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      Artificial Intelligence, Transport and the Smart City: Definitions and Dimensions of a New Mobility Era

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      Sustainability
      MDPI AG

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          Abstract

          Artificial intelligence (AI) is a powerful concept still in its infancy that has the potential, if utilised responsibly, to provide a vehicle for positive change that could promote sustainable transitions to a more resource-efficient livability paradigm. AI with its deep learning functions and capabilities can be employed as a tool which 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; the era of the “smart city”. One of the key areas that AI can redefine is transport. Mobility provision and its impact on urban development can be significantly improved by the employment of intelligent transport systems in general and automated transport in particular. This new breed of AI-based mobility, despite its machine-orientation, has to be a user-centred technology that “understands” and “satisfies” the human user, the markets and the society as a whole. Trust should be built, and risks should be eliminated, for this transition to take off. This paper provides a novel conceptual contribution that thoroughly discusses the scarcely studied nexus of AI, transportation and the smart city and how this will affect urban futures. It specifically covers key smart mobility initiatives referring to Connected and Autonomous Vehicles (CAVs), autonomous Personal and Unmanned Aerial Vehicles (PAVs and UAVs) and Mobility-as-a-Service (MaaS), but also interventions that may work as enabling technologies for transport, such as the Internet of Things (IoT) and Physical Internet (PI) or reflect broader transformations like Industry 4.0. This work is ultimately a reference tool for researchers and city planners that provides clear and systematic definitions of the ambiguous smart mobility terms of tomorrow and describes their individual and collective roles underpinning the nexus in scope.

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                Author and article information

                Contributors
                (View ORCID Profile)
                (View ORCID Profile)
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                Journal
                SUSTDE
                Sustainability
                Sustainability
                MDPI AG
                2071-1050
                April 2020
                April 01 2020
                : 12
                : 7
                : 2789
                Article
                10.3390/su12072789
                2cc8a75b-d49b-481f-a85c-8ddadeb7d232
                © 2020

                https://creativecommons.org/licenses/by/4.0/

                History

                Quantitative & Systems biology,Biophysics
                Quantitative & Systems biology, Biophysics

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