mlsanityData to 7 Oct 2026

Spatio-Temporal Latent Graph Structure Learning for Traffic Forecasting

arXiv 2202.12586 ↗First posted 25 Feb 2022cs.LGcited by 7

Not on the map. Why?

Abstract (arXiv)

Accurate traffic forecasting, the foundation of intelligent transportation systems (ITS), has never been more significant than nowadays due to the prosperity of smart cities and urban computing. Recently, Graph Neural Network truly outperforms the traditional methods. Nevertheless, the most conventional GNN-based model works well while given a pre-defined graph structure. And the existing methods of defining the graph structures focus purely on spatial dependencies and ignore the temporal correlation. Besides, the semantics of the static pre-defined graph adjacency applied during the whole training progress is always incomplete, thus overlooking the latent topologies that may fine-tune the model. To tackle these challenges, we propose a new traffic forecasting framework -- Spatio-Temporal Latent Graph Structure Learning networks (ST-LGSL). More specifically, the model employs a graph generator based on Multilayer perceptron and K-Nearest Neighbor, which learns the latent graph topological information from the entire data considering both spatial and temporal dynamics. Furthermore, with the initialization of MLP-kNN based on ground-truth adjacency matrix and similarity metric in kNN, ST-LGSL aggregates the topologies focusing on geography and node similarity. Additionally, the generated graphs act as the input of the Spatio-temporal prediction module combined with the Diffusion Graph Convolutions and Gated Temporal Convolutions Networks. Experimental results on two benchmarking datasets in real world demonstrate that ST-LGSL outperforms various types of state-of-art baselines.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 21 of its 37 references that are in the map. First posted on arXiv 25 Feb 2022.

Builds on

21 of 37 references.

References · 21

  1. 2017Graph Attention Networkscited by 8,854
  2. 2016WaveNet: A Generative Model for Raw Audiocited by 1,710
  3. 2015Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcastingcited by 1,428
  4. 2017Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastingcited by 1,125
  5. 2019Graph WaveNet for Deep Spatial-Temporal Graph Modelingcited by 820
  6. 2020Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networkscited by 563
  7. 2020Adaptive Graph Convolutional Recurrent Network for Traffic Forecastingcited by 525
  8. 2019GMAN: A Graph Multi-Attention Network for Traffic Predictioncited by 433
  9. 2016Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Predictioncited by 330
  10. 2019Learning Discrete Structures for Graph Neural Networkscited by 228
  11. 2020Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecastingcited by 212
  12. 2020Data Augmentation for Graph Neural Networkscited by 200
9 more
  1. 2021Discrete Graph Structure Learning for Forecasting Multiple Time Seriescited by 169
  2. 2021Dynamic Graph Convolutional Recurrent Network for Traffic Prediction: Benchmark and Solutioncited by 156
  3. 2021SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networkscited by 108
  4. 2019Graph-Revised Convolutional Networkcited by 58
  5. 2021Traffic Flow Forecasting with Spatial-Temporal Graph Diffusion Networkcited by 52
  6. 2019Learning representations of irregular particle-detector geometry with distance-weighted graph networkscited by 43
  7. 2019Deep Iterative and Adaptive Learning for Graph Neural Networkscited by 17
  8. 2019Urban flows prediction from spatial-temporal data using machine learning: A surveycited by 7
  9. 2020Dynamic Graph Neural Network for Traffic Forecasting in Wide Area Networkscited by 7

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 2; 2024: 2; 2025: 0; 2026: 1.

7 papers.

  1. 2022Modeling Multivariate Biosignals With Graph Neural Networks and Structured State Space Modelscited by 29
  2. 2022STGC-GNNs: A GNN-based traffic prediction framework with a spatial-temporal Granger causality graphcited by 12
  3. 2024DG-Mamba: Robust and Efficient Dynamic Graph Structure Learning with Selective State Space Modelscited by 8
  4. 2023HDGL: A hierarchical dynamic graph representation learning model for brain disorder classificationcited by 1
  5. 2023SAMSGL: Series-Aligned Multi-Scale Graph Learning for Spatio-Temporal Forecastingcited by 1
  6. 2024GraphSCENE: On-Demand Critical Scenario Generation for Autonomous Vehicles in Simulationcited by 0
  7. 2026Bilevel Graph Structure Learning, Revisited: Inner-Channel Origins of the Reported Gaincited by 0