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A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA) 
Albuquerque, V., Oliveira, A., Barbosa, J. L., Rodrigues, R. S., Andrade, F., Dias, J....Ferreira, J. (2021). Smart cities: Data-driven solutions to understand disruptive problems in transportation—The Lisbon case study. Energies. 14 (11)
Exportar Referência (IEEE) 
L. V. Albuquerque et al.,  "Smart cities: Data-driven solutions to understand disruptive problems in transportation—The Lisbon case study", in Energies, vol. 14, no. 11, 2021
Exportar BibTeX 
@article{albuquerque2021_1734916791790,
	author = "Albuquerque, V. and Oliveira, A. and Barbosa, J. L. and Rodrigues, R. S. and Andrade, F. and Dias, J. and Ferreira, J.",
	title = "Smart cities: Data-driven solutions to understand disruptive problems in transportation—The Lisbon case study",
	journal = "Energies",
	year = "2021",
	volume = "14",
	number = "11",
	doi = "10.3390/en14113044",
	url = "https://www.mdpi.com/journal/energies"
}
Exportar RIS 
TY  - JOUR
TI  - Smart cities: Data-driven solutions to understand disruptive problems in transportation—The Lisbon case study
T2  - Energies
VL  - 14
IS  - 11
AU  - Albuquerque, V.
AU  - Oliveira, A.
AU  - Barbosa, J. L.
AU  - Rodrigues, R. S.
AU  - Andrade, F.
AU  - Dias, J.
AU  - Ferreira, J.
PY  - 2021
SN  - 1996-1073
DO  - 10.3390/en14113044
UR  - https://www.mdpi.com/journal/energies
AB  - Transportation data in a smart city environment is increasingly becoming available. This data availability allows building smart solutions that are viewed as meaningful by both city residents and city management authorities. Our research work was based on Lisbon mobility data available through the local municipality, where we integrated and cleaned different data sources and applied a CRISP-DM approach using Python. We focused on mobility problems and interdependence and cascading-effect solutions for the city of Lisbon. We developed data-driven approaches using artificial intelligence and visualization methods to understand traffic and accident problems, providing a big picture to competent authorities and supporting the city in being more prepared, adaptable, and responsive, and better able to recover from such events.
ER  -