Abstract
Bicycle sharing systems are becoming increasingly prevalent in urban environments. These systems provide an environmentally friendly transportation alternative in cities. The management of these systems faces many optimization problems. The most important of these problems are the individual maintenance of bicycles, rebalancing and shared facilities, and the use of systems by creating requirements in asymmetrical patterns. A series of data mining tasks based on real data sets is performed to solve the problem of unbalanced bicycle use.

By analyzing the characteristics of each station, the stations are modeled from the perspective of individuals and clusters by means of different models. The evaluation indicators used to address the accuracy of the results provide an effective method for shared bicycle predictions.