A Systematic Literature Review on Machine Learning in Shared Mobility - research and decision-support frameworks

Aug 2, 2026

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Shared mobility is positioned as a sustainable alternative to private cars and traditional public transport, aiming to reduce vehicle ownership while increasing user flexibility through services such as car-sharing, ride-sharing, and micromobility (bike, moped, and e-scooter sharing). Due to strong competition and operational complexity, providers require specialized decision-support methods. This paper delivers a systematic literature review focused on machine learning for decision-making in shared mobility systems, examining employed methods and datasets, highlighting trends and gaps, and proposing a structured framework to support managerial decisions across different operational levels.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages30

Reading Time1 h 16 min

AccessFree to download

Introduction

Shared mobility benefits and market development

Operational challenges and decision-support needs

Review Scope and Objectives

Machine learning in shared mobility decision-making

Methods, datasets, trends, and research gaps

Received 19 September 2023; revised 18 October 2023 and 14 November 2023; accepted 16 November 2023 . Date of publication 21 November 2023; date of current version 6 December 2023 . Digital Object Identifier 10.1109/OJITS.2023.3334393 A Systematic Literature Review on Machine Learning in Shared Mobility JULIAN TEUSCH1 , JAN NIKLAS GREMMEL2 , CHRISTIAN KOETSIER3 , FATEMA TUJ JOHORA 1 , MONIKA SESTER3 , DAVID M. WOISETSCHLÄGER2, AND JÖRG P. MÜLLER1 1 Institute of Informatics, Clausthal University of Technology, 38678 Clausthal-Zellerfeld, Germany 2 Chair of Services Management, Braunschweig Technical University, 38106 Braunschweig, Germany 3 Institute of Cartography and Geoinformatics, Leibniz University Hannover, 30167 Hannover, Germany CORRESPONDING AUTHOR: J. TEUSCH (e-mail: [julian.teusch@tu-clausthal.de](julian.teusch@tu-clausthal.de)) This work was supported in part by the Lower Saxony Ministry of Science and Culture within the Lower Saxony “Vorab” of the Volkswagen Foundation under Grant ZN3493, and in part by the Center for Digital Innovations. 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. INDEX TERMS Decision-making process, machine learning, micromobility, reinforcement learning, shared mobility systems, supervised learning, systematic literature review, unsupervised learning, I. INTRODUCTION OVER the past decades, shared mobility has proven to be a sustainable alternative to private mobility and established public transport. It promises to reduce the number of private vehicles on the road while offering users flexibility in mobility [1], [2] . Numerous new services have emerged in major cities, ranging from car-sharing, ride-sharing, mopedsharing, bike-sharing to e-scooter-sharing [3], [4] . In this context, Shared Mobility Systems (SMS) offer users shortterm access to meet their mobility needs [5] . Despite initial low usage rates of SMS in the early 2010s [6] The review of this article was arranged by Associate Editor Jiaqi Ma. and a temporary slump in demand due to the Covid- 19 outbreak in 2020 [7], the adoption of SMS has been steadily growing [8], [9] . This trend is fueled by ongoing shifts in consumer behaviors and expectations, driven by advancements in mobile information and communication technologies [8], [10], [11] . However, despite the promising outlook for SMS providers, operating such a business remains largely unprofitable in many cases [12], [13] . Service providers also grapple with

FAQs

What does the review focus on in shared mobility systems?

It focuses specifically on how machine learning is used for decision-making in shared mobility systems, emphasizing operational and managerial challenges.

Why do shared mobility providers need specialized decision-support methods?

Providers face intense competition and complex operations that make efficient service management difficult, so specialized methodologies are needed to improve operational efficiency and decision quality.

What outputs does the paper provide at the end of the review?

The review culminates in a comprehensive framework of machine learning techniques mapped to challenges in shared mobility management across multiple levels.

Aug 2, 2026

Upload by Maeve