Road safety measurement with reliability using an advanced hybrid decision model

This study proposes a brand-new hybrid multi-criteria decision-making (MCDM) framework that combines High-Dimensional Vector Projection (HDVP) and Between-class Variance Maximization (BeVarMax), termed the HDVP-BeVarMax model, aiming to provide trustworthy decisions and defensible policy conclusions. Specifically, HDVP quantifies the relative proximity of each country to an ideal performance vector in a high-dimensional space, ensuring scale-invariant and geometrically meaningful aggregation. BeVarMax, inspired by Otsu's thresholding method, maximizes between-class variance to identify optimal groupings and uncover latent structure among alternatives, surpassing conventional clustering techniques such as k-means in robustness and global optimality. Using longitudinal data from 13 East Asia Summit (EAS) countries spanning 2012 to 2023, this model is applied to measure national road safety performance based on 15 tailored safety performance indicators (SPIs). Results demonstrate the model's reliability, robustness, and superior discriminative power across normalization and weighting schemes, validated through extensive sensitivity and benchmarking analyses. Policy implications are twofold: it enables benchmarking of high and low performers to guide targeted interventions, and it supports strategic resource allocation by identifying priority areas such as enforcement, infrastructure, and behavioral factors. The proposed model serves as a practical decision-support tool for monitoring progress and fostering regional cooperation in line with global road safety goals.

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Publication Details

Journal
Scientific Reports
Published
2025-10-08
DOI
https://doi.org/10.1038/s41598-025-18918-7
Citations
1
Primary Topic
Traffic and Road Safety
Type
article
Field-Weighted Citation Impact
1.30

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article

Road safety measurement with reliability using an advanced hybrid decision model

Mingshuo Liu, Haiying Hua, Aifeng Zhou, H. Oliver Gao et al.
1 citations
Scientific Reports
Traffic and Road Safety
1.30
article

Road safety measurement with reliability using an advanced hybrid decision model

Mingshuo Liu, Haiying Hua, Aifeng Zhou, H. Oliver Gao, Faan Chen, H. Liang, Dongxu Qin, F. Tian, Mei Zheng, Xingjian Zhang, Zibo Li, Lei Sun, Jin Yao, Haocheng Yang, H.S. Qi, Yiyun Zhang, Hanrui Feng
article en
1 citations

Abstract

This study proposes a brand-new hybrid multi-criteria decision-making (MCDM) framework that combines High-Dimensional Vector Projection (HDVP) and Between-class Variance Maximization (BeVarMax), termed the HDVP-BeVarMax model, aiming to provide trustworthy decisions and defensible policy conclusions. Specifically, HDVP quantifies the relative proximity of each country to an ideal performance vector in a high-dimensional space, ensuring scale-invariant and geometrically meaningful aggregation. BeVarMax, inspired by Otsu's thresholding method, maximizes between-class variance to identify optimal groupings and uncover latent structure among alternatives, surpassing conventional clustering techniques such as k-means in robustness and global optimality. Using longitudinal data from 13 East Asia Summit (EAS) countries spanning 2012 to 2023, this model is applied to measure national road safety performance based on 15 tailored safety performance indicators (SPIs). Results demonstrate the model's reliability, robustness, and superior discriminative power across normalization and weighting schemes, validated through extensive sensitivity and benchmarking analyses. Policy implications are twofold: it enables benchmarking of high and low performers to guide targeted interventions, and it supports strategic resource allocation by identifying priority areas such as enforcement, infrastructure, and behavioral factors. The proposed model serves as a practical decision-support tool for monitoring progress and fostering regional cooperation in line with global road safety goals.

Scientific ReportsVol. 15(1)
Tongji University (CN), Harvard University (US), University of Illinois Urbana-Champaign (US), University of San Diego (US), Chinese University of Hong Kong (HK), York University (CA), Nanjing University of Information Science and Technology (CN), China Association for Science and Technology (CN), Harvard University Press (US), University of California San Diego (US), Courant Institute of Mathematical Sciences (US), Tabor College (US), University of Chicago (US), Guizhou Electromechanical Research and Design Institute (CN), Zhejiang Radio and Television University (CN), Jinling Institute of Technology (CN), New York University (US), Syracuse University (US), University of Pennsylvania (US), University of California, Davis (US), Nanjing University (CN), Bank of China (CN), Northeastern University (CN)
Harvard University
Openalex Percentile: Top 16%
Traffic and Road Safety
1.30
DOIOpen Access PDFOpenAlex
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