mlsanityData to 14 Oct 2026

GraphSparseNet: a Novel Method for Large Scale Traffic Flow Prediction

arXiv 2502.19823 ↗First posted 27 Feb 2025cs.LG cs.AIcited by 2

Not on the map. Why?

Abstract (arXiv)

Traffic flow forecasting is a critical spatio-temporal data mining task with wide-ranging applications in intelligent route planning and dynamic traffic management. Recent advancements in deep learning, particularly through Graph Neural Networks (GNNs), have significantly enhanced the accuracy of these forecasts by capturing complex spatio-temporal dynamics. However, the scalability of GNNs remains a challenge due to their exponential growth in model complexity with increasing nodes in the graph. Existing methods to address this issue, including sparsification, decomposition, and kernel-based approaches, either do not fully resolve the complexity issue or risk compromising predictive accuracy. This paper introduces GraphSparseNet (GSNet), a novel framework designed to improve both the scalability and accuracy of GNN-based traffic forecasting models. GraphSparseNet is comprised of two core modules: the Feature Extractor and the Relational Compressor. These modules operate with linear time and space complexity, thereby reducing the overall computational complexity of the model to a linear scale. Our extensive experiments on multiple real-world datasets demonstrate that GraphSparseNet not only significantly reduces training time by 3.51x compared to state-of-the-art linear models but also maintains high predictive performance.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 20 of its 42 references that are in the map. First posted on arXiv 27 Feb 2025.

Builds on

20 of 42 references.

References · 20

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  2. 2015Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcastingcited by 1,436
  3. 2017Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastingcited by 1,136
  4. 2019Graph WaveNet for Deep Spatial-Temporal Graph Modelingcited by 829
  5. 2020Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networkscited by 566
  6. 2020Adaptive Graph Convolutional Recurrent Network for Traffic Forecastingcited by 530
  7. 2016Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Predictioncited by 333
  8. 2020Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecastingcited by 213
  9. 2021Spatial-Temporal Graph ODE Networks for Traffic Flow Forecastingcited by 210
  10. 2023Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Surveycited by 140
  11. 2023PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow Predictioncited by 137
  12. 2023LargeST: A Benchmark Dataset for Large-Scale Traffic Forecastingcited by 118
8 more
  1. 2022The Devil in Linear Transformercited by 98
  2. 2024UniST: A Prompt-Empowered Universal Model for Urban Spatio-Temporal Predictioncited by 87
  3. 2019Learning Graph Embedding with Adversarial Training Methodscited by 84
  4. 2018Bike Flow Prediction with Multi-Graph Convolutional Networkscited by 42
  5. 2016Short-term traffic flow forecasting with spatial-temporal correlation in a hybrid deep learning frameworkcited by 23
  6. 2023Localised Adaptive Spatial-Temporal Graph Neural Networkcited by 14
  7. 2020Fine-Grained Urban Flow Inferencecited by 10
  8. 2019Dynamic Spatial-Temporal Representation Learning for Traffic Flow Predictioncited by 2

Built on by

Cited by year

Citing papers per year: 2025: 2.

2 papers.

  1. 2025Efficient Traffic Forecasting on Large-Scale Road Network by Regularized Adaptive Graph Convolutioncited by 1
  2. 2025HieraMix: A Hierarchical MLP-Mixer for Large-Scale Traffic Forecastingcited by 1