fraga-Lamas et al. 2019). Current research lines include the application of Bluetooth sensors (Martínez Plumé et al. 2019), wireless sensor networks (Coluccia and Fascista 2019; Nguyen et al. 2019), and integrated devices (Din et al. 2019) for real-time monitoring of parameters such as noise pollution, carbon dioxide concentration, light intensity (Moreno et al. 2019), and for the interpretation of traffic information (Ali, El-Sappagh, and Kwak 2019).

The fourth cluster sharing to meet the demand of human mobility has a double significance. The idea of sharing is associated both with the “sharing of data and information” and with the “sharing” of transport in the provision of transport services. In both cases, the precondition is the use of information and communication technologies to support the reduction of car dependency. In fact, studies on smart cities suggest putting people at the planning center, by actively engaging citizens in cocreating innovative urban services based on participatory governance processes (Cellina et al. 2020).

As regard the first meaning—the sharing of data and information—some studies describe the creation of collaborative mobility systems that allow vehicles and infrastructures to interconnect and share information to coordinate their actions (Autili et al. 2019). Especially in large cities, the use of smart cards makes it possible to analyze interpersonal and intrapersonal variability in the weekly use of public transit (Deschaintres, Morency, and Trépanier 2019), therefore to infer the mobility models of urban collective transport services (Zhao et al. 2019) and plan more efficient services that are close to citizens’ needs. Supporting mobility through automation processes can make the transport system so efficient as to manage traffic flows and adapt in real time by eliminating or minimizing total movements. The use of autonomous vehicles can offer economic, social, and environmental benefits (Lim and Taeihagh 2019). In particular, autonomous vehicles connected with Vehicle-to-Vehicle communication as basic technology exhibit an energy-saving potential higher than other systems (Ma et al. 2019).

The second meaning of sharing refers to modes of transport. Sharing mobility is one of the many variations of the collaborative economy that has been successful, thanks to the interconnection between web platforms and apps. The sharing systems include different modes of transport in order to promote the potential reduction of emissions. One possibility, probably the most widespread, is suggested by the bike-sharing system, oriented toward the “green behavior trends” with benefits for health and the environment (Cerutti et al. 2019). Other smart mobility services such as car sharing, ride-sharing (Yu and Peng 2019), and carpooling are often promoted as the key to a sustainable transport future; however, they can be associated with different risks such as increased congestion and inequality (Moscholidou and Pangbourne 2019).

The fifth cluster sustainable planning for quality services refers widely to the concept of the smart grid (Hall et al. 2019; Khayrullina, Blinov, and Borzenko 2019; Monteiro et al. 2019; Ryghaug et al. 2019) as a set of information and electricity distribution networks for efficient and sustainable urban mobility planning.

Electric vehicles have an important role in the future energy system (Cooper et al. 2019; Xiang et al. 2019). Their use represents a powerful eco-friendly initiative that, if integrated well with an urban environment, could be a key element of the smart city concept (Aymen and Mahmoudi 2019). However, the increased demand for mobility, and therefore energy, can create constraints on the power network, which can reduce the benefits of electrification as a certain and reliable source. Thus, the rise in the use of electric vehicles needs electric grids to be able to feed the increased energy demand while the current infrastructure supports it (Tayarani et al. 2019). For these reasons, some studies have deepened the electricity balancing process applied to the electric bus grid (Wu et al. 2019). For example, a study (Tu et al. 2019), given the observed heterogeneity of ride-hailing vehicle travel, outlines the importance of individual-level analysis to understand the electrification potential and future benefits of electric vehicles in the era of shared smart transportation.

The sixth cluster simulation and modeling to monitor mobility concerns the simulation and modeling of traffic and transport, which assume a crucial role in planning processes by changing to direct the logistical and settlement choices of the territorial contexts. Mobility of people can be configured as an information-intensive process resulting from a complex set of factors. In particular, simulation analyzes are conducted by processing these data in order to construct assessment scenarios connected to different solutions available for particular demand segments (Del Vecchio et al. 2019). Understanding and modeling urban mobility correctly is a crucial issue for the development of Smart Cities (Mamei et al. 2019). The object of the modeling can be displacements (Mamei et al. 2019), congestion levels (Del Vecchio et al. 2019), and exposure to pollution factors (Trewhela et al. 2019). The type of model varies according to the type of relationship between the variables to be theorized. For example, in the case of predictive models, techniques and statistical algorithms are generally used; the latter are used to extrapolate the trends of the quantities of interest and define reliable forecasts.

The seventh cluster accessibility and connectivity of transport networks refers to aspects that have a decisive impact on the growth and economic competitiveness of the territories. In particular, accessibility can be understood as an offer of a service or as a satisfied demand. In the first case, reference is made to the extension of the network and mileage, in the second to the number of passengers transported per kilometer of service in order to give an idea regarding the ability to meet the transport demand. The connectivity of the territory is expressed by the number of people and by the extent of the areas served in the basin of attraction of the stops and by the connections available between locations. Recognizing the importance of these elements, some studies focus, for example, on the analysis of the territories that suffer from lack of accessibility, depopulation and which are poorly connected (Mazzarino and Rubini 2019), or on the relationship that exists between the values of ownership residential and accessibility indicators (Siripanich, Rashidi, and Moylan 2019) in order to promote and evaluate innovative and efficient transport solutions.

Summarizing the issues emerged, we propose to define the smart mobility as “the result of a planning process (cluster 5) which makes use of technological supports (cluster 3) in the simulation phases (cluster 6), use and monitoring of individual and shared transport systems to ensure safety standards (cluster 1), functionality (clusters 4 and 7) and sustainability (cluster 2).”

