mlsanityData to 14 Oct 2026

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

arXiv 2306.13897 ↗First posted 24 Jun 2023cs.CY cs.AIcited by 2

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 25 of its 83 references that are in the map. First posted on arXiv 24 Jun 2023.

Builds on

25 of 83 references.

References · 25

  1. 2017Attention Is All You Needcited by 60,361
  2. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  3. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  4. 2016Densely Connected Convolutional Networkscited by 6,720
  5. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,057
  6. 2016WaveNet: A Generative Model for Raw Audiocited by 1,722
  7. 2014On the Properties of Neural Machine Translation: Encoder-Decoder Approachescited by 1,304
  8. 2017Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastingcited by 1,136
  9. 2015Multi-Scale Context Aggregation by Dilated Convolutionscited by 1,133
  10. 2019Graph WaveNet for Deep Spatial-Temporal Graph Modelingcited by 829
  11. 2020Adaptive Graph Convolutional Recurrent Network for Traffic Forecastingcited by 530
  12. 2016Temporal Convolutional Networks for Action Segmentation and Detectioncited by 508
13 more
  1. 2018T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Predictioncited by 401
  2. 2021SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interactioncited by 338
  3. 2016Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Predictioncited by 333
  4. 2020Spatial-Temporal Transformer Networks for Traffic Flow Forecastingcited by 180
  5. 2018Deep Multi-View Spatial-Temporal Network for Taxi Demand Predictioncited by 162
  6. 2023PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow Predictioncited by 137
  7. 2017Predicting Station-level Hourly Demands in a Large-scale Bike-sharing Network: A Graph Convolutional Neural Network Approachcited by 35
  8. 2019Contextualized Spatial-Temporal Network for Taxi Origin-Destination Demand Predictioncited by 35
  9. 2019Predicting origin-destination ride-sourcing demand with a spatio-temporal encoder-decoder residual multi-graph convolutional networkcited by 23
  10. 2022Improving short-term bike sharing demand forecast through an irregular convolutional neural networkcited by 9
  11. 2023Deep trip generation with graph neural networks for bike sharing system expansioncited by 8
  12. 2023Travel Demand Forecasting: A Fair AI Approachcited by 7
  13. 2021Real-Time Forecasting of Dockless Scooter-Sharing Demand: A Spatio-Temporal Multi-Graph Transformer Approachcited by 6

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025A Comprehensive Machine Learning Framework for Micromobility Demand Predictioncited by 0
  2. 2026A Grid-Based Framework for E-Scooter Demand Representation and Temporal Input Design for Deep Learning: Evidence from Austin, Texascited by 0