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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)
Gil, B., Albuquerque, V., Dias, J., Abranches, R. & Ogando, M. (2022). Data driven spatiotemporal analysis of e-cargo bike network in Lisbon and its expansion: the Yoob case study. EAI INTSYS 2022.
Exportar Referência (IEEE)
B. Gil et al.,  "Data driven spatiotemporal analysis of e-cargo bike network in Lisbon and its expansion: the Yoob case study", in EAI INTSYS 2022, Lisboa, 2022
Exportar BibTeX
@misc{gil2022_1777454738424,
	author = "Gil, B. and Albuquerque, V. and Dias, J. and Abranches, R. and Ogando, M.",
	title = "Data driven spatiotemporal analysis of e-cargo bike network in Lisbon and its expansion: the Yoob case study",
	year = "2022",
	howpublished = "Outro",
	url = "https://futuretransport.eai-conferences.org/2022/"
}
Exportar RIS
TY  - CPAPER
TI  - Data driven spatiotemporal analysis of e-cargo bike network in Lisbon and its expansion: the Yoob case study
T2  - EAI INTSYS 2022
AU  - Gil, B.
AU  - Albuquerque, V.
AU  - Dias, J.
AU  - Abranches, R.
AU  - Ogando, M.
PY  - 2022
CY  - Lisboa
UR  - https://futuretransport.eai-conferences.org/2022/
AB  - The adoption of more environmentally friendly and sustainable fleets for last-mile parcel delivery within large urban centers, such as e-cargo bikes, has gained the interest of the community. The logistics infrastructure network, had to adapt to the requirements of this new type of fleet, and micro-hubs and nano-hubs emerged. In this paper we tackle spatiotemporal characterization of e-cargo bike fleet behavior, by conducting a data centered case study where we explore data from Yoob, a last mile delivery e-cargo bike logistics startup that operates in the Lisbon area and outskirts. We also address the identification of potential expansion locations to the establishment of new hubs. Our data was collected during a 4-month period (January to April 2022). By adopting state-of-the-art data science and machine learning techniques, and following the CRIPS-DM data mining method, our innovative approach discovered five clusters that are able to characterize the Yoob fleet, with variations in distances traveled, times, transported volumes and speeds. In the perspective of expanding Yoob's e-cargo bike network, three new locations in Lisbon were signaled for potential new hub installation. To the authors knowledge this is the first study of this kind carried in Portugal, bringing new insights in the field of last-mile logistics.
ER  -