Discussion

The article described the SLR process conducted by moving from some research questions reported in third section to reach a new definition of smart mobility reported in fourth section. According to these, the results obtained will be discussed below.

First of all, we consider the first RQ. The SLR has made possible to identify which elements guide the planning of Smart Sustainable Mobility system. The analysis of the scientific literature on trends that characterize smart mobility has highlighted a widespread orientation toward the use of technological solutions in transport planning processes. The global network—the Internet—has changed the urban planning model by convincing traditional planners to look at the urban planning of the city (Aldegheishem 2019). According to Turetken et al. (2019), a domain where digital innovation has great potential is smart mobility. Current technology makes possible the association of smart mobility with a sustainable mobility performance that affects quality of life (Olaverri-Monreal 2016). The new mobility models are, in fact, permeated by the use of digital systems and theoretical modeling with a twofold objective: to help redesign the overall project of the territories through environmentally sustainable solutions and to intercept the needs of citizens in order to implement functional choices in the planning and management of mobility flows. Therefore, to answer the first research question by summarizing what is described in second section, it can be said that the main elements that influence smart transition processes applied to mobility planning are technological innovation, environmental sustainability, and user satisfaction, in addition to the physical characteristics of the infrastructure system.

In light of this, considering the second RQ, we wonder whether it was possible to give a definition of smart mobility that includes these aspects in a more explicit and integrated way than already reported in the literature. This need is recognized in literature (Škorput, Mandžuka, and Schatten 2013; Mandzuka, Gregurić, and Kljaić 2016). Šemanjski, Mandžuka, and Gautama (2018) say “in future researches, special interest is the construction of appropriate taxonomy and associated ontology for smart mobility area.” For this purpose, we conducted an SLR and applied the technique of CA on the selected literature. CA allowed identification of the main research areas in smart mobility. Summarizing the issues emerged, we proposed a new definition of smart mobility in fourth section.

We compare this definition with other studies and can conclude that this is more inclusive and broader sectoral. For example, according to Lombardi et al. (2012), smart mobility refers to “the use of ICT in modern transport technologies to improve urban traffic.” Aspects referring to the preservation of the natural environment in cities are extensively covered in Giffinger et al. (2007), Albino and Dangelico (2012), and Nikitas et al. (2020). They look at transport as a key area that artificial intelligence (AI) can redefine. They think that AI with its deep learning functions and capabilities can be employed as a tool that empowers machines to solve problems that could reform urban landscapes as we have known them for decades now and help with establishing a new era. Another example of a definition of smart mobility that focuses its theory around intelligent transport systems is proposed by Kulesa (2009). It includes factors of urban mobility such as integrating the management of road, tariffs parking, and the forecasting traffic, in order to improve transport and create information services for travelers. Staricco (2013) also states that most of the opportunities of smart mobility are related to technological innovations for managing and organizing trips and traffic and for improving the environmental efficiency of vehicles, but the impacts of these innovations, in particular over the long term, depend on how they are embedded by the users in their daily activities and practices. Fernandez-Sanchez and Fernandez-Heredia (2018) focus on a single topic that is sustainable mobility by bus identifying that strategies and operational or tactical actions are often sponsored through newspapers and websites. Similarly, Shaheen et al. (2015) focus on the shared use of a vehicle, bicycle, or other mode that enables users to gain short-term access to transportation modes on an “as-needed” basis. The term shared mobility includes various forms of car sharing, bike sharing, ride-sharing (carpooling and vanpooling), and on-demand ride services. It can also include alternative transit services, such as paratransit, shuttles, and private transit services, called microtransit, which can supplement fixed-route bus and rail services. "With many new options for mobility emerging, so have the smartphone “apps” that aggregate these options and optimize routes for travelers. […] Shared mobility has had a transformative impact on many global cities by enhancing transportation accessibility, while simultaneously reducing driving and personal vehicle ownership”. Without giving a definition, Porru et al. (2020) highlight on how the deployment of smart mobility solutions within the rural context compare to that within the urban context. As well as this research comparing different projects, intelligent mobility is widely addressed on websites and in gray literature especially local and national newspapers and policy documents that provide communications on local projects and initiatives. Considering taxonomies, Cledou, Estevez, and Barbosa (2018) provide a common vocabulary to discuss and share information about services comprising eight dimensions: type of services, maturity level, users, applied technologies, delivery channels, benefits, beneficiaries, and common functionality in order to guide policy makers by identifying a spectrum of mobility services that can be provided, to whom, what technologies can be used to deliver them, and what is the delivered public value so to justify their implementation. Benevolo, Dameri, and D’auria (2016) analyze the smart mobility initiatives like part of a larger smart city initiative portfolio and investigate about the role of ICT in supporting smart mobility actions, influencing their impact on the citizens’ quality of life and on the public value created for the city as a whole. Ontologies on smart mobility reported in the literature are very specific and focus on topics such as knowledge for collaboration among travelers and the surrounding infrastructure using advanced navigation systems (Syzdykbayev, Hajari, and Karimi 2019) and management of semantic locations and trajectories (Ilarri, Stojanovic, and Ray 2015).

Unlike these studies, our definition, thanks to its derivation process, is interdisciplinary. In fact, the analysis of the literature made reference to a large sample of documents, selected without applying inclusion or exclusion criteria in reference to the subject area to which they belong.

