Compressed Spatio Temporal Graph Neural Networks for Multivariate Time-Series Forecasting

Precise traffic flow forecasting is crucial for efficient transportation management and traffic congestion alleviation. Existing models typically fail to process the intricate spatial- temporal relationships in traffic data and thus incur compromised prediction performance. In this work, we introduce a Compressed Spatial-Temporal Enhanced Graph Neural Network (Comp-STEMGNN) to overcome these limitations. Our model combines 1D convolution-based temporal compression with graph neural networks to compress redundant time-series information without compromising vital patterns. Graph convolutional layers and temporal convolutional blocks extract the spatial and temporal relationships and facilitate efficient learning from enormous sensor networks. Experimental comparisons on benchmark traffic datasets show that Comp-STEMGNN outperforms existing approaches in forecasting accuracy while enjoying substantial computational complexity reduction. These findings identify its potential in real-time traffic forecasting and intelligent transportation systems.

• Publication year

  2025

• Venue

  2025 1st IEEE Uttar Pradesh Section Women in Engineering International Conference on Electrical Electronics and Computer Engineering (UPWIECON)

• Publication date

  2025-10-30

• Fields of study

  Not labeled

• Identifiers
  DOI 10.1109/UPWIECON67212.2025.11390294
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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