Research Article

Improving Service Quality in Station-Based Bike-Sharing Systems: A Data-Driven Evaluation of a Crowdsourced Rebalancing Strategy

Volume: 10 Number: 1 April 11, 2026

Improving Service Quality in Station-Based Bike-Sharing Systems: A Data-Driven Evaluation of a Crowdsourced Rebalancing Strategy

Abstract

Bike-sharing systems (BSS) are a key component of sustainable urban mobility, but their effectiveness is often hindered by the operational challenge of fleet rebalancing, which leads to service failures and high costs. This study proposes and evaluates a dynamic, rule-based user-incentive policy designed to mitigate system imbalances. We developed a data-driven discrete-event simulation model, calibrated with over one million real-world trips from the Oslo city bike system, to serve as a virtual laboratory for testing the policy's performance. The simulation results, averaged over multiple replications, demonstrate the policy's significant effectiveness. Compared to a baseline scenario with a dock unavailability rate of 6.27%, the incentive policy with a 75% user acceptance rate reduced this failure rate to a negligible 0.25%, representing a 96% improvement in service quality for returning users. This work validates that crowdsourcing rebalancing efforts through user incentives, evaluated via high-fidelity simulation, is a powerful and practical approach for enhancing the operational efficiency and reliability of station-based BSS.

Keywords

Ethical Statement

Ethics committee approval was not required for this study as it was based on the analysis of publicly available, anonymized data.

Thanks

The authors wish to express their gratitude to Oslo Bysykkel for making their high-quality, historical trip data publicly available. This open data was indispensable for the development and validation of the simulation model presented in this study.

References

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  2. Chiariotti, F., Pielli, C., Zanella, A., & Zorzi, M. (2018). A dynamic approach to rebalancing bike-sharing systems. Sensors, 18(2), 512.
  3. Dell'Amico, M., Hadjicostantinou, E., Iori, M., & Novellani, S. (2014). The bike sharing rebalancing problem: Mathematical formulations and benchmark instances. Omega, 45, 7-19.
  4. Dell’Amico, M., Iori, M., Novellani, S., & Subramanian, A. (2018). The bike sharing rebalancing problem with stochastic demands. Transportation research part B: methodological, 118, 362-380.
  5. Fan, Y., Wang, G., Lu, X., & Wang, G. (2019). Distributed forecasting and ant colony optimization for the bike-sharing rebalancing problem with unserved demands. PloS one, 14(12), e0226204.
  6. Fukushige, T. (2022). Incentive-based Approach to Rebalancing a Dock-less E-Bike-Share System for Sustainability (Doctoral dissertation, University of California, Davis).
  7. Jian, N., Freund, D., Wiberg, H. M., & Henderson, S. G. (2016, December). Simulation optimization for a large-scale bike-sharing system. In 2016 winter simulation conference (WSC) (pp. 602-613). IEEE.
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Details

Primary Language

English

Subjects

Modelling and Simulation , Industrial Engineering

Journal Section

Research Article

Publication Date

April 11, 2026

Submission Date

October 1, 2025

Acceptance Date

November 3, 2025

Published in Issue

Year 2026 Volume: 10 Number: 1

APA
Parlak, İ. E. (2026). Improving Service Quality in Station-Based Bike-Sharing Systems: A Data-Driven Evaluation of a Crowdsourced Rebalancing Strategy. Journal of Innovative Science and Engineering, 10(1), 86-95. https://doi.org/10.38088/jise.1794672
AMA
1.Parlak İE. Improving Service Quality in Station-Based Bike-Sharing Systems: A Data-Driven Evaluation of a Crowdsourced Rebalancing Strategy. JISE. 2026;10(1):86-95. doi:10.38088/jise.1794672
Chicago
Parlak, İsmail Enes. 2026. “Improving Service Quality in Station-Based Bike-Sharing Systems: A Data-Driven Evaluation of a Crowdsourced Rebalancing Strategy”. Journal of Innovative Science and Engineering 10 (1): 86-95. https://doi.org/10.38088/jise.1794672.
EndNote
Parlak İE (April 1, 2026) Improving Service Quality in Station-Based Bike-Sharing Systems: A Data-Driven Evaluation of a Crowdsourced Rebalancing Strategy. Journal of Innovative Science and Engineering 10 1 86–95.
IEEE
[1]İ. E. Parlak, “Improving Service Quality in Station-Based Bike-Sharing Systems: A Data-Driven Evaluation of a Crowdsourced Rebalancing Strategy”, JISE, vol. 10, no. 1, pp. 86–95, Apr. 2026, doi: 10.38088/jise.1794672.
ISNAD
Parlak, İsmail Enes. “Improving Service Quality in Station-Based Bike-Sharing Systems: A Data-Driven Evaluation of a Crowdsourced Rebalancing Strategy”. Journal of Innovative Science and Engineering 10/1 (April 1, 2026): 86-95. https://doi.org/10.38088/jise.1794672.
JAMA
1.Parlak İE. Improving Service Quality in Station-Based Bike-Sharing Systems: A Data-Driven Evaluation of a Crowdsourced Rebalancing Strategy. JISE. 2026;10:86–95.
MLA
Parlak, İsmail Enes. “Improving Service Quality in Station-Based Bike-Sharing Systems: A Data-Driven Evaluation of a Crowdsourced Rebalancing Strategy”. Journal of Innovative Science and Engineering, vol. 10, no. 1, Apr. 2026, pp. 86-95, doi:10.38088/jise.1794672.
Vancouver
1.İsmail Enes Parlak. Improving Service Quality in Station-Based Bike-Sharing Systems: A Data-Driven Evaluation of a Crowdsourced Rebalancing Strategy. JISE. 2026 Apr. 1;10(1):86-95. doi:10.38088/jise.1794672


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The works published in Journal of Innovative Science and Engineering (JISE) are licensed under a  Creative Commons Attribution-NonCommercial 4.0 International License.