Bike sharing usage prediction with deep learning: a survey

As a representative of shared mobility, bike sharing has become a green and convenient way to travel in cities in recent years. Bike usage prediction becomes more important for supporting efficient operation and management in bike share systems as the basis of inventory management and bike rebalancing. The essential of usage prediction in bike sharing systems is to model the spatial interactions of nearby stations, the temporal dependence of demands, and the impacts of environmental and societal factors. Deep learning has shown a great advantage of making a precise prediction for bike sharing usage. Recurrent neural networks capture the temporal dependence with the memory cell and gate mechanisms. Convolutional neural networks and graph neural networks learn spatial interactions of nearby stations with local convolutional operations defined for the grid-format and graph-format inputs respectively. In this survey, the latest studies about bike sharing usage prediction with deep learning are reviewed, with a classification for the prediction problems and models. Different applications based on bike usage prediction are discussed, both within and beyond bike share systems. Some research directions are pointed out to encourage future research. To the best of our knowledge, this paper is the first comprehensive survey that focuses on bike sharing usage prediction with deep learning techniques.