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Review

Smart Mobility Adoption: A Review of the Literature

1
Faculty of Engineering and Natural Sciences, Ankara Yildirim Beyazit University, 06760 Ankara, Turkey
2
School of Accounting, Information Systems and Supply Chain, RMIT University, Melbourne, VIC 3000, Australia
3
School of Civil and Environmental Engineering, Queensland University of Technology, Brisbane, QLD 4000, Australia
4
Transport Research Center, Polytechnic University of Madrid, 28040 Madrid, Spain
5
Institute for studies on the Mediterranean, National Research Council, 00185 Roma, Italy
6
Department of Civil Engineering, School of Engineering, University of Birmingham, Birmingham B15 2TT, UK
7
Department of Applied Economics, University of Granada, 18071 Granada, Spain
*
Author to whom correspondence should be addressed.
J. Open Innov. Technol. Mark. Complex. 2021, 7(2), 146; https://0-doi-org.brum.beds.ac.uk/10.3390/joitmc7020146
Submission received: 29 March 2021 / Revised: 17 May 2021 / Accepted: 18 May 2021 / Published: 1 June 2021

Abstract

:
Traffic congestion and air pollution continue to be serious concerns, especially in large cities, and going forward, this is not sustainable. Urban transport around the world is facing challenges, such as air pollution and inefficient use of resources, that often inhibit economic development. Simply building more roads cannot address such challenges. There is a need to integrate the urban infrastructure through smart connectivity. Smart mobility, as a vital cornerstone of a smart city, will potentially reduce traffic jams, commuting times, and road crashes and create an opportunity for passengers to customize their journeys. In fact, planning smart mobility solutions is among the top challenges for large cities around the world. It involves a set of deliberate actions backed by sophisticated technologies. The different elements and dimensions that characterize smart mobility are investigated to depict the overall picture surrounding the smart mobility domain. Additionally, the trends, opportunities, and threats inherent to smart mobility are addressed. There are four segments of smart mobility that are highlighted in this paper: intelligent transport systems, open data, big data analytics, and citizen engagement. These segments are all inter-related and play a crucial role in the successful implementation of smart mobility.

1. Smart Mobility

Smart mobility is becoming a significant area of research in urban planning [1]. Since the inception of the smart mobility concept, it has been utilized in transportation networks in both urban and transport planning spheres because of its innovativeness. Equally, smart mobility has been a part of policy-making as this approach gains more acceptance and technical feasibility [1,2]. In contrast with digital platforms that no longer focus on sustainable mobility, given that their emphasis has shifted to active mobility, academic work has always focused on sustainable forms of mobility [3,4,5]. Therefore, this paper attempts to clarify the meaning of a different idea whose competence has surpassed smart mobility. In this research, the objective is to focus on the methods and perspectives that underlie the research on smart mobility. Smart mobility is a deeply grounded notion typified by a significant research gap because it is in its early stages of development. As such, no standards have been developed to aid in the definition of the term “smart mobility”. Urban mobility is an important part of the integrated city strategy [1]. Tomaszewska and Florea [2] suggest that smart mobility is “a cornerstone of a smart city strongly associated with the transboundary haze (routing, digital transformation systems, and forecast of car traffic) decisions and policy of municipalities that are focused on the tools and innovations of data and communication.” (citation and page no.) Some scholars [3] define smart mobility as an aspect that consists of the set of acts that encourage traffic flow, either on foot or by bicycle, or via federal or state transportation, all following a shared goal to minimize economic, environmental, and time costs. Other authors [4] claim that smart mobility emphasizes infusing technology into urban infrastructure and focuses on how people who interact with the urban environment can do so in an enlightened and stylish way. Table 1 summarizes past research with a focus on the meaning of smart mobility.
Smart mobility is not an entirely new campaign; however, it is an abstract set of plans and actions that have unparalleled technological and social aspects, such as a structure and goals. Table 2 summarizes the definition of smart mobility. To date, researchers around the world have not agreed upon a definition of ‘smart mobility’. Diverse considerations of smart mobility contribute to multiple definitions, which are not limited to the following assertions:
  • Intelligent Transportation Systems (ITSs) are advanced intermodal transport networks used for smart cities. As one of the key tenets of mobility solutions, ITSs are specialized systems for data collection, storage, and processing and provide expertise in the planning, execution, and assessment of the integrated initiatives and policies of smart mobility. Urban areas are also connected to innovative ideas enabled by the Internet of Things (IoT), as per the common idea of smart connectivity [6,7].
  • Open-data and open-source transport frameworks are used to model mass transit connectivity, develop and mimic bicycle sharing schemes, collect mass transit routing data, offer real-time alternative route information [8], track and document traffic safety data, and perform travel time questionnaires. Open data implementation can be used by authorities and supervisors of urban countries to bring about cost-effective designs and execution [9]. Urban areas gather valuable information and create vast amounts of data for development, invention, and decision-making [10].
  • Big data modeling and data gathering, virtualization, and structured recognition-based methods are used to consider the commuter’s needs, traffic control, and shifts in prodding behavior. Through the introduction of modern IoT apps, the scale of collected data has increased tremendously. This scenario can be used for various reasons. It may be used to forecast movements in areas with a high population density. In traffic-related scenarios, the most popular applications with huge data sets are cooperative and sharing platforms that enable improved efficiency and control with the use of pre-existing traffic control resources [11,12].
  • The essence of this topic is to empower people to have views and input, as well as to engage in decision-making processes. Cities and neighborhoods welcome the opportunity to work with their residents to cocreate safer and smarter mobility for commuters with respect to new ways of community governance and involvement. It can be used to track road construction and maintenance, account for road incidents, evaluate safety and security issues, gather vehicle-sharing information, and curtail excessive pedestrian occupancy [11,12].
The idea of smart mobility was articulated and presented as a broad, organic system [6,9]. Different types of mobility systems and methods of mobility preparation have been established and described in the literature on transportation planning [10,12]. Smart mobility, for example, is often viewed as comprising approaches that contribute to more efficient transportation systems [2,3,4]. Connectivity is a key feature of smart mobility, which, together with large data, enables consumers to send all travel data instantaneously while members of the local municipal administrations may perform strategic control simultaneously [10]. In different terms, smart mobility is mainly connected to real-time traffic operations, consumer-means administration, applications and logistics monitoring, automobile parking maintenance, automobile allocation services, and numerous other intelligent transport services [7]. Researchers currently emphasize the benefits of training urban administrations and decision-makers for the proliferation of emerging city innovations—be it the IoT, collaborative robotics, a shared market, big data, simulated intelligence, crowd sourcing, drones, or 3D production [1,13].
One further consideration concerns the need for smart mobility systems to be synergistic with all other infrastructure and urban systems that are operating in support of people and their activities. People’s mobility typically occurs in corridors that also facilitate the movement of resources that supply utility services throughout an urban area. Out of convenience, these utility service pipelines and cables are typically buried, and as a result are commonly overlooked when designing service mobility systems. However, when these buried pipelines and cables need to be maintained, repaired, or augmented by providing a new capacity, surface activity is disturbed, sometimes to a considerable degree and for a significant time. Often referred to as a consequence of infrastructure interdependencies, consideration should be given to the provision of local, temporary adjustments to smart mobility systems or, preferably, the introduction of technologies that would limit or remove such disturbances. This includes the mandatory adoption of trenchless technologies to maintain, repair, refurbish, upgrade, or install utility pipelines, aided by the use of robotic systems to assess the condition of the existing infrastructure (e.g., see www.pipebots.ac.uk, accessed on 16 June 2020) so that proactive action can be taken to avoid pipeline failures—the emergency repair of leaking water or gas pipelines often requires trenches to be excavated, thereby causing an unwanted disruption. Smart mobility and smarter engineering of associated infrastructure systems (see www.ukcric.com, accessed on 16 June 2020) must be pursued simultaneously.
The impacts, difficulties, and openings that keen mobilities can offer residents have been introduced, for instance, in various projects, undertakings, and drives that urban areas everywhere in the world have carried out, focusing on sustainable social innovations, open innovation dynamics, open innovation cultures, open business models, car-sharing open innovations, intelligent robot open innovations, and social open innovation.
In this paper, particular attention is paid to clarifying the meaning of the word “smart” in the context of urban mobility. To help clarify the concept, the authors conducted an in-depth literature review of recent and relevant studies. In this analysis, a bibliometric assessment based on the Scopus database preceded the review of the existing literature, enabling the analysis to identify the current research and tendencies of intelligent transport. The article describes international research patterns that include widely cited papers on transportation models from 2010 to 2020 based on a bibliometric review of these publications from the Scopus database’s SCI-E and International journals databases.
This paper is divided into six sections. The following section presents an overview of smart mobility, including its importance and the potential benefits for different stakeholders. Section 3 surveys state-of-the-art approaches and solutions across a broad range of projects for smart mobility systems. Section 4 presents related works within the field. Section 5 revisits the existing methods in the related works on vehicular communication. Finally, we make some concluding remarks in Section 6, with suggestions for future research.

