ICN: Interactive Convolutional Network for Forecasting Travel Demand of Shared Micromobility
This work addresses accurate demand prediction for shared micromobility operators and city planners, but it is incremental as it builds on existing deep learning approaches with specific enhancements.
The paper tackles the problem of forecasting spatiotemporal travel demand for shared micromobility by proposing the Interactive Convolutional Network (ICN), which significantly outperforms benchmark models in real-world case studies in Chicago and Austin.
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.