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Export Reference (APA) 
Albuquerque, V., Dias, J. & Bacao, F. (2021). Machine learning approaches to bike-sharing systems: A systematic literature review. ISPRS International Journal of Geo-Information. 10 (2)
Export Reference (IEEE) 
L. V. Albuquerque et al.,  "Machine learning approaches to bike-sharing systems: A systematic literature review", in ISPRS Int. Journal of Geo-Information, vol. 10, no. 2, 2021
Export BibTeX 
@null{albuquerque2021_1764938548748,
	year = "2021",
	url = "https://www.mdpi.com/2220-9964/10/2/62/pdf"
}
Export RIS 
TY  - GEN
TI  - Machine learning approaches to bike-sharing systems: A systematic literature review
T2  - ISPRS International Journal of Geo-Information
VL  - 10
AU  - Albuquerque, V.
AU  - Dias, J.
AU  - Bacao, F.
PY  - 2021
SN  - 2220-9964
DO  - 10.3390/ijgi10020062
UR  - https://www.mdpi.com/2220-9964/10/2/62/pdf
AB  - 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.
ER  -