Optimizing bike sharing system flows using graph mining, convolutional and recurrent neural networks (2020)
- Authors:
- Autor USP: KON, FABIO - IME
- Unidade: IME
- DOI: 10.1109/E-TEMS46250.2020.9111707
- Subjects: APRENDIZADO COMPUTACIONAL; APRENDIZAGEM PROFUNDA; ANÁLISE DE DADOS; ANÁLISE DE SÉRIES TEMPORAIS
- Keywords: data science; data visualization; bike-sharing systems; graph mining; time series prediction; recurrent neural networks; convolutional neural networks; shareable cities; urban informatics
- Language: Inglês
- Imprenta:
- Publisher: IEEE
- Publisher place: Piscataway
- Date published: 2020
- Source:
- Título do periódico: Proceedings
- Conference titles: IEEE European Technology and Engineering Management Summit - E-TEMS
- Este periódico é de assinatura
- Este artigo é de acesso aberto
- URL de acesso aberto
- Cor do Acesso Aberto: green
-
ABNT
LJUBENKOV, Davor e KON, Fábio e RATTI, Carlo. Optimizing bike sharing system flows using graph mining, convolutional and recurrent neural networks. 2020, Anais.. Piscataway: IEEE, 2020. Disponível em: https://doi.org/10.1109/E-TEMS46250.2020.9111707. Acesso em: 28 jul. 2024. -
APA
Ljubenkov, D., Kon, F., & Ratti, C. (2020). Optimizing bike sharing system flows using graph mining, convolutional and recurrent neural networks. In Proceedings. Piscataway: IEEE. doi:10.1109/E-TEMS46250.2020.9111707 -
NLM
Ljubenkov D, Kon F, Ratti C. Optimizing bike sharing system flows using graph mining, convolutional and recurrent neural networks [Internet]. Proceedings. 2020 ;[citado 2024 jul. 28 ] Available from: https://doi.org/10.1109/E-TEMS46250.2020.9111707 -
Vancouver
Ljubenkov D, Kon F, Ratti C. Optimizing bike sharing system flows using graph mining, convolutional and recurrent neural networks [Internet]. Proceedings. 2020 ;[citado 2024 jul. 28 ] Available from: https://doi.org/10.1109/E-TEMS46250.2020.9111707 - Design, implementation, and performance of an automatic configuration service for distributed component systems
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Informações sobre o DOI: 10.1109/E-TEMS46250.2020.9111707 (Fonte: oaDOI API)
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