A further element that proves the proposed definition is the methodological choice to define the homogeneous classes without the conditioning of an interpretative model assigned “a priori.” In fact, the generation of research hypotheses is the result of the application of an inductive multivariate statistical analysis technique that is the CA. This allowed us to try to explain the multiple meanings attributed to the concept and the many different approaches in current urban planning literature. Other literature reviews are conditioned by author choices. As an example, Papa and Lauwers (2015) in their work choose to focus only on two main aspects attributed to the concept of smart mobility that are the techno-centric smart mobility and the consumer-centric smart mobility. Fiore, Florea, and Pérez Lechuga (2019) work on smart traffic and on the aspects concerning the gathering, fusion, and transmission of information. Although broad from a conceptual point of view, the use of the described procedure places limits related to the selection of literature, such as the choice of the search space to obtain data, the limitation of documents to those written in English, and the reference time frame. Therefore, especially in the long term, updating and expanding the sample of reference publications will allow further innovative aspects to be included in the proposed definition.

Conclusion and Future Research

The article makes extensive reference to the theoretical and empirical contents of the literature in relation to the concept of smart city and above all of smart mobility. Generally, similar models are applied to big cities and metropolitan areas, in order to analyze the main policies that characterize them and suggest strategies aimed at improving the quality of life of people and the environmental and economic sustainability of the territory. However, the definition of smart mobility that has been reached through the application of the methodology described in the second section and discussed in the previous section appears sufficiently exhaustive compared to the topics addressed for the case studies proposed in the selected literature. With the aim of declining the need to transform the minor urban polarities—particularly widespread in Italy and Europe—it is necessary to focus attention on aspects selected according to local vocations and specificities.

In particular, we are currently conducting a study aimed at defining a model capable of measuring the degree of smartness of rural areas (Francini et al. 2020). The model is structured according to the widespread and internationally recognized approach to “policy axes” and is characterized by a hierarchical structure. The axes coincide with the dimensions identified by Giffinger et al. (2007) as transformation domains for the smart city: economy, environment, governance, living, mobility, and people. Each domain is “represented” by factors, each factor is “qualified” by variables, and each variable is “quantified” by indicators. We want to evolve the smart system by adapting it to territories with dimensional, physical, and vocational characteristics considerably different from those commonly associated with the idea of a smart city by building a new model called Smart Land. Therefore, the reasons that led to the study of the literature described in this article are deduced. The general framework proposed, even analyzing different contexts, represents the starting point for our research quantitative developments.

In particular, in order to define the factors that represent the smart mobility domain, we plan to consider the main elements that, from the analysis of the literature, were influential (second section) for the implementation of the smart transition processes applied to planning mobility, namely, the characteristics of the infrastructure system, technological innovation, environmental sustainability, and user satisfaction. The definition of smart mobility proposed (fifth section) on the basis of the analysis of the selected literature (fourth section) represents, instead, the reference for the identification of the variables that qualify the factors. These must be associated with performance indicators designed specifically for rural areas. The areas being studied are, in fact, portions of territory characterized by a considerable spatial and temporal dispersion with a generally medium-low transport demand. The concept of weak demand refers to both the number of displacements generated by the area and the degree of fragmentation of the residential fabric. In particular, the presence of hamlets and scattered houses tends to reduce the demand for mobility and consequently makes conventional transport systems expensive and inefficient. For example, it may be necessary to adopt more flexible forms of local public transport in order to combine different lines with many stops. These needs should be taken into the process of selecting indicators and for defining the relative performance scales.

In conclusion, the study conducted, in addition to summarizing the theoretical and empirical contents on the specific topic through the approach of the SLR, has allowed to reach an extended and shared definition of the same based on the results of the application of the CA on a selected literature. The importance of the research is condensed into its analytical feature. The result obtained represents a useful reference both from a theoretical and from an operational point of view. Specifically, what emerged could:

– provide foundation of knowledge on topic;

– identify areas of prior studies to prevent duplication;

– identify gaps in research, conflicts in previous studies, and open questions left from other research and generate new original ideas;

– support academics in placing their research within the context of existing literature making a case for why further study is needed; and

– support policy makers and planners in identifying advantages and success factors to be taken into consideration in transforming theoretical concepts into real projects.

In addition, the set of results constitutes the framework for the quantitative development of our method of assessing the degree of smartness for rural areas proposed and aimed at a better management of mobility flows as well as an innovative design of transport infrastructures.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Mauro Francini

Annunziata Palermo

References

Albino

Vito

Dangelico

Rosa Maria

. 2012. “Green Cities into Practice.” In The Economy of Green Cities, edited by Simpson

Richard

Zimmermann

Monika

, 99–113. Dordrecht, the Netherlands: Springer.

Aldegheishem

Abdulaziz

. 2019. “Success Factors of Smart Cities: A Systematic Review of Literature from 2000-2018.” TeMA: Journal of Land Use, Mobility and Environment 12 (1): 53–64.

Alexopoulos

Charalampos

Pereira

Gabriela V.

Charalabidis

Yannis

Madrid

Lorenzo

. 2019. “A Taxonomy of Smart Cities Initiatives.” In Proceedings of the 12th International Conference on Theory and Practice of Electronic Governance, edited by Soumaya

Ben D.

Lemuria

Carter

Mark

Gregory

, 281–90. New York: ACM Press.

Ali

Farman

El-Sappagh

Shaker

Kwak

Daehan

. 2019. “Fuzzy Ontology and LSTM-based Text Mining: A Transportation Network Monitoring System for Assisting Travel.” Sensors 19 (2): 234.

Anthopoulos

Leonidas G.

