A new weighting approach based on rough set theory and granular computing for road safety indicators analysis

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A new weighting approach based on rough set theory and granular computing for road safety indicators analysis. / Ruan, Da; Li, Tianrui; Shen, Youngjun; Hermans, Elke; Wets, Geert.

In: Computational Intelligence, Vol. 32, No. 4, 01.11.2016, p. 517-534.

Research output: Contribution to journalArticle

Harvard

Ruan, D, Li, T, Shen, Y, Hermans, E & Wets, G 2016, 'A new weighting approach based on rough set theory and granular computing for road safety indicators analysis', Computational Intelligence, vol. 32, no. 4, pp. 517-534. https://doi.org/10.1111/coin.12061

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Vancouver

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Ruan, Da ; Li, Tianrui ; Shen, Youngjun ; Hermans, Elke ; Wets, Geert. / A new weighting approach based on rough set theory and granular computing for road safety indicators analysis. In: Computational Intelligence. 2016 ; Vol. 32, No. 4. pp. 517-534.

Bibtex - Download

@article{cad8ee983c46457b842fea2a8194f829,
title = "A new weighting approach based on rough set theory and granular computing for road safety indicators analysis",
abstract = "The steadily increasing volume of road traffic has resulted in many safety problems. Road safety performance indicators may contribute to better understand current safety conditions and monitor the effect of policy interventions. A composite road safety performance indicator is desired to reduce the dimensions of selected risk factors. The essential step for constructing such a composite indicator is to assign a suitable weight to each indicator. However, no agreement on weighting and aggregation in the composite indicator literature has been reached so far. Granular computing is an emerging computing paradigm of information processing that makes use of granules in problem solving. Rough set theory is considered as one of the leading special cases of granular computing approaches. In this article, a new weighting approach based on rough set theory and granular computing is introduced for road safety indicator analysis. The proposed method is applied to a real case study of 21 European countries of which only the class information (not the real values) on all indicators is used to calculate the weights. Experimental evaluation shows that it is an efficient approach to combine individual road safety performance indicators into a composite one.",
keywords = "Granular computing, Road safety performance indicators, Rough set theory, Weighting, Accident prevention, Algorithms, Computation theory, Waste disposal, Safety engineering, Set theory, Emerging computing paradigm, Composite indicators",
author = "Da Ruan and Tianrui Li and Youngjun Shen and Elke Hermans and Geert Wets",
note = "Score=10",
year = "2016",
month = "11",
day = "1",
doi = "10.1111/coin.12061",
language = "English",
volume = "32",
pages = "517--534",
journal = "Computational Intelligence",
issn = "0824-7935",
publisher = "Wiley",
number = "4",

}

RIS - Download

TY - JOUR

T1 - A new weighting approach based on rough set theory and granular computing for road safety indicators analysis

AU - Ruan, Da

AU - Li, Tianrui

AU - Shen, Youngjun

AU - Hermans, Elke

AU - Wets, Geert

N1 - Score=10

PY - 2016/11/1

Y1 - 2016/11/1

N2 - The steadily increasing volume of road traffic has resulted in many safety problems. Road safety performance indicators may contribute to better understand current safety conditions and monitor the effect of policy interventions. A composite road safety performance indicator is desired to reduce the dimensions of selected risk factors. The essential step for constructing such a composite indicator is to assign a suitable weight to each indicator. However, no agreement on weighting and aggregation in the composite indicator literature has been reached so far. Granular computing is an emerging computing paradigm of information processing that makes use of granules in problem solving. Rough set theory is considered as one of the leading special cases of granular computing approaches. In this article, a new weighting approach based on rough set theory and granular computing is introduced for road safety indicator analysis. The proposed method is applied to a real case study of 21 European countries of which only the class information (not the real values) on all indicators is used to calculate the weights. Experimental evaluation shows that it is an efficient approach to combine individual road safety performance indicators into a composite one.

AB - The steadily increasing volume of road traffic has resulted in many safety problems. Road safety performance indicators may contribute to better understand current safety conditions and monitor the effect of policy interventions. A composite road safety performance indicator is desired to reduce the dimensions of selected risk factors. The essential step for constructing such a composite indicator is to assign a suitable weight to each indicator. However, no agreement on weighting and aggregation in the composite indicator literature has been reached so far. Granular computing is an emerging computing paradigm of information processing that makes use of granules in problem solving. Rough set theory is considered as one of the leading special cases of granular computing approaches. In this article, a new weighting approach based on rough set theory and granular computing is introduced for road safety indicator analysis. The proposed method is applied to a real case study of 21 European countries of which only the class information (not the real values) on all indicators is used to calculate the weights. Experimental evaluation shows that it is an efficient approach to combine individual road safety performance indicators into a composite one.

KW - Granular computing

KW - Road safety performance indicators

KW - Rough set theory

KW - Weighting

KW - Accident prevention

KW - Algorithms

KW - Computation theory

KW - Waste disposal

KW - Safety engineering

KW - Set theory

KW - Emerging computing paradigm

KW - Composite indicators

UR - http://ecm.sckcen.be/OTCS/llisapi.dll/open/35888561

U2 - 10.1111/coin.12061

DO - 10.1111/coin.12061

M3 - Article

VL - 32

SP - 517

EP - 534

JO - Computational Intelligence

JF - Computational Intelligence

SN - 0824-7935

IS - 4

ER -

ID: 5647586