BikeCAP: Deep Spatial-temporal Capsule Network for Multi-step Bike Demand Prediction
BikeCAP: Deep Spatial-temporal Capsule Network for Multi-step Bike Demand Prediction
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DOI：
10.1109/icdcs54860.2022.00085
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发表时间：
2022-07
期刊：
影响因子：
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通讯作者：
Shuxin Zhong;Wenjun Lyu;Desheng Zhang;Yu Yang
中科院分区：
文献类型：
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作者：
Shuxin Zhong;Wenjun Lyu;Desheng Zhang;Yu Yang
鉴于最近全球自行车共享系统的发展，已经提出了许多方法来预测其用户需求。这些方法对于单步预测（即，10分钟）但限于在多步预测中进行预测（即，超过60分钟），这对于需要长时间操作的自行车重新平衡等应用至关重要。为了解决这一限制，我们利用了上游运输需求的事实，例如，地铁，可以帮助下游运输的未来需求预测，例如，自行车具体来说，我们设计了一个名为BikeCAP的深度时空胶囊网络，它有三个组件：（1）一个历史胶囊，它学习上游（即，地铁）和下游（即，自行车）运输系统，其中金字塔卷积层探索同时的时空相关性;（2）主动捕获从上游到下游系统的动态时空传播相关性的未来胶囊，其中时空路由技术有利于减少累积的预测误差;（3）3D去卷积解码器，其考虑相邻网格和相邻时隙中的类似下游需求模式来构建未来自行车需求。在实验方面，我们对深圳市收集的30，000辆自行车和7条地铁线路的数据进行了综合实验，结果表明BikeCAP优于几种最先进的方法，在多步预测准确率方面，性能显著提高了38.6%。我们还进行消融研究，以显示BikeCAP不同设计组件的重要性。
Given the recent global development of bike-sharing systems, numerous methods have been proposed to predict their user demand. These methods work fine for single-step prediction (i.e., 10 mins) but are limited to predicting in a multi-step prediction (i.e., more than 60 mins), which is essential for applications such as bike re-balancing that requires long operation time. To address this limitation, we leverage the fact that the demand for upstream transportation, e.g., subways, can assist the future demand prediction of downstream transportation, e.g., bikes. Specifically, we design a deep spatial-temporal capsule network called BikeCAP with three components: (1) a historical capsule that learns the demand characteristics for both the upstream (i.e., subways) and downstream (i.e., bikes) transportation systems, where a pyramid convolutional layer explores the simultaneous spatial-temporal correlations; (2) a future capsule that actively captures the dynamic spatial-temporal propagation correlations from the upstream to the downstream system, in which a spatial-temporal routing technique benefits to reduce the accumulated prediction errors; (3) a 3D-deconvolution decoder that constructs future bike demand considering the similar downstream demand patterns in neighboring grids and adjacent time slots. Experimentally, we conduct comprehensive experiments on the data of 30, 000 bikes and 7 subway lines collected in Shenzhen City, China, The results show that BikeCAP outperforms several state-of-the-art methods, significantly increasing the performance by 38.6% in terms of accuracy in multi-step prediction. We also conduct ablation studies to show the significance of BikeCAP’s different designed components.