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Enhanced multi objective graph learning approach for optimizing traffic speed prediction on spatial and temporal

B Karthika1, N Uma Maheswari2

  • 1PSNA College of Engineering and Technology (PSNACET), Dindigul, Tamil Nadu, India. karthikabm@gmail.com.

Scientific Reports
|September 30, 2025
PubMed
Summary

This study introduces Multi Objective Graph Learning (MOGL) for accurate traffic speed prediction. MOGL enhances intelligent transportation systems by improving real-time traffic management and reducing congestion.

Keywords:
AccuracyAdaptive samplingGraph learningGraph neural networkMulti objectivePredictionSpatialTemporal

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Area of Science:

  • Intelligent Transportation Systems (ITS)
  • Machine Learning for Spatiotemporal Data Analysis
  • Traffic Engineering and Management

Background:

  • Traffic Speed Prediction (TSP) is crucial for Intelligent Transportation Systems (ITS), enabling efficient traffic management and urban mobility.
  • Existing TSP methods face challenges due to the dynamic nature of temporal and spatial factors, leading to generalization issues and prediction instability.
  • Complicated spatiotemporal dependencies in road networks present significant hurdles for accurate traffic speed prediction.

Purpose of the Study:

  • To propose a novel approach, Multi Objective Graph Learning (MOGL), to address the complexities of traffic speed prediction.
  • To enhance the accuracy and reliability of real-time traffic speed estimation in large-scale road networks.
  • To improve the performance of Intelligent Transportation Systems through more precise traffic speed forecasting.

Main Methods:

  • Developed a three-phase MOGL approach integrating Adaptive Graph Sampling with Spatio Temporal Graph Neural Network (AGS-STGNN).
  • Employed Pareto Efficient Global Optimization (ParEGO) for multi-objective Bayesian optimization in adaptive graph sampling to extract refined spatial and temporal features.
  • Utilized enhanced Attention Gated Recurrent Units (EAGRU) with a feature fusion stage for dynamic prioritization of critical road segments and time intervals.

Main Results:

  • The MOGL approach demonstrated superior performance on benchmark datasets (METR-LA and PeMS-BAY), achieving low Mean Absolute Error (MAE) and Root Mean Square Error (RMSE).
  • Achieved MAE values of 2.09 and 2.15, RMSE values of 3.29 and 3.22, and MAPE values of 3.17 and 3.21 across the datasets.
  • Significantly reduced RMSE by up to 28.9% on the METR-LA dataset compared to DSTMAN, outperforming other state-of-the-art models like STGCN variants.

Conclusions:

  • The proposed MOGL approach offers a significant advancement in real-time and large-scale traffic speed prediction.
  • MOGL effectively captures complex spatiotemporal dependencies, leading to enhanced prediction accuracy and reliability.
  • The model's ability to dynamically prioritize influential factors contributes to its improved performance in intelligent transportation systems.