mlsanityData to 7 Oct 2026

Fusion Matrix Prompt Enhanced Self-Attention Spatial-Temporal Interactive Traffic Forecasting Framework

arXiv 2410.09356 ↗First posted 12 Oct 2024cs.LGcited by 0

Not on the map. Why?

Abstract (arXiv)

Recently, spatial-temporal forecasting technology has been rapidly developed due to the increasing demand for traffic management and travel planning. However, existing traffic forecasting models still face the following limitations. On one hand, most previous studies either focus too much on real-world geographic information, neglecting the potential traffic correlation between different regions, or overlook geographical position and only model the traffic flow relationship. On the other hand, the importance of different time slices is ignored in time modeling. Therefore, we propose a Fusion Matrix Prompt Enhanced Self-Attention Spatial-Temporal Interactive Traffic Forecasting Framework (FMPESTF), which is composed of spatial and temporal modules for down-sampling traffic data. The network is designed to establish a traffic fusion matrix considering spatial-temporal heterogeneity as a query to reconstruct a data-driven dynamic traffic data structure, which accurately reveal the flow relationship of nodes in the traffic network. In addition, we introduce attention mechanism in time modeling, and design hierarchical spatial-temporal interactive learning to help the model adapt to various traffic scenarios. Through extensive experimental on six real-world traffic datasets, our method is significantly superior to other baseline models, demonstrating its efficiency and accuracy in dealing with traffic forecasting problems.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 22 of its 39 references that are in the map. First posted on arXiv 12 Oct 2024.

Builds on

22 of 39 references.

References · 22

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,571
  3. 2021MLP-Mixer: An all-MLP Architecture for Visioncited by 1,441
  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. 2016Language Modeling with Gated Convolutional Networkscited by 688
  7. 2020Adaptive Graph Convolutional Recurrent Network for Traffic Forecastingcited by 525
  8. 2019GMAN: A Graph Multi-Attention Network for Traffic Predictioncited by 433
  9. 2020Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecastingcited by 212
  10. 2021Spatial-Temporal Graph ODE Networks for Traffic Flow Forecastingcited by 209
  11. 2014Recurrent Neural Network Regularizationcited by 189
  12. 2021Graph Neural Controlled Differential Equations for Traffic Forecastingcited by 133
10 more
  1. 2018Multilevel Wavelet Decomposition Network for Interpretable Time Series Analysiscited by 61
  2. 2020Coupled Layer-wise Graph Convolution for Transportation Demand Predictioncited by 54
  3. 2024Can we Soft Prompt LLMs for Graph Learning Tasks?cited by 52
  4. 2024Macro Graph Neural Networks for Online Billion-Scale Recommender Systemscited by 40
  5. 2023SGL-PT: A Strong Graph Learner with Graph Prompt Tuningcited by 35
  6. 2021Ranger21: a synergistic deep learning optimizercited by 21
  7. 2023GraphPro: Graph Pre-training and Prompt Learning for Recommendationcited by 17
  8. 2022Spatial-Temporal Interactive Dynamic Graph Convolution Network for Traffic Forecastingcited by 14
  9. 2023Causal conditional hidden Markov model for multimodal traffic predictioncited by 14
  10. 2022Modeling Network-level Traffic Flow Transitions on Sparse Datacited by 12

Built on by

Not cited yet.