Station Correlation Attention Learning for Data-driven Bike Sharing System Usage Prediction

After years of development, bike sharing has been one of the major choices of transportation for urban residents worldwide. However, efficient use of bike sharing resources is challenging due to the unbalanced station-level demands and supplies, which causes the maintenance of the bike sharing systems painstaking. To achieve system efficiency, efforts have been made on accurate prediction of bike traffic (demands/pick-ups and returns/drop-offs). Nonetheless, bike station traffic prediction is difficult due to the spatio-temporal complexity of bike sharing systems. Moreover, such level of prediction over the entire bike sharing systems is also challenging due to the large number of bike stations. To fill this gap, we propose EikeGAAN, a graph adjaceny attention neural network to predict station-level bike traffic for entire bike sharing systems. The proposed prediction system consists of a graph convolutional network with an attention mechanism differentiating the spatial correlations between features of bike stations in the system and a long short-term memory network capturing temporal correlations. We have conducted extensive data analysis upon bike usage, weather, points of interest and event data, and derived the graph representation of the bike sharing networks. Through experimental study on over 27 millions trips of bike sharing systems of four metropolitan cities in the U.S., New York City, Chicago, Washington D.C. and Los Angeles, our network design has shown high accuracy in predicting the bike station traffic in the cities, outperforming other baselines and state-of-art models.