Artificial intelligence as a determinant for reshaping the automotive industry and urban mobility services

Fabio Antonialli, Andrea Martinesco and Sylvie Mira-Bonnardel ()
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Fabio Antonialli: LTDS - Laboratoire de Tribologie et Dynamique des Systèmes - ECL - École Centrale de Lyon - Université de Lyon - ENTPE - École Nationale des Travaux Publics de l'État - ENISE - Ecole Nationale d'Ingénieurs de Saint Etienne - CNRS - Centre National de la Recherche Scientifique
Andrea Martinesco: DANTE - Laboratoire de Droit des Affaires et Nouvelles Technologies - UVSQ - Université de Versailles Saint-Quentin-en-Yvelines
Sylvie Mira-Bonnardel: MAGELLAN - Laboratoire de Recherche Magellan - UJML - Université Jean Moulin - Lyon 3 - Université de Lyon - Institut d'Administration des Entreprises (IAE) - Lyon

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Abstract: With the growing interest in smart cities and autonomous mobility, policymakers, citizens, and the automotive industry face a looming shift towards intelligent mobility solutions. By filling a gap of transversal analysis among artificial intelligence (AI), the automotive industry, and urban mobility; the present study discusses how AI has been transforming both the automotive industry and urban mobility globally. By applying a qualitative and descriptive approach, data were collected from primary and secondary sources and analysed from the standpoint of technology, regulations (public policies), and citizens (users). The results show how AI is bringing the traditional automotive industry and the urban mobility sector closer through mobility-as-a-service schemes and illustrate with the city of Curitiba (Brazil) how such AI-enabled technological advances linked with public policies can help design sustainable urban mobility in developing countries.

Keywords: artificial intelligence; automotive industry; citizens behaviour transformation; MaaS; mobility-as-a-service; public policies; technological innovation; urban mobility (search for similar items in EconPapers)
Date: 2022-01-01
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Published in International Journal of Automotive Technology and Management, 2022, 22 (3), ⟨10.1504/IJATM.2022.10048005⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04288753

DOI: 10.1504/IJATM.2022.10048005

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