Jiang Weiwei
School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, 100876 China.
Neural Comput Appl. 2022;34(18):15369-15385. doi: 10.1007/s00521-022-07380-5. Epub 2022 Jun 10.
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.
作为共享出行的代表，共享单车近年来已成为城市中一种绿色便捷的出行方式。自行车使用情况预测对于支持共享单车系统的高效运营和管理变得愈发重要，它是库存管理和自行车再平衡的基础。共享单车系统中使用情况预测的关键在于对附近站点的空间交互、需求的时间依赖性以及环境和社会因素的影响进行建模。深度学习在共享单车使用情况的精确预测方面展现出巨大优势。循环神经网络通过记忆单元和门控机制捕捉时间依赖性。卷积神经网络和图神经网络分别针对网格格式和图格式输入定义的局部卷积操作来学习附近站点的空间交互。在本次综述中，回顾了关于深度学习在共享单车使用情况预测方面的最新研究，并对预测问题和模型进行了分类。讨论了基于自行车使用情况预测的不同应用，包括共享单车系统内外的应用。指出了一些研究方向以鼓励未来的研究。据我们所知，本文是第一篇专注于深度学习技术在共享单车使用情况预测方面的全面综述。