2015. “Understanding the Smart City Domain: A Literature Review.” In Transforming City Governments for Successful Smart Cities, edited by Rodríguez-Bolívar

Manuel P.

9–21. Cham, Switzerland: Springer.

Anwer

Faiza

Aftab

Shabib

. 2017. “Latest Customizations of XP: A Systematic Literature Review.” International Journal of Modern Education and Computer Science 9 (12): 26–37.

Autili

Marco

Salle

Amleto Di

Gallo

Francesco

Pompilio

Claudio

Tivoli

Massimo

. 2019. “A Choreography-based and Collaborative Road Mobility System for L’Aquila City.” Future Internet 11 (6): 132.

Aymen

Flah

Mahmoudi

Chokri

. 2019. “A Novel Energy Optimization Approach for Electrical Vehicles in a Smart City.” Energies 12 (5): 929.

Ballas

Dimitris

. 2013. “What Makes a ‘Happy City’?” Cities 32:S39–50.

10.

Benevolo

Clara

Dameri

Renata P.

D’auria

Beatrice

2016. “Smart Mobility in Smart City.” In Empowering Organizations, edited by Torre

Teresina

Braccini

Alessio M.

Riccardo

Spinelli

, 13–28. Cham, Switzerland: Springer.

11.

Bertolini

Luca

. 2012. “Integrating Mobility and Urban Development Agendas: A Manifesto.” disP-The Planning Review 48 (1): 16–26.

12.

Budde

Matthias

Leiner

Simon

Köpke

Marcel

Riesterer

Johannes

Riedel

Till

Beigl

Michael

. 2019. “FeinPhone: Low-cost Smartphone Camera-based 2D Particulate Matter Sensor.” Sensors 19 (3): 749.

13.

Bueno-Delgado

Maria V.

Romero-Gázquez

José-Luis

Jiménez

Pilar

Pavón-Mariño

Pablo

. 2019. “Optimal Path Planning for Selective Waste Collection in Smart Cities.” Sensors 19 (9): 1973.

14.

Caragliu

Andrea

Chiara Del

Nijkamp

Peter

. 2011. “Smart Cities in Europe.” Journal of Urban Technology 18 (2): 65–82.

15.

Cellina

Francesca

Castri

Roberta

Simão

José V.

Granato

Pasquale

. 2020. “Co-creating App-based Policy Measures for Mobility Behavior Change: A Trigger for Novel Governance Practices at the Urban Level.” Sustainable Cities and Society 53:101911.

16.

Cerutti

Priscilla

Martins

Rosiane D.

Macke

Janaina

Sarate

Joao A. R.

2019. “Green, but Not as Green as That: An Analysis of a Brazilian Bike-sharing System.” Journal of Cleaner Production 217:185–93.

17.

Chourabi

Hafedh

Nam

Taewoo

Walker

Shawn

Ramon Gil-Garcia

Mellouli

Sehl

Nahon

Karine

Pardo

Theresa A.

Scholl

Hans J.

2012. “Understanding Smart Cities: An Integrative Framework.” Forty-fifth Hawaii International Conference on System Sciences, 2289–97 , Maui, HI.

18.

Cledou

Guillermina

Estevez

Elsa

Barbosa

Luis S.

2018. “A Taxonomy for Planning and Designing Smart Mobility Services.” Government Information Quarterly 35 (1): 61–76.

19.

Cohen

Boyd

. 2015. “The 3 Generations of Smart Cities: Inside the Development of the Technology Driven City.” Accessed December 21, 2020. https://www.fastcompany.com/3047795/the-3-generations-of-smart-cities.

20.

Coluccia

Angelo

Fascista

Alessio

. 2019. “A Review of Advanced Localization Techniques for Crowdsensing Wireless Sensor Networks.” Sensors 19 (5): 988.

21.

Contreras

Gabriela

Platania

Federico

. 2019. “Economic and Policy Uncertainty in Climate Change Mitigation: The London Smart City Case Scenario.” Technological Forecasting and Social Change 142:384–93.

22.

Cooper

Peter

Tryfonas

Theo

Crick

Tom

Marsh

Alex

. 2019. “Electric Vehicle Mobility-as-a-service: Exploring the “Tri-opt” of Novel Private Transport Business Models.” Journal of Urban Technology 26 (1): 35–56.

23.

De Santis

Roberta

Alessandra

Fasano

Nadia

Mignolli

Anna

Villa

. 2014. “Il Fenomeno Smart Cities.” Rivista Italiana di Economia Demografia e Statistica 68 (1): 143–150.

24.

De Souza

Allan M.

Maia

Guilherme

Braun

Torsten

Villas

Leandro A.

2019. “An Interest-based Approach for Reducing Network Contentions in Vehicular Transportation Systems.” Sensors 19 (10): 2325.

25.

Del Vecchio

Pasquale

Secundo

Giustina

Maruccia

Ylenia

Passiante

Giuseppina

. 2019. “A System Dynamic Approach for the Smart Mobility of People: Implications in the Age of Big Data.” Technological Forecasting and Social Change 149:119771.

26.

Denyer

David

Neely

Andy

. 2004. “Introduction to Special Issue: Innovation and Productivity Performance in the UK.” International Journal of Management Reviews 5 (3–4): 131–35.

27.

Deschaintres

Elodie

Morency

Catherine

Trépanier

Martin

. 2019. “Analyzing Transit User Behavior with 51 Weeks of Smart Card Data.” Transportation Research Record 2673 (6): 33–45.

28.

Desouza

Kevin C.

Flanery

Trevor H.

2013. “Designing, Planning, and Managing Resilient Cities: A Conceptual Framework.” Cities 35:88–89.