2. Importance of Smart Mobility

Evidence suggests that social infrastructure requires the collaboration of companies who understand the full benefits of the new technology [14]. New stakeholders may be derived from secondary stakeholders [15,16]. Primary stakeholders are defined more by the degree, or purpose, of their participation, and primary stakeholders are those with direct obligations in the management, service, and maintenance of the system [14,17]. However, those with minimal impact on a system’s operating activities and use are referred to as “alternative stakeholders” [18]. Evidently, both the primary and secondary players have positions in smart mobility project planning [19], growth [20], procedures [21], and maintenance [14,20]. All actors with potential involvement in the project must be defined and involved, as shown in Table 3, from the preparation and operations to the maintenance phase of a project [22]. One aim of smart mobility is to engage a broad spectrum of stakeholders and develop local alliances to build a consensus on the priorities and scale of development in smart mobility and collective problem-solving strategies [16]. It is essential to be prepared to respond to possible concerns as an intelligent transport project evolves in what might be an emerging situation [20,21,22,23]. Secondary stakeholders can introduce problems that need to be recognized and addressed [23].
The outcomes of smart mobility projects impact stakeholders in considerable ways. [24,25,26]. This implies that the all-inclusive framework is followed to recognize and involve all stakeholders from the beginning of the project planning of smart mobility projects [14,27]. This may involve new stakeholders, including financial firms, sellers, broadcasters, telecommunications companies, and providers of value-added services [23,24]. Each stakeholder will have a diverse range of corporate practices and objectives; however, at every stage of the project, they must take responsibility for their duties and responsibilities [17].
Table 3 demonstrates different levels of smart mobility advantages among different stakeholders. Customarily, the government sector has been accountable for highway and roadway network service and maintenance [14,15,16]. A public body, such as a road, a roadway agency, or a commission of public works, has traditionally been responsible for planning, construction, operations, and maintenance [15,16]. Public authorities, for example, are exclusively liable for programs supported by ITSs, such as disaster preparedness and traffic signal operations [16].
With the right development planning, state management can make urban areas more fiscally and ecologically viable, more functional, and more sustainable [18]. Efficient transportation also boosts the economic strength of a city [18,19]. Smart mobility is one area where performers—city representatives and administrative staff—can work as a team with providers to increase political influence to maximize benefits, minimize potential risks, and eliminate institutional and governance barriers to communicating the objectives of smart mobility [19].
A smart transport network enables seamless, affordable, and sustainable methods for residents and clients to get to and return from their destinations. The methods under which the system operates expand job prospects and economic opportunity [27]. Residents can experience a convenient, safe, active, balanced, and secure lifestyle because they can pick from many interlinked transit options [27].

3. Good Practices in Smart Mobility

It is expected that the deployment of various technologies will boost transportation in the years to come, depending on expenditures and financing because of the increased awareness of using clean and innovative practices to optimize framework performance [6,7,8,9,10,11,12]. Table 4 shows illustrations of the guiding principles in mobility solutions in many European countries [28]. Electric cars, mass transportation, and parking operations are the domains of urban transportation that have several opportunities to incorporate ICT [28].
Table 4 shows that the latest mass transit innovations include, but are not limited to, smart cars, electric cars, driverless cars, planning tools for coordinating a mass transit system, data collection systems, data analysis tools, numerous real-time information systems, and smart public transit stops [28]. Existing technologies are placed in parking spaces; smart parking apps are meant to optimize parking spots, devices, and sensors for on-road parking options currently being offered; intelligent cards accept parking charges; and parking management platforms organize it all [28]. Popularly rated mobility strategies should offer effective intelligent transport approaches while promoting creativity, fostering a supportive atmosphere, and enabling sustainable development goals [29,30,31,32,33,34,35,36,37]. Table 5 shows that many developed nations are attempting to incorporate the idea of autonomous driving in metropolitan areas [29,30,31,32,33,34,35,36,37]. These frameworks form part of the quickly changing urban transportation environment as shown through the lens of a green technology planner [33,34,35,36,37]. Techniques to meet the challenges of urban transport and to address city mobility issues are unique to each nation (especially its urban areas), and they include:
  • the design of reliable, accessible, safe, and comfortable transport networks, integrated with ridesharing technologies (MaaS) as well as other channels;
  • adaptation to the acceptance and development of vehicles (fully independent, linked, battery powered, communicated, dockless);
  • development of effective public–private partnerships (PPPs) and collaboration with knowledgeable institutions to discuss problems, such as pollution levels, overcrowding, and sustainability; and
  • expansion of new infrastructure—both technical and electronic—to support creative government and industry mobility solutions.
A sequence of illustrations depicting intelligent transport activities was coordinated by civil society organizations, academic institutions, and private entities and is accompanied by quality management illustrations from the cities [32,33,34,35,36,37]. The Shared Personalized Automated Connected Vehicles (SPACE) program will enable towns, users, enterprises, and developers by offering guidelines on how independent cars can be incorporated into mass transit. The research focuses on the idea that if driverless cars are being used as public vehicles incorporated into an efficient mass transit network [29], this would occur. One of the main problems with transportation modernization is the failure of community electrical networks to provide charging stations. Power technology is increasingly constrained, and affirmation of the network can be expensive. Consequently, the autonomous car project, Fleet-Center for Local Power Systems (EFLES), aims at the smart enhancement of corporations by increasing hybrid car fleets. The plan seeks to show how a smart grid will enable massive fleet companies to take the plunge to hybrid cars and, in effect, considerably reduce the expenses of carbon dioxide emissions, environmental damage, and energy [30].
The proposal provides an exciting opportunity for metro stations to fulfill their requirements as an essential component of human livelihoods and to react to the learners of both independent users and organizations. Equally, this plan offers an exciting opportunity to go further than traditional legislation and explore how new and creative transportation systems might be designed [33]. State-of-the-art modern media ensure a secure and productive operation in Germany. The Hamburg Port Authority’s management systems are world-leading, while the relationship among sensing technologies and analytics, prediction, and data analytics provides huge improvements in efficiency. The administration is increasing the performance of the harbor owing to smart approaches to the flow of vehicles and goods [34]. The world city Teams Strategy is exploring a variety of incentives that emerging mobility-related technology brings to the United States. They offer information on the preparation of these innovations and their effect on cities and public development, providing possible short- and long-term changes in health, affordability, the economy, jobs, and congestion. The first phase and the last phase of a strategy are meant to lead communities to a desirable future of metropolitan mobility [35].
The Federal Transit Administration (FTA) has established an on-demand mobility model (MOD) to simulate a multimodal, distributed, standardized, open, and wired transportation system across the United States. The MOD enables travelers to use on-demand data, real-time data, and predictive modeling to make design choices that ideally fit their needs and circumstances. The MOD consolidates innovations that enable a traveler-centered solution and offers users improved vehicle options [36]. As the popularity of smart transportation initiatives keeps rising, there is also a huge demand to formulate and maintain tools and predictors that effectively evaluate the performance of such modalities [38,39,40]. It is elucidated in part by the numerous advantages that smart mobility evaluation actions can offer to similar actors and stakeholders [14,15,16,17,18,19,20,21,22,23,24,25,26,27]. Some of these tools and indicators can be found in Table 6. The various indicators of metropolitan competence (connectivity, prosperity, ICT, public transportation automobiles, and innovative transport strategies; transport and transportation support measures, information gathering, and storage and analysis structures; experience and data targeted at designing, implementing, and evaluating intelligent transport policies and interoperable programs; mass transit, bike paths, bike rentals, and ridesharing; and the confidential transportation support network and the mass transit support network) require a concise description of what encompasses smart mobility, what its characteristics are, and how it works in comparison to standard cities [38,39,40].
Table 6 demonstrates that smart mobility requires the journey alone and the journey’s efficiency, taking into consideration the town’s key sustainability variables. There are several transportation resources and procedures for intelligent cities that can be considered and adopted by urban centers. The table explains many methods and initiatives funded by leading foreign unions [38,39,40] that provide in-depth information, effective techniques, and processes used in the transportation plans for intelligent cities. In this segment, the recommended solution is extracted from inspiring network-supporting organizations [39].

