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

ICN: Interactive Convolutional Network for Forecasting Travel Demand of Shared Micromobility