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Optimizing urban bike-sharing systems: a stochastic mathematical model for infrastructure planning.
Seyedeh Asra Ahmadi1, Peiman Ghasemi2, Jan Fabian Ehmke3
1Department of Logistics, Tourism and Service Management, German University of Technology in Oman, Muscat, Oman.
Central European Journal of Operations Research
|January 12, 2026
Summary
This study optimizes bike-sharing systems using a stochastic model for dynamic demand, improving resource allocation and infrastructure planning. Effective cost management is key to meeting user travel needs efficiently.
Area of Science:
- Operations Research
- Transportation Science
- Urban Planning
Background:
- Bike-sharing systems face challenges with dynamic demand, exacerbated by events like the COVID-19 pandemic.
- Optimizing resource allocation and infrastructure is crucial for efficient bike-sharing operations.
Purpose of the Study:
- To develop a stochastic mathematical model for optimizing bike-sharing system resource allocation and infrastructure planning.
- To enhance system performance and resource utilization under varying demand scenarios.
- To ensure total travel demand fulfillment and assess network capacity.
Main Methods:
- Development of a stochastic mathematical model for bike-sharing station allocation and network design.
- Application of the model to the Vienna bike-sharing system for a case study.
- Conducting sensitivity analysis on cost parameters.
Main Results:
- The stochastic model effectively optimizes resource allocation and infrastructure planning for bike-sharing systems.
- Case study in Vienna demonstrates practical applicability and potential for improved efficiency and service quality.
- Sensitivity analysis indicates that increased costs for docks and stations negatively impact fulfilled demand.
Conclusions:
- The proposed stochastic model provides a robust framework for bike-sharing system optimization.
- Cost-effective infrastructure planning is essential for maximizing service provision and user satisfaction.
- The model offers valuable insights for enhancing the resilience and efficiency of urban mobility solutions.
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