29.

Din

Sadia

Paul

Anand

Hong

Won-Hwa

Seo

Hyuncheol

. 2019. “Constrained Application for Mobility Management Using Embedded Devices in the Internet of Things Based Urban Planning in Smart Cities.” Sustainable Cities and Society 44:144–51.

30.

Fabbris

Luigi

. 1990. Analisi esplorativa di dati multidimensionali. Padova, Italy: Cleup editore.

31.

Faisal

Asif

Yigitcanlar

Tan

Kamruzzaman

Currie

Graham

. 2019. “Understanding Autonomous Vehicles: A Systematic Literature Review on Capability, Impact, Planning and Policy.” Journal of Transport and Land Use 12 (1): 45–72.

32.

Fernandez-Anez

Victoria

Fernández-Güell

José M.

Giffinger

Rudolf

. 2018. “Smart City Implementation and Discourses: An Integrated Conceptual Model. The Case of Vienna.” Cities 78:4–16.

33.

Fernandez-Sanchez

Gonzalo

Fernandez-Heredia

Alvaro

. 2018. “Strategic Thinking for Sustainability: A Review of 10 Strategies for Sustainable Mobility by Bus for Cities.” Sustainability 10 (11): 4282.

34.

Fiore

Ugo

Florea

Adrian

Lechuga

Gilberto Pérez

. 2019. “An Interdisciplinary Review of Smart Vehicular Traffic and Its Applications and Challenges.” Journal of Sensor and Actuator Networks 8 (1): 13.

35.

Fontana

Federico

. 2014. “La pianificazione e l’implementazione della Smart City.” ImpresaProgetto Electronic Journal of Management 4:1–32.

36.

Fraga-Lamas

Paula

Celaya-Echarri

Mikel

Lopez-Iturri

Peio

Castedo

Luis

Azpilicueta

Leyre

Aguirre

Erik

Suarez-Albela

Manuel

Falcone

Francisco

Fernández-Caramés

Tiago M.

2019. “Design and Experimental Validation of a LoRaWAN Fog Computing Based Architecture for IoT Enabled Smart Campus Applications.” Sensors 19 (15): 3287.

37.

Francini

Chieffallo

Palermo

Viapiana

M. F.

2020. “Estimation of the Smart Land Index: Application to the Rural Context of the Crati Valley.” European Planning Studies 28 (4): 749–70.

38.

Freudendal-Pedersen

Malene

Kesselring

Sven

Servou

Eriketti

. 2019. “What Is Smart for the Future City? Mobilities and Automation.” Sustainability 11 (1): 221.

39.

Garau

Chiara

Desogus

Giulia

Zamperlin

Paola

. 2020. “Governing Technology-based Urbanism.” In The Routledge Companion to Smart Cities, edited by Willis

Katharin S.

Aurigi

Alessandro

(pp. 157–174). New York: Routledge.

40.

Giffinger

Rudolf

Fertner

Christian

Kramar

Hans

Meijers

Evert

. 2007. “Smart Cities: Ranking of European Medium-sized Cities.” Accessed December 21, 2020. http://www.smartcity-ranking.eu/download/city_ranking_final.pdf.

41.

Ginieis

Matias

Sánchez-Rebull

Maria-Victoria

Campa-Planas

Fernando

. 2012. “The Academic Journal Literature on Air Transport: Analysis Using Systematic Literature Review Methodology.” Journal of Air Transport Management 19:31–35.

42.

Green

Paul E.

Frank

Ronald E.

Robinson

Patrick J.

1967. “Cluster Analysis in Test Market Selection.” Management Science 13 (8): B387.

43.

Gudanowska

Alicja E.

2017. “A Map of Current Research Trends within Technology Management in the Light of Selected Literature.” Management and Production Engineering Review 8 (1): 78–88.

44.

Hall

Stephen

Jonas

Andrew E.

Shepherd

Simon

Wadud

Zia

. 2019. “The Smart Grid as Commons: Exploring Alternatives to Infrastructure Financialisation.” Urban Studies 56 (7): 1386–403.

45.

Hamad

Zuhal

Salim

Naumie

. 2014. “Systematic Literature Review (SLR) Automation: A Systematic Literature Review.” Journal of Theoretical and Applied Information Technology 59 (3): 661–72.

46.

George T. S.

Tsang

Yung P.

H. Wu

Chun

Wong

Wai H.

Choy

King L.

2019. “A Computer Vision-based Roadside Occupation Surveillance System for Intelligent Transport in Smart Cities.” Sensors 19 (8): 1796.

47.

Ilarri

Sergio

Stojanovic

Dragan

Ray

Cyril

. 2015. “Semantic Management of Moving Objects: A Vision towards Smart Mobility.” Expert Systems with Applications 42 (3): 1418–35.

48.

Jardine

Nicolas

Sibson

Robin

. 1971. Mathematical Taxonomy. London, UK: Wiley.

49.

Jiménez Herrero

Luis M.

2011. “Transport and Mobility: The Keys to Sustainability.” Fundación General CSIC Lychnos 4 (4): 40–45.

50.

Joshi

Sujata

Saxena

Saksham

Godbole

Tanvi

. 2016. “Developing Smart Cities: An Integrated Framework.” Procedia Computer Science 93:902–9.

51.

Kamolov

Sergey

Kandalintseva

Yulia

. 2020. “The Study on the Readiness of Russian Municipalities for Implementation of the “Smart City” Concept.” In Proceedings of the Ecological-socio-economic Systems: Models of Competition and Cooperation (ESES 2019), 256–60. Atlantis Press. Accessed January 12, 2021. https://www.atlantis-press.com/proceedings/eses-19/125932018.

