Creating a composite road safety performance index by a hierarchical fuzzy TOPSIS approach

Qiong Bao, D. Ruan, Yongjun Shen, Elke Hermans
{"title":"Creating a composite road safety performance index by a hierarchical fuzzy TOPSIS approach","authors":"Qiong Bao, D. Ruan, Yongjun Shen, Elke Hermans","doi":"10.1109/ISKE.2010.5680828","DOIUrl":null,"url":null,"abstract":"With the increasing public awareness of the complexity of road safety phenomenon, much more detailed aspects of crash and injury causation rather than only crash data (e.g., the number of road fatalities) are extensively investigated in the current road safety research. Safety performance indicators (SPIs), which are causally related to the number of crashes or to the injury consequences of a crash, are thus rapidly developed and increasingly used. Furthermore, to measure the multi-dimensional concept of road safety which cannot be captured by a single indicator, the exploration of a composite road safety performance index is attractive and desirable. This study proposes a hierarchical fuzzy TOPSIS method to combine the multilayer SPIs into one overall index by incorporating experts' opinions. Using the number of road fatalities per million inhabitants as a relevant point of reference, the proposed method has proven valuable as an alternative way in creating a composite road safety performance index for a set of European countries. Meanwhile, it effectively handles experts' linguistic expressions instead of crisp values and takes the layered hierarchy of the indicators into account which is seldom considered in the current index research.","PeriodicalId":6417,"journal":{"name":"2010 IEEE International Conference on Intelligent Systems and Knowledge Engineering","volume":"10 1","pages":"458-463"},"PeriodicalIF":0.0000,"publicationDate":"2010-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 IEEE International Conference on Intelligent Systems and Knowledge Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISKE.2010.5680828","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7

Abstract

With the increasing public awareness of the complexity of road safety phenomenon, much more detailed aspects of crash and injury causation rather than only crash data (e.g., the number of road fatalities) are extensively investigated in the current road safety research. Safety performance indicators (SPIs), which are causally related to the number of crashes or to the injury consequences of a crash, are thus rapidly developed and increasingly used. Furthermore, to measure the multi-dimensional concept of road safety which cannot be captured by a single indicator, the exploration of a composite road safety performance index is attractive and desirable. This study proposes a hierarchical fuzzy TOPSIS method to combine the multilayer SPIs into one overall index by incorporating experts' opinions. Using the number of road fatalities per million inhabitants as a relevant point of reference, the proposed method has proven valuable as an alternative way in creating a composite road safety performance index for a set of European countries. Meanwhile, it effectively handles experts' linguistic expressions instead of crisp values and takes the layered hierarchy of the indicators into account which is seldom considered in the current index research.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
采用层次模糊TOPSIS方法建立道路安全性能综合指标
随着公众日益认识到道路安全现象的复杂性,在目前的道路安全研究中,广泛调查了更详细的碰撞和伤害原因方面,而不仅仅是碰撞数据(例如,道路死亡人数)。因此,与碰撞次数或碰撞伤害后果有因果关系的安全性能指标(spi)得到了迅速发展和越来越多的使用。此外,为了衡量单一指标无法捕捉的多维道路安全概念,探索复合道路安全绩效指数是有吸引力和可取的。本研究提出了一种层次模糊TOPSIS方法,通过结合专家意见,将多层指标综合为一个整体指标。将每百万居民的道路死亡人数作为相关参考点,所提议的方法已被证明是为一组欧洲国家制定综合道路安全绩效指数的一种有价值的替代方法。同时,它有效地处理了专家的语言表达,而不是简单的数值,并考虑了指标的层次性,这是目前指标研究中很少考虑的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Applying B and ProB to a Real-world Data Validation Project A Method of Point Cloud Processing in Transformer Substation Computational Task Offloading Scheme based on Deep Learning for Financial Big Data A Feasible System of Automatic Flame Detection and Tracking for Fire-fighting Robot Design of Parallel Algorithm of Transfer Learning based on Weak Classifier
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1