4. Research Design

This section examines the methodology adopted in this article. In this part, consideration will be given to the methodological decisions of this exploration. These decisions are important to the execution of this exploration. It comprises an examination system, an exploration plan, an assortment of tests and information (a literature survey and a subjective and quantitative methodology), the utilized measurable techniques, an evaluation of the legitimacy and dependability of the investigation, and, finally, exploration suggestions.
This article reviews the literary works over the period 2002–2018 on the advancement of detection frameworks for smart mobilities. We briefly talk about the foundation, and innovations that help the utilization, of sensor networks in smart mobility observation. At that point, we review various existing methodologies for the sending issues and the overseeing issues. The consequences of the coding obviously indicate that the range of social, conduct, and cultural issues connected to smart mobilities remain under-investigated. Finally, it ought to be noted that while there were more scholarly reports in the group of articles, there were more all-out coding cases for dark writing, showing that the dim writing records would in general be more far-reaching and cover a wider scope of subjects. On the other hand, numerous scholastic articles only zeroed in on one or a few subjects.
A database search utilizing the University of Laapeenranta’s Online Library, Web of Knowledge (ThomsonReuters), EBSCO, and Google Scholar was attempted in order to access applicable articles. The articles’ theoretical substance, catchphrases, and presentation were respected in the choice about the consideration of the particular scholastic work in this study. The literature audit comprised two focal viewpoints: (1) acquiring and organizing data in the specific field; and (2) performing basic examinations and discovering holes in definitions, depictions, differentiations, and likenesses to inspire analysts to close these holes.
The point of the database inquiry was to study a range of data sets—scholastic and non-scholarly—to discover important material related to conduct, social, and cultural issues of smart mobilities. Additionally, pertinent dim writing material was found in a range of non-scholarly databases and existing reference indices. Chosen keywords and their equivalent words were utilized to attempt the search. This obviously indicates that the most savvy and versatile examination, essentially scholastic, should zero in on specialized and mechanical parts of smart mobilities and not the related social, conduct, and cultural issues. The speed at which innovation in smart mobility is occurring highlights the criticalness of the need to comprehend the social and conduct ramifications of this technology. Given that the most significant scholastic data sets were completely studied, the quantity of scholarly sources distinguished in this investigation is probably going to be illustrative of the absolute number of existing scholastic sources written in English.
This review sums up the different subjects and points that have been tended to or talked about in the scholastic literature and in the dim writing, identifying the conduct, social, and cultural parts of smart mobilities. The examination likewise features the holes in the literature connected to these points. To identify the material relevant to the subject of the examination, a range of scholarly and non-scholastic databases have been overviewed, yielding more than 50,000 outcomes. This initial search was then limited in order to distinguish material straightforwardly pertinent to the point and a total of 432 reports were at last chosen for assessment. These archives incorporated a range of scholarly and dim writing (for example consultancy or research organization reports). Each one of the 432 reports was broken down, screened based on title and dynamic, and coded according to topical codes; altogether, around 100 topical codes were made. Following this, a top-to-bottom examination of the most important archives (more than 60) was attempted. The consequences of the screening and the comprehensive investigation were then cross-referred to deliver a literature survey dependent on key topics.
Through Google Scholar, we had the option of alluding to articles and other exploration sources, such as meeting procedures, theories, books, book sections, reports, and qualified working papers. We created far-reaching search strings using the following keywords: “smart city(ies)”, “resident driven”, “resident centricity”, “individuals (focused)”, “resident interest”, and “citizenship responsibility(ies)”. Therefore, rehashed singular searches were more coordinated with respect to changing the outcomes over to an expert book-keeping page for information blending and investigation. Specifically, ISI articles distributed since 2008 were utilized for this study. In light of the absence of speculation and examination about this issue and field, contextual investigation is a legitimate technique for investigating this field.
Scientists should endeavor to stay away from inclination and reviews ought not be affected by including certain data that could prompt error or the discarding of some type of proof. The outcomes ought not be impacted by points of view, qualities, responsibilities, or individual interests and the analyst should take a nonpartisan position. Analysts should pursue unwavering quality, method reliability, relentlessness, and trustworthiness and be “worried about whether or not one’s discoveries will be found once more”.
Distinctive definitions of the term ‘smart mobility’ have been proposed in a few journal articles having a place in different journal classes in the Web of Science since 2008. We planned a nitty gritty writing network for utilizations of shrewd urban areas dependent on assessments of smart mobility topics, proposed techniques, benefits, and limits.
All of the investigations found through the inquiry by utilizing the chosen catchphrase strings were consolidated in an expert Excel worksheet. In the second phase of screening, copies were erased. As the Google Scholar database is algorithmically autogenerated, duplications should have been physically erased by filtering the worksheet. Furthermore, we excluded papers with missing or unimportant title sources (i.e., PowerPoint introductions, white papers, book presentations, calls for papers, rivalry declarations, and all non-English works).

5. Approaches of Previous Smart Mobility Researchers

In principle, a bibliometric study is conducted to determine both research strategies and the academic structure in various disciplines of science [41,42,43]. The form of study may provide guidance to new scholars interested in similar fields [41,42,43]. Additionally, it can provide support to some previous research while refuting research from other scholars [43]. Previous bibliometric analyses identified the characteristics of frequently cited papers on different subjects, such as intelligent town mobility [2], city logistics success indicators [44], urban sustainability [45], and sustainable urban development [46]. A growing number of researchers are writing widely cited papers and most studies require worldwide collaboration [46].
Research papers were the primary research subject for intelligent transport studies, sequential explanatory assessments, cross-sectional studies, and empirical evidence. The previous articles used a wide range of research methodologies, such as laboratory trials, large-scale, data-based, and policy-oriented analyses, and face-to-face interviews (see Table 7).
These research papers covered all forms of individual modeling and estimations of intelligent transport metrics for city ratings with respect to accessibility and efficiency [47,48,49]. Evaluations of research articles analyzed different guidelines for legislative initiatives [50,51]. As anticipated, the authors were selected with respect to the research objectives for this paper [48,49]. For example, numerical simulations were primarily used to provide quantitative techniques, which can be used in a diverse range of situations, and problem-solving was conducted in a well-defined, theoretical, and researched procedure of calculation [51]. Similarly, other researchers have developed simulation tools to analyze and forecast the complex unraveling of occurrences or procedures after analysts have set some parameters for the research [48].
Quantitative evaluations provided all forms of analytical smart mobility studies using mathematical or computational methods. The four major performance test materials, used only to empirically assess innovation initiatives, were sensor data, signal timing data, dynamic programming, and a synthesized signifier [25,28,30,50].
Empirical evaluations identified the major trends in research journals containing articles on urban smart mobility [2,7]. Analysis of the available research has contributed to the computation of the latest projections in urban smart transportation research [2]. The research approach is summarized in a diagram explaining the real outcomes of this methodology and the actual empirical and applicable literature of the technical developments that exist today in their particular development stage or during the phase of industrial implementation [2,7].
Some policy-oriented research on the growth of smart transportation has been conducted over the last five years [17,19]. Policy-oriented work is intended to educate facets of the mechanism of public transportation legislation, covering decision-making and legislation formulation, implementation, and evaluation.
Causal-comparative projects involve evaluating two factors and analyzing the association between them, without an independent variable being manipulated. Co-relational work is not characterized from where the data are obtained. For example, a study by Tan and Kamruzzaman [1] on a multinomial multiple linear regression model inside a data panel structure explored the effect of increased access to broadband Internet connectivity on the selection of a sustainable transportation mode by local governments.
Although the potential to incorporate smart mobility—using static and wireless detectors to generate big data and to learn how to manage transportation usage—is growing, there remains a large gap between the sustainability objectives of smart mobility. For example, encouraging less individual usage of cars during peak hours is aligned with the ability to endorse progressively more individualized changes in multimodal transportation to accomplish these objectives [41].
Turetken et al. [24] introduced seminars for the development of economic models to tackle the accessibility issues facing a number of cities in Europe. To tackle the problems of regional transition that a number of Western cities face, they arranged a set of enterprising concept layout sessions with the involvement of sector practitioners employed by transportation companies and in relevant domains. Inside the project timeline, they organized an economic model architecture session with the involvement of a diverse range of stakeholders, including public, private, and individual stakeholders [24].
The research involved a study of univariate multiple linear regression models inside a board data system with the aim of exploring whether increasing access to flexible Internet connectivity gives rise to the efficient provision of mass commuting facilities by Australian local authorities in various regions [1]. The study attempts to identify the key developments in research journals that define innovation initiatives in urban areas. The study culminated in the identification of emerging technological developments in urban mobility solutions [2]. The book seeks to deliver a broad introduction to fundamental but useful publications related to transportation networks and is addressed to computer programming and information systems scholars [6]. Giannopoulos explores the potential for the use of data and ICT in the hauling industry during the current decade. The author evaluated different implementations under the following four headings: infrastructure service and control (all modalities); user information and advice (on transportation networks); road freight operations; and maintenance services [7]. The authors offer an open-source intelligent road regulatory structure, called Intelligent-GH, that uses open-source software and participative monitoring, in which people are actively engaged in the collection of city records from their daily environment, such as sound and air quality [9]. The article seeks to shed light on the MaaS definition and what defines a “MaaS operation” and suggests a MaaS topography as a platform to enhance MaaS discourse [13]. The article implements the premise of ensuring and boosting shared trust as a key goal of transformational policy-making. It points out leadership models and approaches that could be applied to guide the changeover and, using four examples with priority, discusses how real issues in mobility policy-making can change [17]. By focusing on interconnected bicycle initiatives, the authors explore the unequal treatment of target audiences and the impact of leadership on Dutch intelligent transport policy [19]. The research suggests the use of provider-dominant business processes that stress the value-chain participant’s engagement as they cocreate value via collaborative networks. It involves the implementation and analysis of an intelligent transport environment and discusses the design of new business models for the collective transport of passengers and products for technology transformation [24]. The framework allows the customer to understand the motive of the difference in travel time (TTV) and to ascertain the system-wide benefits of the variability and authority most needed [25]. The study is an analytical study of 11 Italian cities and explores to what degree the smart city model, when applied to the infrastructure market, is capable of improving existing urban productivity and sustainability [28]. The report outlines a quantitative approach used by a synthetic metric to measure transportation systems in Cagliari and proposes steps that Cagliari should take to meet primary international transportation habits [30]. The McKinsey and Company corporate study aims to recognize the most critical elements of transportation that make transportation networks work, or not, and to contrast them throughout 24 international cities as a way to help leaders understand what they need to do to enhance the health of their cities [47]. The study seeks to aid municipalities and countries in designing tomorrow’s expanded transportation environments and promotes an open conversation among stakeholders interested in urban transportation. The objectives of this research are to share insights and feedback with transportation decision-makers and participants on the establishment of green interventions that address ongoing and emerging issues in transportation [48]. The study included breakout sessions on various threats and opportunities, including obtaining and handling study results, affordability and fairness, creative business models, and the movement-on-request policy (e.g., the urban environment, property use, and the ‘building privileges’ way of governance) [49]. Their research focuses on the design, implementation, and ultimate usage of smart techniques, equipment, and software to recognize mobility in a modern city. As an example of developing this cyber–physical network at a cheap price, they concentrate on intelligent campus research at the University of Malaga [50]. The report aimed to conduct a detailed analysis of the role of ITSs in promoting city-wide smart mobility, highlighting the critical knowledge gaps and detailing the limitations of the study. A total of 71 papers were extensively analyzed; they concentrate primarily on technology, with little exposure to create interest [51]. The study had the objective of exploring the extent to which smart and sustainable mobility are compatible with one another through the implementation of studies among critical intelligent mobility actors. Investors trust that advances in technology alone will contribute to sustainable mobility, especially with regard to self-driving vehicles [52]. The study outlines the creation and assessment of a revolutionary digital mobility aid for the visually impaired [53]. The individual integrated mobility aid, called PAM-AID, was created to overcome the challenges faced by vulnerable, aged, and sight-disabled individuals in transportation [54]. The study of location-based data helps us determine that resources may be useful to people at a given time, for example, by increasing the ability of people to access far more effective routes and travel modes. To this end, detectors may be used in road networks to identify and track a wide range of transport-related operations [55]. The suggested taxonomy serves as a tool to direct decision-makers by defining a continuum of mobility services, the individuals to whom they can be offered, which technology can be used to provide them, and the public benefit generated to support their deployment [56]. The authors explain the implementation of the trip zoom framework introduced as part of the SUNSET—Integrated Social Transit System Services—program that aims to research and create a fixed and wireless traffic sensor system to promote in-person transportation transitions. Its key breakthrough is its capacity to use remote sensors to identify different urban transport forms and create information-rich and flexible accounts for individuals and groups [57]. The authors implement a new mechanism in which client identification and context-based sorting of information both comply with the development recommendations. Upon defining and implementing the UTravel framework, they present the findings of an experimental investigation, including simulated as well as real users, that they carried out [58]. Smart mobility is the subject of this paper with a focus on enhancing one of its key elements: placement. The authors build and introduce a novel architectural framework that is coupled with an outside control system to enable smooth indoor and outdoor movement and path determination [59]. The study primarily contributes a detailed definition of prospective mass-transit-utilizing automated vehicles. The modern transit system framework and the IT framework, as well as road and information regulation, are interconnected [60]. With the key problems and planned improvements given the expectations for 5G cellular networks, the authors suggest many flexible management systems. Via optimizing the time period, the emerging intelligent transport strategies will reduce the downtime between communications systems and improve direct device-to-device (D2D) and end-to-end (E2E) bandwidth [61]. The aim of the analysis is two-pronged: to evaluate the smartness functionality of Ghanaian cities and to show how the definition can be operationalized to reduce several of the detrimental consequences of urbanization in these cities. An analytical structure was used to appraise the transportation smartness of Ghanaian municipalities [62].