52.

Khayrullina

Aliya

Blinov

Dmitry

Borzenko

Vasily

. 2019. “Air Heated Metal Hydride Energy Storage System Design and Experiments for Microgrid Applications.” International Journal of Hydrogen Energy 44 (35): 19168–76.

53.

Kitchin

Rob

. 2015. “Making Sense of Smart Cities: Addressing Present Shortcomings.” Cambridge Journal of Regions, Economy and Society 8 (1): 131–36.

54.

Kulesa

Tammy

. 2009. “Intelligent Transport: How Cities Can Improve Mobility.” Accessed January 12, 2021. https://www.semanticscholar.org/paper/Intelligent-Transport%3A-How-Cities-Can-Improve-July/2d93334c0dffe4d22e1b9878491c98edf92b8f97?p2df.

55.

Kunzmann

Klaus R.

2014. “Smart Cities: A New Paradigm of Urban Development.” Crios 4 (1): 9–20.

56.

Kurushina

Elena V.

Kurushina

Victoriya A.

2014. “Evolution of Economic Development Aims. Assessment of the Smart Growth.” Life Science Journal 11 (11): 517–21.

57.

Lakshmanaprabu

S. K.

Shankar

Rani

S. S.

Abdulhay

Arunkumar

Ramirez

Uthayakumar

2019. “An Effect of Big Data Technology with Ant Colony Optimization Based Routing in Vehicular Ad Hoc Networks: Towards Smart Cities.” Journal of Cleaner Production 217:584–93.

58.

Lee

Jung H.

Phaal

Robert

Lee

Sang-Ho

. 2013. “An Integrated Service-device-technology Roadmap for Smart City Development.” Technological Forecasting and Social Change 80 (2): 286–306.

59.

Leung

Carson K.

Braun

Peter

Cuzzocrea

Alfredo

. 2019. “AI-based Sensor Information Fusion for Supporting Deep Supervised Learning.” Sensors 19 (6): 1345.

60.

Xia

Fong

Patrick S. W.

Dai

Shengli

Yingchun

. 2019. “Towards Sustainable Smart Cities: An Empirical Comparative Assessment and Development Pattern Optimization in China.” Journal of Cleaner Production 215:730–43.

61.

Lim

Hazel S. M.

Taeihagh

Araz

. 2019. “Algorithmic Decision-making in AVs: Understanding Ethical and Technical Concerns for Smart Cities.” Sustainability 11 (20): 5791.

62.

Litwin

Wojciech

Leśniewski

Wojciech

Piątek

Daniel

Niklas

Karol

. 2019. “Experimental Research on the Energy Efficiency of a Parallel Hybrid Drive for an Inland Ship.” Energies 12 (9): 1675.

63.

Lombardi

Patrizia

Giordano

Silvia

Farouh

Hend

Yousef

Wael

. 2012. “Modelling the Smart City Performance.” Innovation: The European Journal of Social Science Research 25 (2): 137–49.

64.

Fangwu

Yang

Wang

Jiawei

Liu

Zhenze

Jiahang

Nie

Jiahong

Shen

Yucheng

Liang

. 2019. “Predictive Energy-saving Optimization Based on Nonlinear Model Predictive Control for Cooperative Connected Vehicles Platoon with V2V Communication.” Energy 189:116120.

65.

Mamei

Marco

Bicocchi

Nicola

Lippi

Marco

Mariani

Stefano

Zambonelli

Franco

. 2019. “Evaluating Origin–destination Matrices Obtained from CDR Data.” Sensors 19 (20): 4470.

66.

Mandzuka

Sadko

Gregurić

Martin

Kljaić

2016. “Cooperative Environment for E-mobility Infrastructure.” Twenty-fourth Telecommunications Forum (TELFOR), 1–4. Belgrade, Serbia.

67.

Marletto

Gerardo

. 2019. “Who Will Drive the Transition to Self-driving? A Socio-technical Analysis of the Future Impact of Automated Vehicles.” Technological Forecasting and Social Change 139:221–34.

68.

Martínez Plumé

Javier

Durá

Juan J. M.

Gimeno

R. V. C.

García

Francisco R. S.

Celda

Garcìa A.

2019. “Evaluation of the Use of a City Center through the Use of Bluetooth Sensors Network.” Sustainability 11 (4): 1–18.

69.

Mazzarino

Marco

Rubini

Lucio

. 2019. “Smart Urban Planning: Evaluating Urban Logistics Performance of Innovative Solutions and Sustainable Policies in the Venice Lagoon: The Results of a Case Study.” Sustainability 11 (17): 4580.

70.

Monteiro

Vitor

Afonso

José A.

Ferreira

Joao C.

Afonso

Joao L.

2019. “Vehicle Electrification: New Challenges and Opportunities for Smart Grids.” Energies 12 (1): 118.

71.

Moreno

Jaime

Morales

Oswaldo

Tejeida

Richardo

Posadas

Juan

Quintana

Hugo

Sidorov

Grigori

. 2019. “Distributed Learning Fractal Algorithm for Optimizing a Centralized Control Topology of Wireless Sensor Network Based On the Hilbert Curve L-system.” Sensors 19 (6): 1442.

72.

Moscholidou

Ioanna

Pangbourne

Kate

. 2019. “A Preliminary Assessment of Regulatory Efforts to Steer Smart Mobility in London and Seattle.” Transport Policy 98:170–77.

73.

