mlsanityData to 7 Oct 2026

Event-Aware Multimodal Mobility Nowcasting

arXiv 2112.08443 ↗First posted 14 Dec 2021cs.LG cs.AIcited by 18

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Abstract (arXiv)

As a decisive part in the success of Mobility-as-a-Service (MaaS), spatio-temporal predictive modeling for crowd movements is a challenging task particularly considering scenarios where societal events drive mobility behavior deviated from the normality. While tremendous progress has been made to model high-level spatio-temporal regularities with deep learning, most, if not all of the existing methods are neither aware of the dynamic interactions among multiple transport modes nor adaptive to unprecedented volatility brought by potential societal events. In this paper, we are therefore motivated to improve the canonical spatio-temporal network (ST-Net) from two perspectives: (1) design a heterogeneous mobility information network (HMIN) to explicitly represent intermodality in multimodal mobility; (2) propose a memory-augmented dynamic filter generator (MDFG) to generate sequence-specific parameters in an on-the-fly fashion for various scenarios. The enhanced event-aware spatio-temporal network, namely EAST-Net, is evaluated on several real-world datasets with a wide variety and coverage of societal events. Both quantitative and qualitative experimental results verify the superiority of our approach compared with the state-of-the-art baselines. Code and data are published on https://github.com/underdoc-wang/EAST-Net.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). Builds on 19 of its 39 references that are in the map. First posted on arXiv 14 Dec 2021.

Builds on

19 of 39 references.

References · 19

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7 more
  1. 2017Meta Networkscited by 268
  2. 2019Continual learning with hypernetworkscited by 267
  3. 2018Deep Multi-View Spatial-Temporal Network for Taxi Demand Predictioncited by 162
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 5; 2024: 4; 2025: 4; 2026: 3.

18 papers.

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  1. 2023Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Surveycited by 140
  2. 2022Spatio-Temporal Meta-Graph Learning for Traffic Forecastingcited by 88
  3. 2025Foundation Models for Spatio-Temporal Data Science: A Tutorial and Surveycited by 23
  4. 2023STAEformer: Spatio-Temporal Adaptive Embedding Makes Vanilla Transformer SOTA for Traffic Forecastingcited by 12
  5. 2023Spatial Heterophily Aware Graph Neural Networkscited by 7
  6. 2023Learning Gaussian Mixture Representations for Tensor Time Series Forecastingcited by 6
  7. 2023MemDA: Forecasting Urban Time Series with Memory-based Drift Adaptationcited by 6
  8. 2024CausalMob: Causal Human Mobility Prediction with LLMs-derived Human Intentions toward Public Eventscited by 5
  9. 2025Learning to Estimate Package Delivery Time in Mixed Imbalanced Delivery and Pickup Logistics Servicescited by 5
  10. 2024DualCast: A Model to Disentangle Aperiodic Events from Traffic Seriescited by 4
  11. 2025DeepSTA: A Spatial-Temporal Attention Network for Logistics Delivery Timely Rate Prediction in Anomaly Conditionscited by 4
  12. 2025How Different from the Past? Spatio-Temporal Time Series Forecasting with Self-Supervised Deviation Learningcited by 4
6 more
  1. 2022MegaCRN: Meta-Graph Convolutional Recurrent Network for Spatio-Temporal Modelingcited by 3
  2. 2026WED-Net: A Weather-Effect Disentanglement Network with Causal Augmentation for Urban Flow Predictioncited by 2
  3. 2024Causal Adjacency Learning for Spatiotemporal Prediction Over Graphscited by 1
  4. 2024STTM: A New Approach Based Spatial-Temporal Transformer And Memory Network For Real-time Pressure Signal In On-demand Food Deliverycited by 0
  5. 2026Frequency-Domain Multi-Modality Transportation Modelingcited by 0
  6. 2026EventOD: Event-Aware OD Flow Generation via LLM-Guided Semantic Modulationcited by 0