6. Organizational, Technical, and Social Requirements for Intelligent Transport Performance

The existing functional concepts of autonomous driving and a variety of existing theoretical relations related to intelligent mobility [63] describe a variety of similar, multifaceted components of intelligent transport frameworks and the key factors for an effective intelligent vehicle campaign. ‘Smart mobility’ is a term that exceeds common themes that traditionally lack the holistic approach, concentrating solely on enhancing human, technical, or structural factors [9,16,26]. In this respect, intelligent mobility is accomplished by the use of a comprehensive method for the formulation and management of ideas, innovations, and leadership, each allowing for resilient and sustainable towns to be achieved [9,26].
Table 7 shows how conceptual intelligent transport frameworks can be defined. On one side, there is the techno-centric method (championed by the ICT industry), by which metropolitan procedures and services in a connected city can be rendered more productive and effective [2,16,24,63]. Data-gathering efforts and the conversion of the gathered data into knowledge via efficient tools of analysis enable an improved economic status. However, the definition takes a comprehensive approach, which combines it with a correlation among individual, institutional, and technical factors [9,26]. Table 7 offers practical concepts that resonate with the three main elements of mobility solutions (technology, citizens, and organizations): the convergence of infrastructure, services, and technology-mediated systems; the growth of a city’s mobility to enhance human capacity; and leadership for institutional improvement and citizen participation. Turetken et al. [24] emphasize that technical challenges are at the core of intelligent mobility’s social, economic, and political challenges, often creating issues, but more often providing opportunities and remedies. Turetken et al. [24] continue to make some interesting observations about the potential position of innovation from this viewpoint. The technical aspect refers to a new age of interconnected equipment, software, and network innovations, which provide real-time IT frameworks with real-world awareness and intelligent systems that help clients make smarter choices regarding options and activities that will optimize business processes and a company’s profitability [24]. The forces driving intelligent transport projects are attuned toward the inclusion of ICT [16,63]. Many authors have introduced innovative programs and initiatives called “smart city projects” to better serve people and enhance their standard of living [9,63]. Many stakeholders are involved in these benefits [16,26].

7. Discussion

Smart mobility is an inclusive, efficient, and sustainable form of transportation. The concept of smart mobility emerged organically. There are different systems that have been documented in the process of transportation planning. The term ‘smart mobility’, as it is currently used, might not cover the broad range of applications of the term. As it currently stands, the global community has not arrived at a single definition of smart mobility. This research finding was unexpected and suggests that it is evidence of the fact that it is possible to define the concept of smart transportation.
With the current rapid rise in smart cities around the world, there is a need to embrace the idea of smart mobilities. While smart mobility is aimed at primarily serving society, its role in providing solutions to contemporary transportation networks is inevitable. The rapid adoption of smart mobility technology in various sectors has paved the way to a great opportunity for advancement in the transportation sector. The importance of smart mobility varies from improving the efficiency of the traveling to the reduction of crashes caused by human beings. For example, autonomous driving has been a dream since the invention of the first automobile. With the current advancements in smart mobility technology, this is no longer a dream but a reality.
The information obtained from research papers was also important. One aspect of smart transport is that it is backed by technology (i.e., tools and techniques created in the ICT industry). This aspect makes it possible for a city to be connected through effective and productive transportation. The second important aspect of smart transportation is its comprehensive approach. This aspect factors in individuals, institutions, and technology and how they can integrate to make smart mobility possible. The three important elements that should be part of all mobility solutions—organizations, technology, and people—are all considered in the previous articles. The article covers concepts such as how the growing mobility of cities will enhance human capacity; how infrastructure, services, and technology-powered systems will converge successfully; the participation of citizens; and the importance of having the right leadership to facilitate the improvement of institutions. Most publications portray the concept of smart mobility as a managerial or technical issue. Academia has responded to the challenges of modern urban transport with a holistic approach. The rapid growth of ICT is a major advantage toward actualizing smart mobility for many countries because they can use technological devices and services to manage transport systems. Sensor data, dynamic programming, synthesized signifiers, and signal timing data were the test materials used to offer quantitative information on the performance of mobility systems. Given how useful numerical simulations are in providing quantitative information for research, these techniques are important to providing calculations during research, solving problems in a manner that is well defined, and producing sound solutions. The previous articles had diverse approaches to research (i.e., empirical studies, cross-sectional studies, and explanatory assessments). Some articles analyzed policies on smart mobility and collected data by conducting face-to-face interviews, while others posted results from laboratory tests. The outcome of this article may have a significant impact on the role of stakeholders. The different stakeholders involved are all bound to achieve their different corporate objectives. However, in the different stages of development of smarter modalities of transportation, stakeholders are expected to be held accountable for smart projects. Success indicates the effectiveness of the framework put in place by the stakeholders when they see the project come to fruition—from the planning phase of the project to its completion. In developing this system, all available stakeholders are expected to participate in the process to help develop a consensus on such factors as priority and the scale of the project. An inclusive approach to developing smart mobility solutions also produces superior problem-solving. Therefore, the concept of smart mobility is a complex and long-term vision of a more efficient urban mobility, factoring in the rising growth rates of urban populations. Moreover, the concept is largely supported by ICT, which implies that both forward and backward applications can be applied to support optimized traffic flow and improve the quality of urban transportation.

