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http://hdl.handle.net/1942/22839
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DC Field | Value | Language |
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dc.contributor.author | Li, Tianrui | - |
dc.contributor.author | RUAN, Da | - |
dc.contributor.author | SHEN, Yongjun | - |
dc.contributor.author | HERMANS, Elke | - |
dc.contributor.author | WETS, Geert | - |
dc.date.accessioned | 2016-12-02T13:45:10Z | - |
dc.date.available | 2016-12-02T13:45:10Z | - |
dc.date.issued | 2016 | - |
dc.identifier.citation | COMPUTATIONAL INTELLIGENCE, 32(4), p. 517-534 | - |
dc.identifier.issn | 0824-7935 | - |
dc.identifier.uri | http://hdl.handle.net/1942/22839 | - |
dc.description.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. | - |
dc.description.sponsorship | The authors thank anonymous referees for their constructive comments, which have helped to improve the quality of this article. This work is supported by the National Science Foundation of China (Nos. 61175047, 61100117) and the Fundamental Research Funds for the Central Universities (SWJTU11ZT08). | - |
dc.language.iso | en | - |
dc.publisher | WILEY-BLACKWELL | - |
dc.rights | © 2015 Wiley Periodicals, Inc. | - |
dc.subject.other | granular computing; rough set theory; weighting; road safety performance indicators | - |
dc.subject.other | granular computing; rough set theory; weighting; road safety performance indicators | - |
dc.title | A New Weighting Approach Based on Rough Set Theory and Granular Computing for Road Safety Indicator Analysis | - |
dc.type | Journal Contribution | - |
dc.identifier.epage | 534 | - |
dc.identifier.issue | 4 | - |
dc.identifier.spage | 517 | - |
dc.identifier.volume | 32 | - |
local.format.pages | 18 | - |
local.bibliographicCitation.jcat | A1 | - |
dc.description.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. | - |
local.publisher.place | HOBOKEN | - |
local.type.refereed | Refereed | - |
local.type.specified | Article | - |
local.class | dsPublValOverrule/author_version_not_expected | - |
dc.identifier.doi | 10.1111/coin.12061 | - |
dc.identifier.isi | 000387354900001 | - |
item.validation | ecoom 2017 | - |
item.accessRights | Restricted Access | - |
item.fullcitation | Li, Tianrui; RUAN, Da; SHEN, Yongjun; HERMANS, Elke & WETS, Geert (2016) A New Weighting Approach Based on Rough Set Theory and Granular Computing for Road Safety Indicator Analysis. In: COMPUTATIONAL INTELLIGENCE, 32(4), p. 517-534. | - |
item.fulltext | With Fulltext | - |
item.contributor | Li, Tianrui | - |
item.contributor | RUAN, Da | - |
item.contributor | SHEN, Yongjun | - |
item.contributor | HERMANS, Elke | - |
item.contributor | WETS, Geert | - |
crisitem.journal.issn | 0824-7935 | - |
crisitem.journal.eissn | 1467-8640 | - |
Appears in Collections: | Research publications |
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li 1.pdf Restricted Access | Published version | 412.28 kB | Adobe PDF | View/Open Request a copy |
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