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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., Andrade, F., Ferreira, J., Dias, J. & Bacao, F. (2021). Bike-sharing mobility patterns: a data-driven analysis for the city of Lisbon. EAI Endorsed Transactions on Smart Cities. 21 (16)
Exportar Referência (IEEE)
L. V. Albuquerque et al.,  "Bike-sharing mobility patterns: a data-driven analysis for the city of Lisbon", in EAI Endorsed Transactions on Smart Cities, vol. 21, no. 16, 2021
Exportar BibTeX
@article{albuquerque2021_1721616827866,
	author = "Albuquerque, V. and Andrade, F. and Ferreira, J. and Dias, J. and Bacao, F.",
	title = "Bike-sharing mobility patterns: a data-driven analysis for the city of Lisbon",
	journal = "EAI Endorsed Transactions on Smart Cities",
	year = "2021",
	volume = "21",
	number = "16",
	doi = "10.4108/eai.4-5-2021.169580",
	url = "https://eudl.eu/doi/10.4108/eai.4-5-2021.169580"
}
Exportar RIS
TY  - JOUR
TI  - Bike-sharing mobility patterns: a data-driven analysis for the city of Lisbon
T2  - EAI Endorsed Transactions on Smart Cities
VL  - 21
IS  - 16
AU  - Albuquerque, V.
AU  - Andrade, F.
AU  - Ferreira, J.
AU  - Dias, J.
AU  - Bacao, F.
PY  - 2021
SN  - 2518-3893
DO  - 10.4108/eai.4-5-2021.169580
UR  - https://eudl.eu/doi/10.4108/eai.4-5-2021.169580
AB  - New technologies applied to transportation services in the city, enable the shift to sustainable transportation modes making bike-sharing systems (BSS) more popular in the urban mobility scenario. This study focuses on understanding the spatiotemporal station and trip activity patterns in the Lisbon BSS, based in 2018 data taken as the baseline, and understand trip rate changes in such system, that happened in the following years of 2019 and 2020. Furthermore, our paper aims to understand the COVID-19 pandemic impact in BSS mobility patterns. In this paper, we analyzed large datasets adopting a CRISP-DM data mining method. By studying and identifying spatiotemporal distribution of trips through stations, combined with weather factors, we looked at BSS improvements more suitable to accommodate users’ demand. Our major contribution was a new insight on how people move in the city using bikes, via a data science approach using BSS network usage data. Major findings show that most bike trips occur on weekdays, with no precipitation, and we observed a substantial growth of trip count, during the observed time frame, although cut short by the pandemic. We believe that our approach can be applied to any city with available urban mobility data.
ER  -