7.1. Smart Mobility: Now and into the Future

The future of smart mobility is broad, complex, and involves significant uncertainty and challenges. For example, currently there is a vast amount of intellectual effort and resources being invested in the development of autonomous vehicles. However, there are competing technologies and systems under development that are likely to evolve faster because they face less challenges than those involved in the development of fully autonomous and safe vehicles. Examples of competitive modes and alternatives include flying cars, virtual traveling, electric bicycles, and active transportation. Similarly, as society evolves, the demand for transport and associated activities is likely to change; therefore, the adoption of alternative and more sustainable travel modes should be encouraged and pursued.
The truth of changing mobility is, as of now, clear. Various patterns, from energy decentralization to the Internet of Things, are probably going to converge to produce radical changes in versatility frameworks over the course of the following 10 to 15 years. These progressions will permit individuals to travel more proficiently, more efficiently, more frequently, and in an unexpected way. The versatility frameworks of the future are probably going to be altogether different from those that currently exist. By 2030, we anticipate that a number of additional systems will be at the main edge of the subsequent period of cutting-edge mobility. In broad terms, the best systems will integrate shared versatility, self-rule, and charging with energy frameworks, public vehicles, and a foundation. In explicit terms, urban communities will explore these potential outcomes in an unexpected way. Neighborhood conditions—such as populace thickness, abundance, the condition of streets and public travel frameworks, contamination and clog levels, and nearby administration abilities—will determine what changes happen, and how rapidly.

7.2. Smart Mobility and Open Innovation

The investigation shows that another way to deal with open innovation is arising. This methodology joins technological advances with individuals, metropolitan domains, and different urban areas and will probably become progressively more compelling in the future. We think that this methodology of utilizing open innovation to share dreams, information, abilities, experiences, and methodologies for planning the conveyance of administrations, products, and strategies in urban areas will be successful, effective, and manageable. Notwithstanding, predictable systems, standards, and key plans are required in order to ideally tie these components together. Digitalization, open structures, and open information are needed to help these cycles of open metropolitan advancement in an ICT-empowered city. One further thought concerns the requirement for brilliant portability frameworks to be synergistic with the existing foundation and metropolitan frameworks that are working on the side of individuals and their activities. Individuals’ movement commonly happens in corridors that likewise work with the assets that utility administrations use all through a metropolitan territory. Out of comfort, these utility pipelines and links are normally covered, and subsequently are generally disregarded when planning administration versatility frameworks. Be that as it may, when these covered pipelines and links require maintenance, fixing, or an increase in capacity, the surface movement is upset, in some cases to an extensive degree and in a critical time frame. Regularly alluded to as a result of foundation interdependencies, thought ought to be given to the arrangement of the neighborhood, brief acclimations to smart mobility frameworks, or, ideally, the presentation of technologies that would restrict or eliminate such an unsettling influence. This incorporates the conservative reception of trenchless advances to look after, fix, renovate, overhaul, or introduce utility pipelines, supported by the utilization of automated frameworks to survey the condition of the current foundation so that a proactive move can be made to avoid pipeline disappointments; in a crisis, the fixing of spilled water or gas in pipelines frequently requires channels to be unearthed, in this way causing an undesirable interruption. Smart mobility and a more brilliant design of related frameworks should be sought after. The effects, challenges, and opportunities that smart mobilities can offer to occupants has been presented, for example, in different activities, endeavors, and drives that metropolitan regions around the world have completed, zeroing in on manageable social innovations, open advancement elements, open development societies, open business models, vehicle-sharing open innovations, intelligent robot open innovations, and social open innovation.

8. Conclusions

These are but a few advantages that may come to be obtained if municipalities joined hands with private stakeholders to fulfill this vision. Arguably, the idea is to create a self-sustaining transportation network with the ability to operate without a human operator. This technological approach may present a solution to the increased demand for infrastructure and maintenance. According to the research, an intelligent transit network has more economic advantages than a human-monitored transit network, which requires social skills and capability. Equally, instead of incorporating many policies, encouraging vehicle owners to maintain specific agreements with the necessary authority controlling autonomous driving should be encouraged. Generally, this lowers the hassle that comes with intense policy requirements in the transportation sector. This is a relatively new concept in the sense that it is an integrative approach that uses holistic and system-level perspectives to deal with the complex problem of mobility around urban centers. For optimal efficiency, the strategy requires the involved authorities to leverage big data to engage citizens with the operations of the intelligent transport system.
Smart transportation will only become more popular as time goes by. This is evidenced by the increase in initiatives pushing for smarter transportation options. With such demand comes the need for the scientific community to create tools they can use to evaluate how practical, effective, and safe these new modalities of transportation will be for the general public. Such evaluations will protect the different stakeholders involved in the process of developing smart transportation options. Its wide range of benefits surpasses any other transportation solution in the current, and future, transportation sphere, especially with innovation and flexibility as transportation elements. As such, smart mobility remains the solution for transportation systems for future cities. This paper may be useful to policy-makers to facilitate the conceptualization of smart mobilities, to plan incentives for their development, and to monitor the smart progress of future transportation systems.

Funding

The authors gratefully acknowledge the financial support of the UK Engineering and Physical Sciences Research Council under grant numbers EP/J017698 (Liveable Cities–Transforming the Engineering of Cities to Deliver Societal and Planetary Wellbeing), EP/R017727 (UKCRIC Coordination Node), MR/T045353 (REPLENISH–REimagining PLaces and ENgineered Infrastructure Systems for Health), and EP/S016813 (Pipebots–Pervasive Sensing for Buried Pipes).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

Ethical Consent

All subjects gave their informed consent for inclusion before they participated in the study. The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Ethics Committee of EP/J017698 and EP/R017727.

