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Intelligent Optimization of Bike-Sharing Systems: Predictive Models and Algorithms for Equitable Bicycle Distribution in Barcelona

Author

Listed:
  • Gerard Giner Fabregat

    (Statistics and Operations Research Department, Universitat Politècnica de Catalunya—BarcelonaTech, 08034 Barcelona, Spain
    NTT DATA, 08005 Barcelona, Spain)

  • Pau Fonseca i Casas

    (Statistics and Operations Research Department, Universitat Politècnica de Catalunya—BarcelonaTech, 08034 Barcelona, Spain)

  • Antonio Rivero Martínez

    (NTT DATA, 41092 Sevilla, Spain)

Abstract

This paper aims to propose innovative solutions to improve the management of Barcelona’s bike-sharing system, known as Bicing. This study addresses one of the system’s main challenges: the unequal distribution of bicycles across the city and at different times of the day, which affects the users. The analysis combines advanced statistical techniques, predictive models and optimization algorithms to identify vulnerable areas in terms of accessibility and design strategies to balance bicycle distribution. Using methods such as clustering and predictive models based on machine learning, the system’s usage patterns are anticipated. These predictions feed optimization algorithms that enable the planning of more efficient routes for bicycle repositioning, reducing unnecessary vehicle movement and supporting a more environmentally friendly mobility network. The results highlight the importance of proactive system management, improving both user satisfaction and operational efficiency while fostering a more sustainable urban transport ecosystem.

Suggested Citation

  • Gerard Giner Fabregat & Pau Fonseca i Casas & Antonio Rivero Martínez, 2025. "Intelligent Optimization of Bike-Sharing Systems: Predictive Models and Algorithms for Equitable Bicycle Distribution in Barcelona," Sustainability, MDPI, vol. 17(10), pages 1-33, May.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:10:p:4316-:d:1652606
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    References listed on IDEAS

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