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Related Concept Videos

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End Point Prediction: Gran Plot

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Related Experiment Videos

Data-driven fuzzy information granulation for predicting freight volume trends.

Yunbo Gao1, Xinyu Wang1, Ming Niu1

  • 1College of Traffic and Vehicle Engineering, Wuxi University, Wuxi, China.

Plos One
|May 7, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Fuzzy Information Granulation (FIG) and Improved Particle Swarm Optimization (IPSO) integrated model for accurate rail freight volume trend prediction, significantly improving uncertainty quantification and reliability for operational planning.

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Area of Science:

  • Operations Research
  • Data Science
  • Transportation Logistics

Background:

  • Traditional rail freight volume prediction methods struggle with data fuzziness, complexity, and nonlinearity, limiting their practical application, especially in handling uncertainty.
  • Existing deterministic models often fail to meet the practical needs for predicting freight volume trends accurately, particularly when dealing with inherent data uncertainties.

Purpose of the Study:

  • To develop an advanced freight volume trend prediction model that integrates Fuzzy Information Granulation (FIG) with evolutionary optimization to address the limitations of traditional methods.
  • To enhance the precision and reliability of rail freight volume trend prediction, including uncertainty quantification, for better railway infrastructure planning and operational optimization.

Main Methods:

  • A three-phase methodology was employed, starting with Fuzzy Information Granulation (FIG) to transform raw time-series data into tri-granular representations (Low, R, Up) using fuzzy c-means clustering with temporal constraints.
  • A Support Vector Machine (SVM) was utilized for granular modeling on complex, small-sample datasets, optimized by an Improved Particle Swarm Optimization (IPSO) algorithm featuring dynamic inertia weights and mutation operators.
  • A hybrid FIG-IPSO-SVM architecture was established for granular-level regression, incorporating uncertainty quantification to provide robust prediction intervals.

Main Results:

  • The proposed FIG-IPSO-SVM model demonstrated statistically significant improvements over benchmark models (FIG-GS-SVM, FIG-PSO-SVM), achieving the smallest prediction error for each granulated set (Low, R, Up).
  • The model achieved the lowest mean maxima of absolute percentage error (5.03%) for the prediction interval of freight volume, indicating superior accuracy.
  • The FIG-IPSO-SVM framework yielded the tightest prediction interval, with a relative width (Rw) of 8.53% and an interval width (W) of 516,209 tons, outperforming all comparative models.

Conclusions:

  • The developed FIG-IPSO-SVM framework substantially enhances the precision of interval prediction and the reliability of trend detection for rail freight volume.
  • The model's ability to quantify uncertainty and provide tight prediction intervals offers actionable intelligence for optimizing railway infrastructure planning and operations.
  • This hybrid approach effectively overcomes the challenges posed by data fuzziness and nonlinearity in traditional freight volume prediction models.