TL-FCM: A hierarchical prediction model based on two-level fuzzy c-means clustering for bike-sharing system

In recent years, shared bikes have gradually emerged into public life as a new way to travel and helped solve the last-mile problem of residents' travel. While this development has brought convenient travel to users, a series of problems exist, a prominent one is the uneven distribution of bikes at each shared bike station. Accurately predicting bike usage in a bike-sharing system can help solve this problem. In this paper, we investigate how to improve the accuracy of predicting the usage of bikes in bike-sharing system. First, considering both geographic location information of shared bike stations and the migration trend of bikes between stations, we design a two-level fuzzy c-means clustering algorithm to cluster shared bicycle stations into groups, which can better capture the connection between shared bicycle stations and improve the clustering accuracy of shared bicycle sites, then, we combine the two-level fuzzy c-means clustering algorithm with the multi-similarity reference model to predict the usage of bikes, which can significantly improve the accuracy of the forecast. To evaluate the performance of our model, we validate our model in the New York Bike-Sharing System. The results shows that our model obtained significantly better results than other models.