A simulation framework for optimizing bike rebalancing and maintenance in large-scale bike-sharing systems

Bike-sharing systems (BSSs) have rapidly attracted worldwide interest for their success in improving quality-of-life in metropolitan areas. One kind of BSS requires docking stations to avoid misplacements of bikes. Such a BSS is faced with challenges caused by imbalanced demands across stations and frequent failures of bikes and docks. To achieve a high level of customer satisfaction, timely bike rebalancing and system maintenance must be performed. In this work, a simulation framework is proposed for evaluating different rebalancing and maintenance strategies. The framework can be integrated with any multi-vehicle static or dynamic rebalancing optimization model. An optimization model solved by an enhanced k means clustering method (EKM) and an Ant Colony Optimization (ACO) algorithm is provided as an example for demonstrating such integration. A case study based on the configuration and historical data of Citi Bike in New York City is conducted for simulation model validation and for illustrating the managerial impacts of different rebalancing and maintenance strategies on the investment, operation, and service levels of such a large-scale BSS. The application of the proposed simulation framework is not limited to BSSs but also can be extended to other types of shared transport systems with non-floating stations where rebalancing and maintenance optimization are critical for efficient and healthy operation.

Automated summary

Bike-sharing systems have gained worldwide attention for their ability to enhance quality-of-life in cities. This study proposes a simulation framework to evaluate different strategies for bike rebalancing and maintenance. An optimization model is provided as an example, using enhanced k means clustering and an Ant Colony Optimization algorithm. The framework's application extends beyond bike-sharing systems to other shared transportation systems where rebalancing and maintenance optimization are essential.