International Journal of Geo-Information Review Machine Learning Approaches to Bike-Sharing Systems: A Systematic Literature Review Vitória Albuquerque 1,* , Miguel Sales Dias 1,2 and Fernando Bacao 1 1 NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Campus de Campolide, 1070-312 Lisboa, Portugal;
[email protected] (M.S.D.);
[email protected] (F.B.) 2 Instituto Universitário de Lisboa (ISCTE-IUL), ISTAR, 1649-026 Lisboa, Portugal * Correspondence:
[email protected] Abstract: Cities are moving towards new mobility strategies to tackle smart cities’ challenges such as carbon emission reduction, urban transport multimodality and mitigation of pandemic hazards, emphasising on the implementation of shared modes, such as bike-sharing systems. This paper poses a research question and introduces a corresponding systematic literature review, focusing on machine learning techniques’ contributions applied to bike-sharing systems to improve cities’ mobility. The preferred reporting items for systematic reviews and meta-analyses (PRISMA) method was adopted to identify specific factors that influence bike-sharing systems, resulting in an analysis of 35 papers published between 2015 and 2019, creating an outline for future research. By means of systematic literature review and bibliometric analysis, machine learning algorithms were identified in two groups: classification and prediction. Keywords: bike-sharing systems; machine learning; classification; prediction; PRISMA method 1. Introduction Citation: Albuquerque, V.; Sales Changes are taking place in the future development of the transport sector. To this Dias, M.; Bacao, F. Machine Learning aim, concrete plans are already in place, such as the Sustainable Development Goals Approaches to Bike-Sharing Systems: (SDGs) [1], the New Urban Agenda [2] and the Organisation for Economic Co-operation A Systematic Literature Review.