Díaz, J. J. V., Pozo, R. F., González, A. B. R., Wilby, M. R., & Ávila, C. S. (2020). Hierarchical Agglomerative Clustering of Bicycle Sharing Stations Based on UltraLight Edge Computing. Sensors, 20(12), 3550. Doi: 10.3390/s20123550. JIF (2019): 3,275; Instruments & Instrumentation (SSCI): Q1.

TÍTULO: Hierarchical Agglomerative Clustering of Bicycle Sharing Stations Based on UltraLight Edge Computing
AUTORES: Juan José Vinagre Díaz, Rubén Fernández Pozo, Ana Belén Rodríguez González, Mark R. Wilby y Carmen Sánchez Ávila
REVISTA: Sensors, 20(12), 3550
AÑO: 2020

URL: https://doi.org/10.3390/s20123550

Bicycle sharing systems (BSSs) have established a new shared-economy mobility model. After a rapid growth they are evolving into a fully-functional mobile sensor platform for cities. The viability of BSSs is floored by their operational costs, mainly due to rebalancing operations. Rebalancing implies transporting bicycles to and from docking stations in order to guarantee the service. Rebalancing performs clustering to group docking stations by behaviour and proximity. In this paper we propose a Hierarchical Agglomerative Clustering based on an Ultra-Light Edge Computing Algorithm (HAC-ULECA). We eliminate the proximity and let Hierarchical Agglomerative Clustering (HAC) focus on behaviour. Behaviour is represented by ULECA as an activity profile based on the net flow of arrivals and departures in a docking station. This drastically reduces the computing requirements which allows ULECA to run as an edge computing functionality embedded into the physical layer of the Internet of Shared Bikes (IoSB) architecture. We have applied HAC-ULECA to real data from BiciMAD, the public BSS in Madrid (Spain). Our results, presented as dendograms, graphs, geographical maps, and colour maps, show that HAC-ULECA is capable of separating behaviour profiles related to business and residential areas and extracting meaningful spatio-temporal information about the BSS and the city’s mobility.