Muratori

Matteo

Jadun

Paige

Bush

Brian

Bielen

David

Vimmerstedt

Laura

Gonder

Jeff

Gearhart

Chris

Arent

Doug

. 2020. “Future Integrated Mobility-energy Systems: A Modeling Perspective.” Renewable and Sustainable Energy Reviews 119:109541.

74.

Neirotti

Paolo

Marco

Alberto De

Cagliano

Anna C.

Mangano

Giulio

Scorrano

Francesco

. 2014. “Current Trends in Smart City Initiatives: Some Stylised Facts.” Cities 38:25–36.

75.

Nguyen

Huy

Nam T.

Hoan

Nguyen C.

Jang

Yeong M.

2019. “Real-time Mitigation of the Mobility Effect for IEEE 802.15. 4g SUN MR-OFDM.” Applied Sciences 9 (16): 3289.

76.

Nikitas

Alexandros

Michalakopoulou

Kalliopi

Njoya

Eric T.

Karampatzakis

Dimitris

. 2020. “Artificial Intelligence, Transport and the Smart City: Definitions and Dimensions of a New Mobility Era.” Sustainability 12 (7): 2789.

77.

Ning

Zhaolong

Xia

Feng

Ullah

Noor

Kong

Xiangjie

Xiping

. 2017. “Vehicular Social Networks: Enabling Smart Mobility.” IEEE Communications Magazine 55 (5): 16–55.

78.

Olaverri-Monreal

Cristina

. 2016. “Autonomous Vehicles and Smart Mobility Related Technologies.” Infocommunications Journal 8 (2): 17–24.

79.

Papa

Enrica

Lauwers

Dirk

. 2015. “Smart Mobility: Opportunity or Threat to Innovate Places and Cities.” In 20th International Conference on Urban Planning and Regional Development in the Information Society (REAL CORP 2015), 543–50. Accessed January 12, 2021. https://westminsterresearch.westminster.ac.uk/download/30a918a69316ec6f2b350e40ef549e8d0ce25a447ce46832976746f3e5cd4ef1/274316/CORP2015_46-1.pdf.

80.

Pinna

Francesco

Masala

Francesca

Garau

Chiara

. 2017. “Urban Policies and Mobility Trends in Italian Smart Cities.” Sustainability 9 (4): 494.

81.

Piro

Giuseppe

Cianci

Ilaria

Grieco

Luigi A.

Boggia

Gennaro

Camarda

Pietro

. 2014. “Information Centric Services in Smart Cities.” Journal of Systems and Software 88:169–88.

82.

Porru

Simone

Misso

Francesco E.

Pani

Filippo E.

Repetto

Cino

. 2020. “Smart Mobility and Public Transport: Opportunities and Challenges in Rural and Urban Areas.” Journal of Traffic and Transportation Engineering 7 (1): 88–97.

83.

Pucihar

Andreja

Zajc

Iztok

Sernec

Radovan

Lenart

Gregor

. 2019. “Living Lab as an Ecosystem for Development, Demonstration and Assessment of Autonomous Mobility Solutions.” Sustainability 11 (15): 4095.

84.

Ryghaug

Marianne

Ornetzeder

Michael

Skjølsvold

Tomas M.

Throndsen

William

. 2019. “The Role of Experiments and Demonstration Projects in Efforts of Upscaling: An Analysis of Two Projects Attempting to Reconfigure Production and Consumption in Energy and Mobility.” Sustainability 11 (20): 5771.

85.

Šemanjski

Ivana

Mandžuka

Sadko

Gautama

Sidharta

. 2018. “Smart Mobility.” In 2018 International Symposium ELMAR, 63–66. Accessed January 12, 2021. https://ieeexplore.ieee.org/document/8534693.

86.

Seuwou

Patrice

Ubakanma

George

Banissi

Ebad

. 2020. “The Future of Mobility with Connected and Autonomous Vehicles in Smart Cities.” In Digital Twin Technologies and Smart Cities, edited by Farsi

Maryam

Daneshkhah

Alireza

Hosseinian-Far

Amin

Jahankhani

Hamid

, 37–52. Cham, Switzerland: Springer.

87.

Shaheen

Susan

Chan

Nelson

Bansal

Apaar

Cohen

Adam

2015. “Shared Mobility: A Sustainability & Technologies Workshop: Definitions, Industry Developments, and Early Understanding.” Accessed December 21, 2020. https://escholarship.org/uc/item/2f61q30s.

88.

Sharida

Abdulrahman

Hamdan

Allam

Mukhtar

Al-Hashimi

. 2020. “Smart Cities: The Next Urban Evolution in Delivering a Better Quality of Life.” In Toward Social Internet of Things (SIoT): Enabling Technologies, Architectures and Applications, edited by Hassanien

Aboul E.

Bhatnagar

Roheet

Khalifa

Nour E. M.

Taha

M. H. N.

, 287–98. Cham, Switzerland: Springer.

89.

Siderska

Julia

Jadaan

Khair S.

2018. “Cloud Manufacturing: A Service-oriented Manufacturing Paradigm. A Review Paper.” Engineering Management in Production and Services 10 (1): 22–31.

90.

Siripanich

Amarin

Rashidi

Taha H.

Moylan

Emily

. 2019. “Interaction of Public Transport Accessibility and Residential Property Values Using Smart Card Data.” Sustainability 11 (9): 2709.

91.

Škorput

Pero

Mandžuka

Sadko

Schatten

Markus

. 2013. “Ontologies in the Area of Cooperative Intelligent Transport System.” In 2013 21st Telecommunications Forum Telfor (TELFOR), 42–45. Accessed January 12, 2021. https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6716167

92.

