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 -