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Cited by 495Transportation and Mobility InnovationsSmart Cities and Technologiestransportation and logistics systems

Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era

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Nikitas, Alexandros, Michalakopoulou, Kalliopi, Njoya, Eric Tchouamou, Karampatzakis, Dimitris·2020·Sustainability
DOI 10.3390/su12072789
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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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  • DL algorithms can also be used to monitor passenger behavior and detect potential safety issues.

    “DL algorithms can also be used to monitor passenger behavior and detect potential safety issues [87] .”

    From Artificial intelligence, machine learning and deep learning in advanced robotics, a review (2023) · cites this paper as [87]

    The Introduction discusses AI methods in transport, and Section 3 lists improved traffic security through surveillance and monitoring as a potential benefit of connected and autonomous vehicles. Neither passage says that deep-learning algorithms monitor passenger behavior or detect safety issues arising from it. (LLM, conf=0.78)

    Experimental — accuracy not yet measured

  • AI and ML can be used to optimize public transportation schedules and routes, providing passengers with more convenient and efficient services.

    “Intelligent Public Transportation: AI and ML can be used to optimize public transportation schedules and routes, providing passengers with more convenient and efficient services.”

    From Artificial intelligence, machine learning and deep learning in advanced robotics, a review (2023) · cites this paper as [87]

    The Introduction says AI algorithms can support personalised travel planning and optimise the combination of mobility alternatives, including public transit. It does not say that AI or ML optimises public-transport schedules or service routes; a search for scheduling and route optimisation found no such passage. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

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Sorted by citations

Authors
Nikitas, A., Michalakopoulou, K., Njoya, E. T., & Karampatzakis, D.
Affiliations
  • University of Huddersfield (GB)
  • International Hellenic University (GR)
Corresponding author
Nikitas, Alexandros
ORCID
Venue
Sustainability
Publisher
MDPI AG
Published
2020-04-01
Volume
12
Issue
7
Pages
2789
ISSN
2071-1050 (ISSN-L)
DOI
10.3390/su12072789
OpenAlex
W3014834477
Cited by
495
References
11
Citations by year
2026: 60 · 2025: 112 · 2024: 110 · 2023: 99 · 2022: 56 · 2021: 44 · 2020: 14
FWCI
19.10
Type
journal article
Language
en
Open access
Licence
Locations
  • Sustainability (publishedVersion, cc-by)
  • Huddersfield Research Portal (University of Huddersfield) (publishedVersion, cc-by)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Physical Sciences › Engineering › Automotive Engineering › Transportation and Mobility Innovations
  • Physical Sciences › Engineering › Media Technology › Smart Cities and Technologies
  • Social Sciences › Transportation › transportation and logistics systems
Keywords
artificial intelligence, smart cities, smart mobility, intelligent transportation systems, connected and autonomous vehicles, unmanned aerial vehicles, Mobility as a Service, Internet of Things, Physical Internet, Industry 4.0, sustainable urban mobility, urban development

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