Sobral

Thiago

Galvão

Teresa

Borges

José

. 2019. “Visualization of Urban Mobility Data from Intelligent Transportation Systems.” Sensors 19 (2): 332.

93.

Song

Yan

. 2005. “Smart Growth and Urban Development Pattern: A Comparative Study.” International Regional Science Review 28 (2): 239–65.

94.

Sotres García

Pablo

Lanza

Jorge

Sánchez

Luis

Santana

Juan R.

López

Carmen

Muñoz

Luis

. 2019. “Breaking Vendors and City Locks through a Semantic-enabled Global Interoperable Internet-of-things System: A Smart Parking Case.” Sensors 19 (2): 229.

95.

Sovacool

Benjamin K.

Martiskainen

Mari

Hook

Andrew

Baker

Lucy

. 2020. “Beyond Cost and Carbon: The Multidimensional Co-benefits of Low Carbon Transitions in Europe.” Ecological Economics 169:106529.

96.

Staricco

Luca

. 2013. “Smart Mobility: opportunità e condizioni.” TeMA: Journal of Land Use, Mobility and Environment 6 (3): 342–54.

97.

Syzdykbayev

Meirman

Hajari

Hadi

Karimi

Hassan A.

2019. “An Ontology for Collaborative Navigation among Autonomous Cars, Drivers, and Pedestrians in Smart Cities.” In 2019 4th International Conference on Smart and Sustainable Technologies (SpliTech) , 1–6. Accessed January 12, 2021. https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8783045

98.

Szarek-Iwaniuk

Patrycia

Senetra

Adam

. 2020. “Access to ICT in Poland and the Co-creation of Urban Space in the Process of Modern Social Participation in a Smart City: A Case Study.” Sustainability 12 (5): 2136.

99.

Tayarani

Hanif

Jahangir

Hamidreza

Nadafianshahamabadi

Razieh

Golkar

Masoud Aliakbar

Ahmadian

Ali

Elkamel

Ali

. 2019. “Optimal Charging of Plug-in Electric Vehicle: Considering Travel Behavior Uncertainties and Battery Degradation.” Applied Sciences 9 (16): 3420.

100.

Trencher

Gregory

. 2019. “Towards the Smart City 2.0: Empirical Evidence of Using Smartness as a Tool for Tackling Social Challenges.” Technological Forecasting and Social Change 142:117–28.

101.

Trewhela

Benjamín

Huneeus

Nicolàs

Munizaga

Marcela

Mazzeo

Andrea

Menut

Laurent

Mailler

Sylvain

Valari

Myrto

Ordoñez

Cesar

. 2019. “Analysis of Exposure to Fine Particulate Matter Using Passive Data from Public Transport.” Atmospheric Environment 215:116878.

102.

Wei

Santi

Paolo

Zhao

Tianhong

Xiaoye

Qingquan

Dong

Lei

Wallington

Timothy J.

Ratti

Carlo

. 2019. “Acceptability, Energy Consumption, and Costs of Electric Vehicle for Ride-hailing Drivers in Beijing.” Applied Energy 250:147–60.

103.

Turetken

Oktay

Grefen

Paul

Gilsing

Rick

Ege Adali

2019. “Service-dominant Business Model Design for Digital Innovation in Smart Mobility.” Business & Information Systems Engineering 61 (1): 9–29.

104.

Wey

Wann-Ming

Hsu

Janice

. 2014. “New Urbanism and Smart Growth: Toward Achieving a Smart National Taipei University District.” Habitat International 42:164–74.

105.

Wolfram

Marc

. 2012. “Deconstructing Smart Cities: An Intertextual Reading of Concepts and Practices for Integrated Urban and ICT Development.” Accessed December 21, 2020. https://pdfs.semanticscholar.org/3053/7dcc1a785e968caf1660d8a3932a1c5c868d.pdf.

106.

Guo

Polak

Strbac

2019. “Evaluating Grid-interactive Electric Bus Operation and Demand Response with Load Management Tariff.” Applied Energy 255:113798.

107.

Xiang

Yue

Jiang

Zhuozhen

Chenghong

Teng

Fei

Wei

Xiangyu

Wang

Yang

. 2019. “Electric Vehicle Charging in Smart Grid: A Spatial-temporal Simulation Method.” Energy 189:116221.

108.

Yang

Xianfeng

Chang

Gang-Len

Zhang

Zhao

Pengfei

. 2019. “Smart Signal Control System for Accident Prevention and Arterial Speed Harmonization under Connected Vehicle Environment.” Transportation Research Record 2673 (5): 61–71.

109.

Yenneti

Komali

Rahiman

Riya

Panda

Adishree

Pignatta

Gloria

. 2019. “Smart Energy Management Policy in India: A Review.” Energies 12 (17): 3214.

110.

Yigitcanlar

Tan

Lee

Sang H.

2014. “Korean Ubiquitous-eco-city: A Smart-sustainable Urban Form or a Branding Hoax?” Technological Forecasting and Social Change 89:100–14.

111.

Haitao

Peng

Zhong-Ren

. 2019. “The Impacts of Built Environment on Ridesourcing Demand: A Neighbourhood Level Analysis in Austin, Texas.” Urban Studies 57 (1): 152–75.

112.

Zhao

Xing

Ya-Peng

Ren

Gang

Kang

Qian

Wen-Wen

. 2019. “Clustering Analysis of Ridership Patterns at Subway Stations: A Case in Nanjing, China.” Journal of Urban Planning and Development 145 (2): 04019005.