mlsanityData to 14 Oct 2026

DualCast: A Model to Disentangle Aperiodic Events from Traffic Series

arXiv 2411.18286 ↗First posted 27 Nov 2024cs.LG cs.AIcited by 4

Not on the map. Why?

Abstract (arXiv)

Traffic forecasting is crucial for transportation systems optimisation. Current models minimise the mean forecasting errors, often favouring periodic events prevalent in the training data, while overlooking critical aperiodic ones like traffic incidents. To address this, we propose DualCast, a dual-branch framework that disentangles traffic signals into intrinsic spatial-temporal patterns and external environmental contexts, including aperiodic events. DualCast also employs a cross-time attention mechanism to capture high-order spatial-temporal relationships from both periodic and aperiodic patterns. DualCast is versatile. We integrate it with recent traffic forecasting models, consistently reducing their forecasting errors by up to 9.6% on multiple real datasets. Our source code is available at https://github.com/suzy0223/DualCast.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 20 of its 49 references that are in the map. First posted on arXiv 27 Nov 2024.

Builds on

20 of 49 references.

References · 20

  1. 2017Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastingcited by 1,136
  2. 2019Graph WaveNet for Deep Spatial-Temporal Graph Modelingcited by 829
  3. 2020Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networkscited by 566
  4. 2019GMAN: A Graph Multi-Attention Network for Traffic Predictioncited by 434
  5. 2017Gated Multimodal Units for Information Fusioncited by 248
  6. 2020Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecastingcited by 213
  7. 2021Spatial-Temporal Graph ODE Networks for Traffic Flow Forecastingcited by 210
  8. 2020Spatial-Temporal Transformer Networks for Traffic Flow Forecastingcited by 180
  9. 2023PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow Predictioncited by 137
  10. 2023FourierGNN: Rethinking Multivariate Time Series Forecasting from a Pure Graph Perspectivecited by 127
  11. 2022Spatio-Temporal Meta-Graph Learning for Traffic Forecastingcited by 88
  12. 2023Deciphering Spatio-Temporal Graph Forecasting: A Causal Lens and Treatmentcited by 47
8 more
  1. 2021When Do Contrastive Learning Signals Help Spatio-Temporal Graph Forecasting?cited by 36
  2. 2023INCREASE: Inductive Graph Representation Learning for Spatio-Temporal Krigingcited by 27
  3. 2021Event-Aware Multimodal Mobility Nowcastingcited by 18
  4. 2023Urban Region Representation Learning with Attentive Fusioncited by 12
  5. 2023Learning Time-aware Graph Structures for Spatially Correlated Time Series Forecastingcited by 7
  6. 2024Spatial-temporal Forecasting for Regions without Observationscited by 6
  7. 2022A Graph and Attentive Multi-Path Convolutional Network for Traffic Predictioncited by 5
  8. 2023Tailoring Self-Attention for Graph via Rooted Subtreescited by 5

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 2.

4 papers.

  1. 2025Revitalizing Canonical Pre-Alignment for Irregular Multivariate Time Series Forecastingcited by 6
  2. 2025The Hybrid Multimodal Graph Index (HMGI): A Comprehensive Framework for Integrated Relational and Vector Searchcited by 0
  3. 2026VLBM: Variational Latent Basis Modeling for OOD Robust Multivariate Time Series Forecastingcited by 0
  4. 2026Eliminating Propagation Delay: Attention-Based Spatial-Temporal Fusion Graph Convolution Network for Traffic Flow Predictioncited by 0