Utilize este identificador para referenciar este registo:
http://hdl.handle.net/10071/22120
Autoria: | Albuquerque, V. Dias, J. Bacao, F. |
Data: | 2021 |
Título próprio: | Machine learning approaches to bike-sharing systems: A systematic literature review |
Volume: | 10 |
Número: | 2 |
ISSN: | 2220-9964 |
DOI (Digital Object Identifier): | 10.3390/ijgi10020062 |
Palavras-chave: | Bike-sharing systems Machine learning Classification Prediction PRISMA method |
Resumo: | 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. |
Arbitragem científica: | yes |
Acesso: | Acesso Aberto |
Aparece nas coleções: | ISTAR-RI - Artigos em revistas científicas internacionais com arbitragem científica |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
---|---|---|---|---|
ijgi-10-00062 (1).pdf | Versão Editora | 4,28 MB | Adobe PDF | Ver/Abrir |
Todos os registos no repositório estão protegidos por leis de copyright, com todos os direitos reservados.