Bike-sharing system is a new transportation that has emerged in recent years. More and more
people will choose to ride bicycle sharing at home and abroad. While we use shared bicycles conveniently,
there are also unfavorable factors that affect the
customer's riding experience in the bicycle-sharing system.
Due to the rents or returns of bikes at different stations in different periods are imbalanced, the bikes in
the system need to be rebalanced frequently. Therefore, there is an urgent need to predict and reallocate
the bikes in advance. In this paper, we propose a hierarchical forecasting model that predicts the number
of rents or returns to each station cluster in a future period to achieve redistribution. First, we propose
a two-level af nity propagation clustering algorithm to divide bike stations into groups where migration
trends of bikes among stations as well as geographical locations information are considered. Based on the
two-level hierarchy of stations, the total rents of bikes are predicted. Then, we use a multi-similarity-based
inference model to forecast the migration proportion of inter-cluster and across cluster, based on which the
rents or returns of bikes at each station can be deduced. In order to verify the effectiveness of our two-level
hierarchical prediction model, we validate it on the bike-sharing system of New York City and compare the
results with those of other popular methods obtained. Experimental results demonstrate the superiority over
other methods.
