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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/22839

Title: A New Weighting Approach Based on Rough Set Theory and Granular Computing for Road Safety Indicator Analysis
Authors: Li, Tianrui
Ruan, Da
Shen, Yongjun
Hermans, Elke
Wets, Geert
Issue Date: 2016
Citation: COMPUTATIONAL INTELLIGENCE, 32(4), p. 517-534
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.
Notes: [Li, Tianrui] Southwest Jiaotong Univ, Sch Informat Sci & Technol, Chengdu 610031, Peoples R China. [Ruan, Da] Belgian Nucl Res Ctr SCK CEN, Mol, Belgium. [Ruan, Da; Shen, Yongjun; Hermans, Elke; Wets, Geert] Hasselt Univ, Transportat Res Inst, Hasselt, Belgium.
URI: http://hdl.handle.net/1942/22839
DOI: 10.1111/coin.12061
ISI #: 000387354900001
ISSN: 0824-7935
Category: A1
Type: Journal Contribution
Validation: ecoom, 2017
Appears in Collections: Research publications

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