RT Journal Article
T1 Pearclustering: a novel clustering algorithm with an application to bike mobility
A1 Márquez Saldaña, Francisco
A1 Aranda Corral, Gonzalo Antonio
A1 Borrego Díaz, Joaquín
AB Bike Sharing Systems (BSS) have become a key solution for urban mobility, reducing traffic-related CO2 emissions. However, managing BSS poses challenges that require data-driven solutions, particularly for understanding their global behavior and forecasting their evolution. These dynamics arise from the interaction among users, companies, dock stations, and city policies, influenced by sociological and infrastructure-based factors. This paper proposes a novel clustering methodology to analyze BSS data across multiple cities. By clustering station-day tuples instead of aggregating statistics, our approach captures seasonal patterns, special events, and weekday/weekend differences. Using Pearson Correlation as a distance metric, it remains robust across different station sizes and system scales. Trained on three European BSS and evaluated across six cities from 4 different countries, our model uncovers meaningful patterns such as work, residential, and leisure areas, as well as seasonal changes even in systems not used in the training process. These insights enhance BSS management, expansion, and decision-making, with applications in monitoring, anomaly detection, and demand prediction.
PB Springer
SN 1864-5909
SN 1864-5917 (electrónico)
YR 2025
FD 2025
LK https://hdl.handle.net/10272/27408
UL https://hdl.handle.net/10272/27408
LA eng
NO Marquez-Saldaña, F., Aranda-Corral, G. A., & Borrego-Díaz, J. (2025). Pearclustering: a novel clustering algorithm with an application to bike mobility. Evolutionary Intelligence, 18(4). https://doi.org/10.1007/s12065-025-01062-6
NO Funding for open access publishing: Universidad de Sevilla / CBUA. Francisco Márquez-Saldaña, Gonzalo A. Aranda-Corral and Joaquín Borrego-Díaz, received Grant PID2023-147198NB-I00 funded by MICI-U/AEI/10.13039/501100011033 (Agencia Estatal de Investigación) and by FEDER, UE.
DS Repositorio Institucional de la Universidad de Huelva
RD 1 jun 2026