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Article

Hierarchical Agglomerative Clustering of Bicycle Sharing Stations Based on Ultra-Light Edge Computing

Group Biometry, Biosignals, Security, and Smart Mobility, Departamento de Matemática Aplicada a las Tecnologías de la Información y las Comunicaciones, Escuela Técnica Superior de Ingenieros de Telecomunicación, Universidad Politécnica de Madrid, Avenida Complutense 30, 28040 Madrid, Spain
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Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Sensors 2020, 20(12), 3550; https://doi.org/10.3390/s20123550
Received: 1 June 2020 / Revised: 17 June 2020 / Accepted: 19 June 2020 / Published: 23 June 2020
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. View Full-Text
Keywords: bicycle sharing systems; hierarchical clustering; edge computing; docking stations; spatio-temporal profiling; Internet of shared bicycles bicycle sharing systems; hierarchical clustering; edge computing; docking stations; spatio-temporal profiling; Internet of shared bicycles
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MDPI and ACS Style

Vinagre Díaz, J.J.; Fernández Pozo, R.; Rodríguez González, A.B.; Wilby, M.R.; Sánchez Ávila, C. Hierarchical Agglomerative Clustering of Bicycle Sharing Stations Based on Ultra-Light Edge Computing. Sensors 2020, 20, 3550. https://doi.org/10.3390/s20123550

AMA Style

Vinagre Díaz JJ, Fernández Pozo R, Rodríguez González AB, Wilby MR, Sánchez Ávila C. Hierarchical Agglomerative Clustering of Bicycle Sharing Stations Based on Ultra-Light Edge Computing. Sensors. 2020; 20(12):3550. https://doi.org/10.3390/s20123550

Chicago/Turabian Style

Vinagre Díaz, Juan J., Rubén Fernández Pozo, Ana B. Rodríguez González, Mark R. Wilby, and Carmen Sánchez Ávila. 2020. "Hierarchical Agglomerative Clustering of Bicycle Sharing Stations Based on Ultra-Light Edge Computing" Sensors 20, no. 12: 3550. https://doi.org/10.3390/s20123550

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