References

  1. Yigitcanlar, T.; Kamruzzaman, M. Smart cities and mobility: Do the smartness of Australian cities lead to sustainable commuting patterns? J. Urban Technol. 2019, 26, 21–46. [Google Scholar] [CrossRef]
  2. Tomaszewska, E.J.; Florea, A. Urban smart mobility in the scientific literature—Bibliometric analysis. Eng. Manag. Prod. Serv. 2018, 10, 41–56. [Google Scholar] [CrossRef] [Green Version]
  3. Aletà, N.B.; Alonso, C.M.; Ruiz, R.M.A. Smart mobility and smart environment in the Spanish cities. Transp. Res. Procedia 2017, 24, 163–170. [Google Scholar] [CrossRef]
  4. Allam, Z.; Newman, P. Redefining the smart city: Culture, metabolism and governance. Smart Cities 2018, 1, 4–25. [Google Scholar] [CrossRef] [Green Version]
  5. Gabrys, J. Programming environments: Environmentality and citizen sensing in the smart city. Environ. Plan. D Soc. Space 2014, 32, 30–48. [Google Scholar] [CrossRef] [Green Version]
  6. Bazzan, A.L.; Klügl, F. Introduction to intelligent systems in traffic and transportation. Synth. Lect. Artif. Intell. Mach. Learn. 2013, 7, 1–137. [Google Scholar] [CrossRef]
  7. Giannopoulos, G.A. The application of information and communication technologies in transport. Eur. J. Oper. Res. 2004, 152, 302–320. [Google Scholar] [CrossRef]
  8. Pigni, F.; Piccoli, G.; Watson, R. Digital data streams: Creating value from the real-time flow of big data. Calif. Manag. Rev. 2016, 58, 5–25. [Google Scholar] [CrossRef] [Green Version]
  9. Nallur, V.; Elgammal, A.; Clarke, S. Smart route planning using open data and participatory sensing. In IFIP International Conference on Open Source Systems; Springer: Cham, Germany, 2015; pp. 91–100. [Google Scholar]
  10. Cui, L.; Yu, F.R.; Yan, Q. When big data meets software-defined networking: SDN for big data and big data for SDN. IEEE Netw. 2016, 30, 58–65. [Google Scholar] [CrossRef]
  11. Khisty, C.J. Citizen involvement in the transportation planning process: What is and what ought to be. J. Adv. Transp. 2000, 34, 125–142. [Google Scholar] [CrossRef]
  12. Irvin, R.A.; Stansbury, J. Citizen participation in decision making: Is it worth the effort? Public Adm. Rev. 2004, 64, 55–65. [Google Scholar] [CrossRef]
  13. Yigitcanlar, T.; Desouza, K.C.; Butler, L.; Roozkhosh, F. Contributions and risks of artificial intelligence (AI) in building smarter cities: Insights from a systematic review of the literature. Energies 2020, 13, 1473. [Google Scholar] [CrossRef] [Green Version]
  14. Lakshmanan, T.R.; Chatterjee, L.R. Economic consequences of transport improvements. ACCESS Mag. 2005, 1, 28–33. [Google Scholar]
  15. Chong, M.; Habib, A.; Evangelopoulos, N.; Park, H.W. Dynamic capabilities of a smart city: An innovative approach to discovering urban problems and solutions. Gov. Inf. Q. 2018, 35, 682–692. [Google Scholar] [CrossRef]
  16. Sochor, J.; Arby, H.; Karlsson, I.M.; Sarasini, S. A topological approach to Mobility as a Service: A proposed tool for understanding requirements and effects, and for aiding the integration of societal goals. Res. Transp. Bus. Manag. 2018, 27, 3–14. [Google Scholar] [CrossRef]
  17. Docherty, I.; Marsden, G.; Anable, J. The governance of smart mobility. Transp. Res. Part A Policy Pract. 2018, 115, 114–125. [Google Scholar] [CrossRef]
  18. Jessop, B. Entrepreneurial City. In The Wiley Blackwell Encyclopedia of Urban and Regional Studies; Wiley Blackwell: Hoboken, NJ, USA, 2019; pp. 1–10. [Google Scholar]
  19. Gironés, E.S.; Vrščaj, D. Who Benefits from Smart Mobility Policies? The Social Construction of Winners and Losers in the Connected Bikes Projects in the Netherlands’. In Governance of the Smart Mobility Transition; Emerald: Bingley, UK, 2018; pp. 85–101. [Google Scholar]
  20. Merlin, L.A. Comparing automated shared taxis and conventional bus transit for a small city. J. Public Transp. 2017, 20, 2–7. [Google Scholar]
  21. Anthopoulos, L. Smart utopia vs. smart reality: Learning by experience from 10 smart city cases. Cities 2017, 63, 128–148. [Google Scholar] [CrossRef]
  22. Appio, F.P.; Lima, M.; Paroutis, S. Understanding Smart Cities: Innovation ecosystems, technological advancements, and societal challenges. Technol. Forecast. Social Chang. 2019, 142, 1–14. [Google Scholar] [CrossRef]
  23. Perboli, G.; Ferrero, F.; Musso, S.; Vesco, A. Business models and tariff simulation in car-sharing services. Transp. Res. Part A Policy Pract. 2018, 115, 32–48. [Google Scholar] [CrossRef]
  24. Turetken, O.; Grefen, P.; Gilsing, R.; Adali, O.E. Service-dominant business model design for digital innovation in smart mobility. Bus. Inf. Syst. Eng. 2019, 61, 9–29. [Google Scholar] [CrossRef] [Green Version]
  25. Chen, P.; Sun, J.; Qi, H. Estimation of delay variability at signalized intersections for urban arterial performance evaluation. J. Intell. Transp. Syst. 2017, 21, 94–110. [Google Scholar] [CrossRef]
  26. Zawieska, J.; Pieriegud, J. Smart city as a tool for sustainable mobility and transport decarbonisation. Transp. Policy 2018, 63, 39–50. [Google Scholar] [CrossRef]
  27. Ismagilova, E.; Hughes, L.; Dwivedi, Y.K.; Raman, K.R. Smart cities: Advances in research—An information systems perspective. Int. J. Inf. Manag. 2019, 47, 88–100. [Google Scholar] [CrossRef]
  28. European Commission. The Making of a Smart City: Best Practices across Europe; European Commission: Brussels, Belgium, 2017; pp. 10–256. Available online: https://smart-cities-marketplace.ec.europa.eu/insights/publications/making-smart-city-best-practices-across-europe (accessed on 16 July 2020).
  29. Space. The Road to Better Mobility. 2019. Available online: https://space.uitp.org/ (accessed on 16 June 2020).
  30. Moixa. Driving Forward with Smart EV Fleet Charging: Another Exciting Innovation Project for Moixa. 2019. Available online: https://www.moixa.com/blog-smart-ev-fleet-charging-driving-forward-with-ups/ (accessed on 16 June 2020).
  31. Ministry of Infrastructure and Water Management. Exploring Mobility-as-a-Service: Insights from Literature and Focus Group Meetings Netherlands Institute for Transport Policy Analysis. 2019. Available online: https://maasalliance.eu/wpcontent/uploads/sites/7/2018/11/MaaS-brochure-ENG.pdf (accessed on 16 June 2020).
  32. Smart City PDX. Traffic Safety Sensor Project. 2019. Available online: https://www.smartcitypdx.com/traffic-safety-sensor-project (accessed on 23 June 2020).
  33. Aurecon. Smart Stations, Australia: Putting User Needs at the Heart of Future Station Design. 2019. Available online: https://www.aurecongroup.com/projects/transport/smart-stations (accessed on 16 June 2020).
  34. Hamburg Port Authority. In Hamburg We Are Smart. 2019. Available online: https://www.hamburg-port-authority.de/en/hpa-360/smartport/ (accessed on 24 June 2020).
  35. Global City Teams Challenge. Blueprint for Improving First and Last Mile Connections for Transit and Freight. 2019. Available online: https://pages.nist.gov/GCTC/uploads/blueprints/20180225_TransportationBlueprintFeb24.pdf (accessed on 16 June 2020).
  36. Federal Transit Administration. Mobility on Demand (MOD) Sandbox Program. 2020. Available online: https://www.transit.dot.gov/research-innovation/mobility-demand-mod-sandbox-program (accessed on 26 June 2020).
  37. Caballero, J.; Colclough, A. Best Practice Guide 2017–2018. In European Mobility Week; 2018; pp. 1–15. Available online: https://mobilityweek.eu/fileadmin/user_upload/materials/participation_resources/2018/2018%20EMW%20Best%20Practice%20Guide%20LR.pdf (accessed on 26 June 2020).
  38. Battarra, R.; Gargiulo, C.; Tremiterra, M.R.; Zucaro, F. Smart Mobility in Italian Metropolitan Cities: A comparative analysis through indicators and actions. Sustain. Cities Soc. 2018, 41, 556–567. [Google Scholar] [CrossRef]
  39. Benevolo, C.; Dameri, R.P.; D’Auria, B. Smart mobility in smart city. In Empowering Organizations; Springer: Cham, Germany, 2016; pp. 13–28. [Google Scholar]
  40. Garau, C.; Masala, F.; Pinna, F. Cagliari and smart urban mobility: Analysis and comparison. Cities 2016, 56, 35–46. [Google Scholar] [CrossRef]
  41. Xian, H.; Madhavan, K. Anatomy of scholarly collaboration in engineering education: A big-data bibliometric analysis. J. Eng. Educ. 2014, 103, 486–514. [Google Scholar] [CrossRef]
  42. Ellegaard, O.; Wallin, J.A. The bibliometric analysis of scholarly production: How great is the impact? Scientometrics 2015, 105, 1809–1831. [Google Scholar] [CrossRef] [Green Version]
  43. Li, W.; Zhao, Y. Bibliometric analysis of global environmental assessment research in a 20-year period. Environ. Impact Assess. Rev. 2015, 50, 158–166. [Google Scholar] [CrossRef]
  44. Braga, I.P.C.; Dantas, H.F.B.; Leal, M.R.D.; Almeida, M.R.D.; Santos, E.M.D. Urban mobility performance indicators: A bibliometric analysis. Gestão Produção 2019, 26, 1–17. [Google Scholar] [CrossRef] [Green Version]
  45. Fu, Y.; Zhang, X. Trajectory of urban sustainability concepts: A 35-year bibliometric analysis. Cities 2017, 60, 113–123. [Google Scholar] [CrossRef]
  46. Trindade, E.P.; Hinnig, M.P.F.; Moreira da Costa, E.; Marques, J.S.; Bastos, R.C.; Yigitcanlar, T. Sustainable development of smart cities: A systematic review of the literature. J. Open Innov. Technol. Mark. Complex. 2017, 3, 11. [Google Scholar] [CrossRef] [Green Version]
  47. Knupfer, M.S.; Pokotilo, V.; Woetzel, J. Elements of Success: Urban Transportation Systems of 24 Global Cities; McKinsey Company: New York, NY, USA, 2018; pp. 5–69. Available online: https://www.mckinsey.com/business-functions/sustainability/our-insights/elements-of-success-urban-transportation-systems-of-24-global-cities (accessed on 11 June 2020).
  48. Van Audenhove, F.J.; Dauby, L.; Korniichuk, O.; Pourbaix, J. The Future of Urban Mobility 2.0: Imperatives to Shape Extended Mobility Ecosystems of Tomorrow. 2014, pp. 20–72. Available online: https://www.uitp.org/sites/default/files/members/140124%20Arthur%20D.%20Little%20%26%20UITP_Future%20of%20Urban%20Mobility%202%200_Full%20study.pdf (accessed on 17 June 2020).
  49. Shaheen, S.; Cohen, A. Mobility on demand (MOD) and mobility as a service (MaaS): Early understanding of shared mobility impacts and public transit partnerships. In Demand for Emerging Transportation Systems; Elsevier: Amsterdam, The Netherlands, 2020; pp. 37–59. [Google Scholar]
