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.

wish to thank Vitor Duarte Santos and Maria Anastasiadou for their help in the PRISMA methodology. The authors would like also to thank the editorial team and the reviewers who provided constructive and helpful comments to improve the quality of the artic

Bike-sharing systems,Machine learning,Classification,Prediction,PRISMA method

• Earth and related Environmental Sciences - Natural Sciences
• Environmental Engineering - Engineering and Technology
• Social and Economic Geography - Social Sciences