Optimizing urban bike-sharing systems: a stochastic mathematical model for infrastructure planning

This paper addresses the optimization of resource allocation and infrastructure planning in bike-sharing systems, particularly inspired by dynamic demand patterns as observed during the COVID-19 pandemic. We introduce a stochastic mathematical model that considers varying demand scenarios to enhance system performance and resource utilization. The research objectives are to fulfill the total travel demand across scenarios and compute the network's capacity to satisfy demand, thereby enhancing the system's efficiency and meeting users' diverse travel needs. The main contributions of this paper include presenting a stochastic mathematical model for bike-sharing station allocation and path network design, which optimizes resource allocation and infrastructure planning. Through a case study on the Vienna bike-sharing system, the model demonstrates practical applicability and effectiveness, offering insights for improving efficiency and service quality. The sensitivity analysis reveals that as costs for bicycle docks and station building increase, fulfilled demand decreases, emphasizing the crucial role of cost management in meeting demand efficiently.