  50. Toutouh, J.; Arellano, J.; Alba, E. BiPred: A bilevel evolutionary algorithm for prediction in smart mobility. Sensors 2018, 18, 4123. [Google Scholar] [CrossRef] [Green Version]
  51. Mangiaracina, R.; Perego, A.; Salvadori, G.; Tumino, A. A comprehensive view of intelligent transport systems for urban smart mobility. Int. J. Logist. Res. Appl. 2017, 20, 39–52. [Google Scholar] [CrossRef]
  52. Noy, K.; Givoni, M. Is ‘smart mobility’ sustainable? Examining the views and beliefs of transport’s technological entrepreneurs. Sustainability 2018, 10, 422. [Google Scholar] [CrossRef] [Green Version]
  53. Ilarri, S.; Stojanovic, D.; Ray, C. Semantic management of moving objects: A vision towards smart mobility. Expert Syst. Appl. 2015, 42, 1418–1435. [Google Scholar] [CrossRef] [Green Version]
  54. Lacey, G.; MacNamara, S. User involvement in the design and evaluation of a smart mobility aid. J. Rehabil. Res. Dev. 2000, 37, 709–723. [Google Scholar] [PubMed]
  55. Olaverri-Monreal, C. Autonomous vehicles and smart mobility related technologies. Infocommun. J. 2016, 8, 17–24. [Google Scholar]
  56. Cledou, G.; Estevez, E.; Barbosa, L.S. A taxonomy for planning and designing smart mobility services. Gov. Inf. Q. 2018, 35, 61–76. [Google Scholar] [CrossRef]
  57. Poslad, S.; Ma, A.; Wang, Z.; Mei, H. Using a smart city IoT to incentivise and target shifts in mobility behaviour—Is it a piece of pie? Sensors 2015, 15, 13069–13096. [Google Scholar] [CrossRef] [PubMed]
  58. Amoretti, M.; Belli, L.; Zanichelli, F. UTravel: Smart mobility with a novel user profiling and recommendation approach. Pervasive Mob. Comput. 2017, 38, 474–489. [Google Scholar] [CrossRef]
  59. Torres-Sospedra, J.; Avariento, J.; Rambla, D.; Montoliu, R.; Casteleyn, S.; Benedito-Bordonau, M.; Huerta, J. Enhancing integrated indoor/outdoor mobility in a smart campus. Int. J. Geogr. Inf. Sci. 2015, 29, 1955–1968. [Google Scholar] [CrossRef] [Green Version]
  60. Földes, D.; Csiszár, C. Conception of future integrated smart mobility. In 2016 Smart Cities Symposium Prague (SCSP); IEEE: Piscataway, NJ, USA, 2016; pp. 1–6. [Google Scholar]
  61. Ning, Z.; Xia, F.; Ullah, N.; Kong, X.; Hu, X. Vehicular social networks: Enabling smart mobility. IEEE Commun. Mag. 2017, 55, 16–55. [Google Scholar] [CrossRef]
  62. Peprah, C.; Amponsah, O.; Oduro, C. A system view of smart mobility and its implications for Ghanaian cities. Sustain. Cities Soc. 2019, 44, 739–747. [Google Scholar] [CrossRef]
  63. Nam, T.; Pardo, T.A. Conceptualizing smart city with dimensions of technology, people, and institutions. In Proceedings of the 12th Annual International Digital Government Research Conference: Digital Government Innovation in Challenging Times; Association for Computing Machinery: New York, NY, USA, 2011; pp. 282–291. [Google Scholar]
Table 1. Meanings of smart mobility.
Table 1. Meanings of smart mobility.
MeaningSource
Smart mobility is a significant element of a smart city plan.[1]
Smart mobility is the pinnacle of a smart city and is associated with a municipal verdict and technique grounded in communication, information, and technological instruments.[2]
Smart mobility contains a number of actions that enhance users’ mobility by foot, public or private transportation, or any other means of transport. It leads to a reduction in economic costs that are incurred by the environment and time. [3]
Smart mobility is not just the embedding of technology into an urban infrastructure, it also calls for citizens to pursue and relate to their urban surroundings in a smart and rational way.[4]
Smart mobility is generally an approach that aids in the reduction of poisonous fumes expelled into the atmosphere by vehicles and human congestion. Equally, smart mobility aids in raising the quality of transportation in a manner that is environmentally friendly.[5]
Table 2. Definitions of markers for smart mobility in the literature.
Table 2. Definitions of markers for smart mobility in the literature.
Intelligent transport system (ITS)An ITS is a network that helps maximize the use of existing infrastructure through a range of technological means, such as traffic signals, travel planners, smart ticketing, and cooperative systems.[6]
ITSs will make transportation safe, efficient, and sustainable by considering appropriate digital technologies for all types of passengers and freight.[7]
Open-data and open-source transport applicationsOpen-data and open-source technology is an international data portal in which anonymous vehicle and smartphone locations are converted into real-time and historical traffic analysis.[8]
Open-source applications and accessible data help to provide social wellness; however, in a smart city implementation, they also relieve several of the unavoidable privacy concerns. Employment of open data is aimed at providing a global-level understanding of the differing facades of a state and the travel behavior of individuals who live in specific constituencies.[9]
Applications for big data analytics Big data has drawn great interest from business and academia alike. Big data contains such large and complex data sets that conventional database management systems or analysis methods are insufficient to handle them. Big data transportation analytics are now providing valuable solutions in the fields of traffic routing, congestion control, and routing. [10]
Citizen engagement and crowd-sourcing strategies from the ground upPublic participation in the process of traffic management is an effort to ensure that civilians have a proper say in public decision-making. Public participation is central to urban planning. When it comes to the planning and implementation of transport infrastructure, there is a tendency to focus on how to involve the public and on what method should be used.[11]
Citizen participation is recognized as a crucial factor in understanding the full impact of urban planning interventions, but the mechanism is still perceived as complicated, time-consuming, and expensive, with a lack of ability at the community level to execute the support programs.[12]
Table 3. Potential benefits of smart mobility for different stakeholders.
Table 3. Potential benefits of smart mobility for different stakeholders.
Potential Stakeholder Benefits
Public authoritiesLinked mass transit systems have one of the highest levels of potential for dramatically enhancing productivity gains across a city [14].
A well-designed smart mobility strategy provides city leaders with the opportunity to obtain and analyze vast amounts of data—and easily gather meaningful, actionable perspectives [15].
Town, national, or state government entities may affect the social and environmental influence of transportation services; that is, they can affect the actions of passengers by setting requirements for carriers (and individual transportation network operators) to establish incentives for acceptable behavior [16].
State subdivisions of transportationEncourages smart mobility to build enabling architectural, legal, and political structures that support the system [17].
PoliticiansInvestment decisions in smart mobility are playing a crucial role in improving the regional and international productivity of cities to draw new businesses [18].
Smart mobility is an approach in which stakeholders—city leaders, executives, and administrations—will work in collaboration with suppliers to harness political control to maximize victors, minimize potential casualties, and eliminate organizational and structural obstacles to achieve the dream of smart mobility [19].
PlannersReduced congestion, driverless car production, and productive automobile navigation all minimize vehicle-related space requirements in city areas, potentially creating a ground for development [20].
InhabitantsThe expansion of digital infrastructure in communities allows smart mobility to enhance connectivity among citizens [21].
Successful, intelligent transport approaches help a community to recognize transportation trends that will benefit the aspirations, needs, and concerns of citizens [22].
VentureIntelligent transport innovations—for example, intelligent parking control—enable cities to leverage extra funding streams [23].
Investment decisions in smart mobility are playing an increasingly significant role in boosting the competitiveness of regional and international cities to draw innovative businesses [24].
Cargo operatorsSmart mobility offers convergence of road traffic management for urban arteries and metropolitan highways [25].
ResearchersBuilding new smart mobility efficiency strategies [26].
Different highway customersSeveral towns have begun spending on mobility solutions to help promote a healthier transportation community [27].
Table 4. Potential mobility indicators [8].
Table 4. Potential mobility indicators [8].
SpainDeveloping infrastructure for billing
Managing parking
Optimized multiple mode lightweight goods transportation
United KingdomE-Bicycles
Minibus battery-powered service
Filling stations for electric vehicles
Testing driverless cars
Loading stations
Switching gasoline to hybrid cars
Powered cargo bikes
GermanyMeets the growing infrastructure for billing
Intelligent credit cards
Managing parking
Shared hybrid and traditional cars and bicycles
Construction of new multisensory transportation channels to boost e-mobility utilization
Goods swapping and distribution stations
Implementation of an established car-sharing system for e-cars
NetherlandsSmart autonomous car charging by optimizing the use of charging stations
Chipping tickets
Detailed parking space evaluation (real-time parking reference framework)
ItalyQuick-charge architecture secured for a frigate of e-taxis
Swift charging points
Bays to park in
Smooth, hybrid, and battery-powered automobiles
Motor homes
Automobiles with e-logistics
Power cabs
FranceStands on smart charging stations
Chipping cards
Self-driving automated electric shuttle
Car-sharing electric cars
Table 5. Finest smart mobility practices.
Table 5. Finest smart mobility practices.
RegionMeasureDescriptionProjectSource
