[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118686-en":3,"doc-seo-118686-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118686,7971461741311,"Ophelia","https://ap-avatar.wpscdn.com/avatar/74000253aff267980c6?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779345379180704826",8,"Research & Report","A Systematic Literature Review on Machine Learning in Shared Mobility","Shared mobility has emerged as a sustainable alternative to private transportation and traditional public transport, aiming to reduce private vehicle use while improving user flexibility. Urban service ecosystems include car-sharing, ride-sharing, and micromobility options such as bike- and e-scooter-sharing. Due to strong competition and operational complexity, providers need decision-support methods. This paper delivers a systematic literature review focused on machine learning for decision-making in shared mobility systems, analyzing methods, datasets, trends, and gaps, and producing a framework supporting managerial decisions across multiple operational levels.","Received 19 September 2023; revised 18 October 2023 and 14 November 2023; accepted 16 November 2023 . Date of publication 21 November 2023;  \ndate of current version 6 December 2023 .  \nDigital Object Identifier 10.1109/OJITS.2023.3334393  \nA Systematic Literature Review on Machine  \nLearning in Shared Mobility  \nJULIAN TEUSCH1 , JAN NIKLAS GREMMEL2 , CHRISTIAN KOETSIER3 , FATEMA TUJ JOHORA  1 , MONIKA SESTER3 , DAVID M. WOISETSCHLÄGER2, AND JÖRG P. MÜLLER1  \n1 Institute of Informatics, Clausthal University of Technology, 38678 Clausthal-Zellerfeld, Germany  \n2 Chair of Services Management, Braunschweig Technical University, 38106 Braunschweig, Germany  \n3 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))  \nThis work was supported in part by the Lower Saxony Ministry of Science and Culture within the Lower Saxony “Vorab”  \nof the Volkswagen Foundation under Grant ZN3493, and in part by the Center for Digital Innovations.  \nABSTRACT 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.  \nINDEX TERMS Decision-making process, machine learning, micromobility, reinforcement learning, shared mobility systems, supervised learning, systematic literature review, unsupervised learning,  \nI. INTRODUCTION  \nOVER the past decades, shared mobility has proven to  \nbe 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]  \nThe review of this article was arranged by Associate Editor Jiaqi Ma.  \nand 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 ","cbCaioxDCdevmrWK","https://ap.wps.com/l/cbCaioxDCdevmrWK","pdf",3990531,1,30,"English","en",105,"# Introduction\n## Shared mobility as a sustainable transport alternative\n## Growth drivers and operational challenges\n## IT platform and data-driven decision tasks","[{\"question\":\"Why is machine learning relevant for decision-making in shared mobility systems?\",\"answer\":\"Providers face intense competition and operational complexity, making specialized decision-support methods necessary. The review highlights machine learning as offering methodological solutions for key management challenges.\"},{\"question\":\"What scope does the systematic literature review cover?\",\"answer\":\"The review specifically targets machine learning applications for decision-making in shared mobility systems. It analyzes methods and datasets, highlights research trends, and identifies research gaps.\"},{\"question\":\"Which shared mobility services are discussed as examples of SMS?\",\"answer\":\"The document references car-sharing, ride-sharing, and micromobility solutions including moped-sharing, bike-sharing, and e-scooter-sharing.\"}]","A Systematic Literature Review on Machine Learning in Shared Mobility | PDF",1785684885,76,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"a-systematic-literature-review-on-machine-learning-in-shared-mobility","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/a-systematic-literature-review-on-machine-learning-in-shared-mobility/118686/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is machine learning relevant for decision-making in shared mobility systems?","Question",{"text":75,"@type":76},"Providers face intense competition and operational complexity, making specialized decision-support methods necessary. The review highlights machine learning as offering methodological solutions for key management challenges.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What scope does the systematic literature review cover?",{"text":80,"@type":76},"The review specifically targets machine learning applications for decision-making in shared mobility systems. It analyzes methods and datasets, highlights research trends, and identifies research gaps.",{"name":82,"@type":73,"acceptedAnswer":83},"Which shared mobility services are discussed as examples of SMS?",{"text":84,"@type":76},"The document references car-sharing, ride-sharing, and micromobility solutions including moped-sharing, bike-sharing, and e-scooter-sharing.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":21,"slug":121},"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]