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

Research output: Contribution to journalArticle

Authors

  • Da Ruan
  • Tianrui Li
  • Youngjun Shen
  • Elke Hermans
  • Geert Wets
  • Southwest Jiaotong University
  • Uhasselt - Hasselt University

Documents & links

DOI

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.

Details

Original languageEnglish
Pages (from-to)517-534
Number of pages18
JournalComputational Intelligence
Volume32
Issue number4
DOIs
Publication statusPublished - 1 Nov 2016
Externally publishedYes

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

ID: 5647586