Abstract
Bike sharing systems have been widely applied to many cities and brought convenience to local citizens for short-ranged transportation. The bike shortage problem due to uneven bikes distribution is one of the biggest challenges in bike sharing systems. In this paper, we focus on station level prediction for each bike station. The proposed architecture is based on Recurrent Neural Network (RNN) and we use only one model to predict both rental and return demand for every station at once which is efficient for online balancing strategies. Without considering the global level bike distribution, the MAPE/RMSLE of the sum over the demand of each station may be too high for rebalancing strategies but the MAE/RMSE are satisficing at station level. Our evaluation shows that the proposed methods meet satisfied results at station level and global level in New York Citi Bike dataset.
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