Details
| Original language | English |
|---|---|
| Pages (from-to) | 870-899 |
| Number of pages | 30 |
| Journal | IEEE Open Journal of Intelligent Transportation Systems |
| Volume | 4 |
| Early online date | 21 Nov 2023 |
| Publication status | Published - 6 Dec 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.
Keywords
- micromobility, reinforcement learning, shared mobility systems, supervised learning, systematic literature review, unsupervised learning
ASJC Scopus subject areas
- Engineering(all)
- Automotive Engineering
- Engineering(all)
- Mechanical Engineering
- Computer Science(all)
- Computer Science Applications
Sustainable Development Goals
Cite this
- Standard
- Harvard
- Apa
- Vancouver
- BibTeX
- RIS
In: IEEE Open Journal of Intelligent Transportation Systems, Vol. 4, 06.12.2023, p. 870-899.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - A Systematic Literature Review on Machine Learning in Shared Mobility
AU - Teusch, Julian
AU - Gremmel, Jan Niklas
AU - Koetsier, Christian
AU - Johora, Fatema Tuj
AU - Sester, Monika
AU - Woisetschlager, David M.
AU - Muller, Jorg P.
PY - 2023/12/6
Y1 - 2023/12/6
N2 - 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.
AB - 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.
KW - micromobility
KW - reinforcement learning
KW - shared mobility systems
KW - supervised learning
KW - systematic literature review
KW - unsupervised learning
UR - http://www.scopus.com/inward/record.url?scp=85178028571&partnerID=8YFLogxK
U2 - 10.1109/OJITS.2023.3334393
DO - 10.1109/OJITS.2023.3334393
M3 - Article
AN - SCOPUS:85178028571
VL - 4
SP - 870
EP - 899
JO - IEEE Open Journal of Intelligent Transportation Systems
JF - IEEE Open Journal of Intelligent Transportation Systems
ER -