Public Bike Scheduling Strategy Based on Demand Prediction for Unbalanced Life-Value Distribution

Public bikes have emerged as a significant mode of transportation for commuters. However, public bikes can be damaged to varying degrees as use frequency increases. The stations occupied by damaged bikes are of low usability, which may result in fewer users and a waste of resources. In this paper, we investigate a global scheduling solution for a bike system with unbalanced life-value distribution and design a scheduling approach. Firstly, we employ the Weibull model to estimate bike life to quantify use load. Moreover, we design a clustering algorithm to partition station regions. We also design a demand prediction model named ST-SAGCN to capture dynamic spatial correlations and spatio-temporal correlations simultaneously. We also propose a scheduling method that meets station demand as much as possible to balance station use and bike-life distribution. We conduct experiments on two public bike datasets covering different time spans in New York and Washington, and compare the bike-life distribution after scheduling with that of real-world actual conditions. The experimental results attest to the effectiveness of our approach to balancing bike use-load. The code is publicly available at https://github.com/Zayn-Tang/BikeSchedulingStrategy.