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, trans-ported 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 author's knowledge, this is the first study of this kind carried in Portugal, bringing new insights in the field of last-mile logistics.

This work is partially funded by national funds through FCT - Fundação para a Ciência e Tecnologia, I.P., under the project FCT UIDB/04466/2020.

E-cargo bikes,K-Means,Last-mile logistics,Micro-hub

• Computer and Information Sciences - Natural Sciences
• Electrical Engineering, Electronic Engineering, Information Engineering - Engineering and Technology