Xie Ziyue, Chen Faan
Bryanston School, Blandford Forum, Dorset, DT11 0PX, UK.
School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, 02138, USA.
Sci Rep. 2024 Oct 4;14(1):23049. doi: 10.1038/s41598-024-73069-5.
Progress monitoring and action recalibrations are advocated as promising methods for improving road safety, which significantly relates to economic stability and social development. To achieve this, an auditing framework that can evaluate road safety and aid in policymaking is urgently required. To this end, this study developed a systemic decision model that integrates the method based on the removal effects of criteria (MEREC), additive ratio assessment (ARAS), and quantile-based k-means clustering (QBKM), termed MEREC-ARAS-QBKM, with the aim of auditing road safety achievements and providing corresponding policy suggestions with substantial reliability. In particular, the performance of the traditional k-means clustering model was improved by implanting quantiles to determine the initial clustering, which overcomes the uncertainty of k-means clustering owing to the variety of initial cluster centers. Multiple comparisons of empirical results based on a case study of the Asia-Pacific Economic Cooperation (APEC) member economies verified the robustness of the proposed model, demonstrating its applicability, practicability, and reliability in handling real-world multi-criteria decision-making problems in the field of road safety. The empirical findings show that road safety developments among the APEC countries are of class differentiation, suggesting an urgent regional benchmarking. Overall, the proposed methodology empowers decision-makers and policymakers in APEC to swiftly formulate effective action plans, countermeasures, and investment schemes, ultimately contributing to the enhancement of road safety performance and socio-economic benefit across APEC members.
进度监测和行动重新校准被视为改善道路安全的有效方法,这与经济稳定和社会发展密切相关。为此,迫切需要一个能够评估道路安全并协助制定政策的审计框架。为此,本研究开发了一种系统决策模型,该模型整合了基于标准去除效应的方法(MEREC)、加法比率评估(ARAS)和基于分位数的k均值聚类(QBKM),称为MEREC-ARAS-QBKM,旨在审计道路安全成果并提供具有高度可靠性的相应政策建议。特别是,通过植入分位数来确定初始聚类,改进了传统k均值聚类模型的性能,克服了由于初始聚类中心的多样性导致的k均值聚类的不确定性。基于亚太经济合作组织(APEC)成员国的案例研究对实证结果进行的多次比较验证了所提出模型的稳健性,证明了其在处理道路安全领域实际多标准决策问题中的适用性、实用性和可靠性。实证结果表明,亚太经合组织国家的道路安全发展存在类别差异,这表明迫切需要进行区域基准测试。总体而言,所提出的方法使亚太经合组织的决策者和政策制定者能够迅速制定有效的行动计划、对策和投资方案,最终有助于提高亚太经合组织成员国的道路安全绩效和社会经济效益。