Road safety assessment and risks prioritization using an integrated SWARA and MARCOS approach under spherical fuzzy environment.

IF 4.5 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Neural Computing & Applications Pub Date : 2023-01-01 DOI:10.1007/s00521-022-07929-4
Saeid Jafarzadeh Ghoushchi, Sina Shaffiee Haghshenas, Ali Memarpour Ghiaci, Giuseppe Guido, Alessandro Vitale
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引用次数: 27

Abstract

There are a lot of elements that make road safety assessment situations unpredictable and hard to understand. This could put people's lives in danger, hurt the mental health of a society, and cause permanent financial and human losses. Due to the ambiguity and uncertainty of the risk assessment process, a multi-criteria decision-making technique for dealing with complex systems that involves choosing one of many options is an important strategy of assessing road safety. In this study, an integrated stepwise weight assessment ratio analysis (SWARA) with measurement of alternatives and ranking according to compromise solution (MARCOS) approach under a spherical fuzzy (SF) set was considered. Then, the proposed methodology was applied to develop the approach of failure mode and effect analysis (FMEA) for rural roads in Cosenza, southern Italy. Also, the results of modified FMEA by SF-SWARA-MARCOS were compared with the results of conventional FMEA. The risk score results demonstrated that the source of risk (human) plays a significant role in crashes compared to other sources of risk. The two risks, including landslides and floods, had the lowest values among the factors affecting rural road safety in Calabria, respectively. The correlation between scenario outcomes and main ranking orders in weight values was also investigated. This study was done in line with the goals of sustainable development and the goal of sustainable mobility, which was to find risks and lower the number of accidents on the road. As a result, it is thus essential to reconsider laws and measures necessary to reduce human risks on the regional road network of Calabria to improve road safety.

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球形模糊环境下基于SWARA和MARCOS的道路安全评价与风险排序
有很多因素使得道路安全评估情况不可预测且难以理解。这可能使人们的生命处于危险之中,损害社会的精神健康,并造成永久性的经济和人员损失。由于风险评估过程的模糊性和不确定性,多准则决策技术是评估道路安全的一种重要策略。本文考虑了球面模糊(SF)集下基于折衷解(MARCOS)方法的综合逐步权重评价比分析(SWARA)方法。然后,将提出的方法应用于意大利南部Cosenza农村道路的失效模式和影响分析(FMEA)方法。并将SF-SWARA-MARCOS改进的FMEA结果与传统的FMEA结果进行了比较。风险评分结果表明,与其他风险来源相比,风险来源(人为)在撞车事故中起着重要作用。在影响卡拉布里亚农村道路安全的因素中,滑坡和洪水这两种风险分别具有最低的值。还研究了情景结果与权重值的主要排序顺序之间的相关性。本研究符合可持续发展的目标和可持续移动的目标,即发现风险,降低道路上的事故数量。因此,必须重新考虑必要的法律和措施,以减少卡拉布里亚区域道路网络的人为风险,以提高道路安全。
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来源期刊
Neural Computing & Applications
Neural Computing & Applications 工程技术-计算机:人工智能
CiteScore
11.40
自引率
8.30%
发文量
1280
审稿时长
6.9 months
期刊介绍: Neural Computing & Applications is an international journal which publishes original research and other information in the field of practical applications of neural computing and related techniques such as genetic algorithms, fuzzy logic and neuro-fuzzy systems. All items relevant to building practical systems are within its scope, including but not limited to: -adaptive computing- algorithms- applicable neural networks theory- applied statistics- architectures- artificial intelligence- benchmarks- case histories of innovative applications- fuzzy logic- genetic algorithms- hardware implementations- hybrid intelligent systems- intelligent agents- intelligent control systems- intelligent diagnostics- intelligent forecasting- machine learning- neural networks- neuro-fuzzy systems- pattern recognition- performance measures- self-learning systems- software simulations- supervised and unsupervised learning methods- system engineering and integration. Featured contributions fall into several categories: Original Articles, Review Articles, Book Reviews and Announcements.
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