Accurate shared micromobility demand predictions are essential for transportation planning and management. Although deep learning models provide powerful tools to deal with demand prediction problems, studies on forecasting highly-accurate spatiotemporal shared micromobility demand are still lacking. This paper proposes a deep learning model named Interactive Convolutional Network (ICN) to forecast spatiotemporal travel demand for shared micromobility. The proposed model develops a novel channel dilation method by utilizing multi-dimensional spatial information (i.e., demographics, functionality, and transportation supply) based on travel behavior knowledge for building the deep learning model. We use the convolution operation to process the dilated tensor to simultaneously capture temporal and spatial dependencies. Based on a binary-tree-structured architecture and interactive convolution, the ICN model extracts features at different temporal resolutions, and then generates predictions using a fully-connected layer. The proposed model is evaluated for two real-world case studies in Chicago, IL, and Austin, TX. The results show that the ICN model significantly outperforms all the selected benchmark models. The model predictions can help the micromobility operators develop optimal vehicle rebalancing schemes and guide cities to better manage the shared micromobility system.

翻译：精确的共享微出行需求预测对于交通规划与管理至关重要。尽管深度学习模型为处理需求预测问题提供了强大工具，但在高精度时空共享微出行需求预测方面的研究仍较为匮乏。本文提出了一种名为交互式卷积网络（ICN）的深度学习模型，用于预测共享微出行的时空出行需求。该模型基于出行行为知识，利用多维空间信息（如人口统计、功能属性及交通供给）开发了一种新颖的通道膨胀方法，以构建深度学习模型。我们采用卷积运算处理膨胀张量，同步捕获时间与空间依赖关系。基于二叉树结构架构与交互式卷积，ICN模型在不同时间分辨率下提取特征，并通过全连接层生成预测结果。在伊利诺伊州芝加哥市和德克萨斯州奥斯汀市的两个实际案例研究中，结果表明ICN模型显著优于所有选定的基准模型。该模型预测结果可帮助微出行运营商制定最优车辆再平衡方案，并指导城市更有效地管理共享微出行系统。