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Improving decision reliability in transport safety engineering through a machine learning-based model.

Ruikang Yan1, Jialin Li2, Xingze Liu3

  • 1College of Computer, Mathematical, and Natural Sciences, University of Maryland, College Park, MD, 20742, USA.

Scientific Reports
|June 24, 2026
PubMed
Summary

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This summary is machine-generated.

This study introduces a hybrid decision model for transport safety engineering, enhancing policy inferences and decision reliability. The model integrates machine learning for clearer, actionable patterns in complex datasets, improving resource allocation.

Area of Science:

  • Decision Science
  • Transport Safety Engineering
  • Machine Learning Applications

Background:

  • Robust decision-making and policy inferences are critical in multi-criteria decision-making, especially within transport safety.
  • Existing methods may present cognitive burdens and lack flexibility in preference articulation.
  • High-dimensional and noisy datasets pose challenges for reliable analysis and pattern identification.

Purpose of the Study:

  • To develop a hybrid decision model integrating preference functions and machine learning for enhanced policy support.
  • To improve decision reliability and reduce cognitive load for decision-makers in complex scenarios.
  • To demonstrate the model's effectiveness in transport safety engineering applications.

Main Methods:

  • Developed a hybrid model: EXPROM II-K-means with linear discriminant analysis (LDA).
Keywords:
Decision reliabilityLinear discriminant analysisMachine learningPreference functionTransport safety engineering

Related Experiment Videos

  • Incorporated a refined nonparametric preference function into the EXPROM II method for flexibility.
  • Utilized LDA for dimensionality reduction, noise filtering, and feature emphasis to enhance K-means clustering.

    • Main Results:

    • The hybrid model demonstrated improved clustering performance on high-dimensional and noisy data.
    • LDA effectively simplified data structure and highlighted informative attributes, leading to clearer patterns.
    • A case study in Asia-Pacific Economic Cooperation transport safety confirmed the model's reliability and scalability.

    Conclusions:

    • The proposed EXPROM II-K-means with LDA model provides a practical, interpretable, and intelligent support tool.
    • The framework enhances stability and reliability for complex decision tasks, particularly in resource allocation and strategic prioritization.
    • The study offers a robust system for defensible policy inferences and decision reliability in transport safety.