GermanyComputerized and linked vehiclesProduction and testing of autonomous and connected cars across the globe.The SPACE initiative reflects the concept that they will be implemented in thousands of shared vehicles and incorporated with public transit systems so autonomous cars can lead to greater transportation.[29]
United Kingdom Vehicle electrification Battery advances, energy efficiency, and centralized control of transportation emissions propel vehicle electrification.EFLES aims to optimize the increasing electric vehicle (EV) fleet of shipping companies and to show how wireless grids will incentivize massive fleet companies to go green.[30]
FinlandTransportation as a ServiceTransport as a network is the convergence of different modes of transportation systems into a unified, on-request, open mobility service.The Whim app from Helsinki seeks to provide an alternative to private cars via versatile ride-sharing programs alongside monthly tickets for mass transit trips.[31]
United States Sensor systemsThe aim of collaborative radar systems is to use interaction and networks to enhance highway safety and to prepare it.The highway safety Monitor Project Initiative will test new sensing devices applied to street lighting to analyze the information required for full transparency into how people are driving and where possible trouble spots might exist.[32]
Australia Smart stationsIntelligent stations leverage station capacity as a forum for the creation of innovative low-carbon and climate-friendly technologies and solutions.The project group from Aurecon conducted detailed client assessments, user and rail personnel interviews, and seminars to identify a “smart station” and devise layout criteria.[33]
Germany Smart logistics Employ smart logistics to more efficiently manage the ever-increasing commodity flows, shippers, trans-shipment hubs, forwarders, and recipients. The intelligent PORT transportation driver-assist platform provides stakeholders with the knowledge that is important to them throughout the logistics chain. With the aid of a single, overall smart logistics network, the Hamburg transportation department can successfully monitor the growing mobility of goods. [34]
United States First and last link information management The database offers up-to-date road traffic information by traffic volume station. The purpose of the Global City Groups contest is to identify resources afforded by first- and last-mile vehicles, including connected, low-speed, and driverless driving, and explain how cars and platforms will play a significant role throughout the last mile delivering packages as well as other cargo.[35]
Czech Republic Feasible Technical and Electronic Infrastructure Practices in the nation received functional as well as inspirational motivation. The state is using innovation initiatives to expand knowledge and expertise. A modern smartphone app offers data on a wide range of paths, such as combined modality choices, informing cyclists where bicycles are welcome by bus and train and reminding drivers how often positions are vacant at the closest park-and-ride. [37]
Table 6. List of metrics used in performance indicators for intelligent transport evaluation.
Table 6. List of metrics used in performance indicators for intelligent transport evaluation.
Number of IndicatorsIntelligent Vehicle MetricsSource
28Connectivity: need for mass transit, availability of public transit, mass transit roads, number of bus stations, rail networks, halts in the transport network, and ticket parking
Viability: environmental buses, foot zones, congestion-enclosed spaces, bike paths, environmental vehicles, requests for carpooling, production of ridesharing, the production capacity of ridesharing, and the density of bike sharing
Data communication innovation: traffic signage schemes, variable message symbols, text messaging for road warnings, automated parking payment systems, smartphone software, SMS for data pertaining to public transit, automated bus station signs, digital travel tickets, digital mobile device travel tickets, route maps, maps, dates, local public transport planners, and online tickets
[38]
46Some of the crowd transportation cars and inventive transport solutions employed include hybrid cars, EUR 5 buses, and consumption of renewable energy sources.
Personal and corporate movement: vehicle rental, ride-pooling, and car sharing; bike rental, sable bus connecting, urban navigation, and environmental driving
Mobility hold-up facilities and policies include parks and drives, cyclers’ pathways, pillars for charging self-directed cars, flexibility warning signs, interactive stop signs, pedestrian- or automobile-free zones, controlled transit areas, bus or bus-only lanes, traffic management programs, pace monitoring and management systems, transportation practices focusing on vehicular networks, the level of pollutant emissions, detailed knowledge to help intelligent transport measures, division of traffic patterns, coordinated implementation of booking of pollutant emissions, detailed knowledge to help intelligent transport, strategic division of traffic patterns, coordinated implementation of booking of tax credits, and measures for sustainable mobility.
Other measures include the establishment of a system that monitors those entering restricted areas. These may include cordon charging, congestion costing, digital toll systems, digital GPS tolling, charge-as-you-continue driving, computerized parking navigation systems, variable message signage, Metro Traffic Control, surveillance systems for area and ecosystem security, software solutions for mobility management, and traffic note-taking.
[39]
19Mass transit: concentrations of the transport system, mass transportation usage, stop signs
Cycle lanes: number of cycle tracks, bike lanes for 10,000 residents
Bike exchange: frequency of bicycle stations, a bike per 1000 people
Ridesharing car for 1000 people, a station per population of 1000
Public transit support network: digital bus traffic signals, online ticketing payment method, route information, timetables and queue length, path estimation travel manager, and online travel booking
[40]
Table 7. Conceptualization of smart mobility with technological, human, and institutional dimensions.
Table 7. Conceptualization of smart mobility with technological, human, and institutional dimensions.
ReferenceInnovation FeaturesSocial FeaturesOrganizational FeaturesObjective Users
[2]Industries can gain insights from strategic efforts to understand technical developments and international business growth.Citizens have to be trained with the community’s
talents, and governments should use gamified strategies to reward good conduct and deter bad conduct.
Cities and funding organizations may use this research to compare practical solutions to smart transit systems at regional, national, and international levels.Investigators, townships, and the industry field
[9]Intelligent mobilities are innovation infusions into the system of a town.Compels people to connect with their city.
People need to be inspired to contribute data to the city, thus helping to establish an index at the municipal level.
Many government and public agencies publish information gathered by their data collection and data analysis entities.Inhabitants
[16]Advancements in connected vehicles as well as other modern technologies may require smart mobilities to be controlled in a timely manner.An individual’s mobility platform would include a part that involves
intelligent transport data but also a part that incorporates haulage services.
Examples of structural frameworks for MaaS entail revising economic policies and redistributing subsidies at the city, region, and national level.Communities, commercial entities
[24]Most of the advancements are based on technology-driven, totally remote innovations, including smart trip planning for passengers.An environment where innovation has tremendous potential is smart transport, which enables one to navigate safely and effectively using a large amount of information to a defined geographical location.To design a commercial plan for a company, one must sit alongside venture and public agencies.Businesses
[26]Urban cities are ever-evolving from a technical point of view, and emerging innovations are generating new opportunities for intelligent transport governance.Technological development needs to be backed by social and attitudinal shifts about the habits of mobility.Environmental safety is a critical feature of the distinctly European and international initiatives.Administration
[63]Smart mobility does provide fully integrated, Internet-based state services that allow omnipresent interconnections to reshape critical functions in authorities.An intelligent transport strategy is a method for integrating entire neighborhoods, developing tailored programs to meet community priorities, and enhancing mutual resources and capabilities.From top-down or central planning strategies, an effective smart mobility system can be developed, but active participation from every sector of society is crucial.Regimes, corporations, hospitals, not-for-profit entities, and registered nationals
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Bıyık, C.; Abareshi, A.; Paz, A.; Ruiz, R.A.; Battarra, R.; Rogers, C.D.F.; Lizarraga, C. Smart Mobility Adoption: A Review of the Literature. J. Open Innov. Technol. Mark. Complex. 2021, 7, 146. https://0-doi-org.brum.beds.ac.uk/10.3390/joitmc7020146

AMA Style

Bıyık C, Abareshi A, Paz A, Ruiz RA, Battarra R, Rogers CDF, Lizarraga C. Smart Mobility Adoption: A Review of the Literature. Journal of Open Innovation: Technology, Market, and Complexity. 2021; 7(2):146. https://0-doi-org.brum.beds.ac.uk/10.3390/joitmc7020146

Chicago/Turabian Style

Bıyık, Can, Ahmad Abareshi, Alexander Paz, Rosa Arce Ruiz, Rosaria Battarra, Christopher D. F. Rogers, and Carmen Lizarraga. 2021. "Smart Mobility Adoption: A Review of the Literature" Journal of Open Innovation: Technology, Market, and Complexity 7, no. 2: 146. https://0-doi-org.brum.beds.ac.uk/10.3390/joitmc7020146

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