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

Author

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  • 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

    as
    1. Rayane El Sibai & Khalil Challita & Jacques Bou Abdo & Jacques Demerjian, 2021. "A New User-Based Incentive Strategy for Improving Bike Sharing Systems’ Performance," Sustainability, MDPI, vol. 13(5), pages 1-18, March.
    2. Linwei Hu & Jie Chen & Joel Vaughan & Soroush Aramideh & Hanyu Yang & Kelly Wang & Agus Sudjianto & Vijayan N. Nair, 2021. "Supervised Machine Learning Techniques: An Overview with Applications to Banking," International Statistical Review, International Statistical Institute, vol. 89(3), pages 573-604, December.
    3. Zhang, Jie & Meng, Meng & Wong, Yiik Diew & Ieromonachou, Petros & Wang, David Z.W., 2021. "A data-driven dynamic repositioning model in bicycle-sharing systems," International Journal of Production Economics, Elsevier, vol. 231(C).
    4. Julio Mario Daza-Escorcia & David Álvarez-Martínez, 2024. "A Matheuristic Approach Based on Variable Neighborhood Search for the Static Repositioning Problem in Station-Based Bike-Sharing Systems," Mathematics, MDPI, vol. 12(22), pages 1-30, November.
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