A Systematic Literature Review on Machine Learning in Shared Mobility

Publikation: Beitrag in FachzeitschriftArtikelForschungPeer-Review

Autoren

  • Julian Teusch
  • Jan Niklas Gremmel
  • Christian Koetsier
  • Fatema Tuj Johora
  • Monika Sester
  • David M. Woisetschlager
  • Jorg P. Muller

Externe Organisationen

  • Technische Universität Clausthal
  • Technische Universität Braunschweig
Forschungs-netzwerk anzeigen

Details

OriginalspracheEnglisch
Seiten (von - bis)870-899
Seitenumfang30
FachzeitschriftIEEE Open Journal of Intelligent Transportation Systems
Jahrgang4
Frühes Online-Datum21 Nov. 2023
PublikationsstatusVeröffentlicht - 6 Dez. 2023

Abstract

Shared mobility has emerged as a sustainable alternative to both private transportation and traditional public transport, promising to reduce the number of private vehicles on roads while offering users greater flexibility. Today, urban areas are home to a myriad of innovative services, including car-sharing, ride-sharing, and micromobility solutions like moped-sharing, bike-sharing, and e-scooter-sharing. Given the intense competition and the inherent operational complexities of shared mobility systems, providers are increasingly seeking specialized decision-support methodologies to boost operational efficiency. While recent research indicates that advanced machine learning methods can tackle the intricate challenges in shared mobility management decisions, a thorough evaluation of existing research is essential to fully grasp its potential and pinpoint areas needing further exploration. This paper presents a systematic literature review that specifically targets the application of Machine Learning for decision-making in Shared Mobility Systems. Our review underscores that Machine Learning offers methodological solutions to specific management challenges crucial for the effective operation of Shared Mobility Systems. We delve into the methods and datasets employed, spotlight research trends, and pinpoint research gaps. Our findings culminate in a comprehensive framework of Machine Learning techniques designed to bolster managerial decision-making in addressing challenges specific to Shared Mobility across various levels.

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A Systematic Literature Review on Machine Learning in Shared Mobility. / Teusch, Julian; Gremmel, Jan Niklas; Koetsier, Christian et al.
in: IEEE Open Journal of Intelligent Transportation Systems, Jahrgang 4, 06.12.2023, S. 870-899.

Publikation: Beitrag in FachzeitschriftArtikelForschungPeer-Review

Teusch, J, Gremmel, JN, Koetsier, C, Johora, FT, Sester, M, Woisetschlager, DM & Muller, JP 2023, 'A Systematic Literature Review on Machine Learning in Shared Mobility', IEEE Open Journal of Intelligent Transportation Systems, Jg. 4, S. 870-899. https://doi.org/10.1109/OJITS.2023.3334393
Teusch, J., Gremmel, J. N., Koetsier, C., Johora, F. T., Sester, M., Woisetschlager, D. M., & Muller, J. P. (2023). A Systematic Literature Review on Machine Learning in Shared Mobility. IEEE Open Journal of Intelligent Transportation Systems, 4, 870-899. https://doi.org/10.1109/OJITS.2023.3334393
Teusch J, Gremmel JN, Koetsier C, Johora FT, Sester M, Woisetschlager DM et al. A Systematic Literature Review on Machine Learning in Shared Mobility. IEEE Open Journal of Intelligent Transportation Systems. 2023 Dez 6;4:870-899. Epub 2023 Nov 21. doi: 10.1109/OJITS.2023.3334393
Teusch, Julian ; Gremmel, Jan Niklas ; Koetsier, Christian et al. / A Systematic Literature Review on Machine Learning in Shared Mobility. in: IEEE Open Journal of Intelligent Transportation Systems. 2023 ; Jahrgang 4. S. 870-899.
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