行人-车辆事故易发地点识别的综合解决方案:基于gis的多准则决策方法

IF 2.4 3区 工程技术 Q3 TRANSPORTATION
Burak Yigit Katanalp, Ezgi Eren, Y. Alver
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引用次数: 5

摘要

基于gis的行人-车辆事故易发地点(api)的空间分布存在差异。此外,由传统模型确定哪些APL更为关键,或者哪种模型在确定APL方面更成功仍然是一个主要问题。为了弥补这一差距,本文提出了一种创新的基于gis的多标准决策(MCDM)方法,以识别最关键的api,并将api与四个基于gis的模型的折衷结果进行排名。将平面KDE、基于网络的KDE、Getis-Ord Gi*、Local Moran’s I等加权预测准确度指数(PAI)与MCDM方法(传统VIKOR和心理测量VIKOR)进行比较。结果与讨论:妥协方案中15个最关键的api按4个时间段进行排名。基于网络的KDE提供了最好的性能,而Local Moran的I表现最差。敏感性分析表明,心理测量VIKOR在api排名中提供了可接受的稳定性。创新的MCDM方法允许将几个模型的结果一起评估。因此,确定了更可靠的api。预算有限的地方政府可以根据建议的方法决定应该考虑哪些api来改善行人安全,并且可以应用于任何研究区域。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An integrated solution to identify pedestrian-vehicle accident prone locations: GIS-based multicriteria decision approach
Abstract Spatial distributions of pedestrian-vehicle accident-prone locations (APLs) according to GIS-based models differ. Also, which APLs are determined by conventional models are more critical or which model is more successful in determining APL is still a major concern. To bridge this gap, this paper presents an innovative GIS-based Multi-Criteria Decision Making (MCDM) approach to identify the most critical APLs and to rank APLs with the compromising results of four GIS-based models. The results of planar KDE, network-based KDE, Getis-Ord Gi*, and Local Moran’s I which are weighted with prediction accuracy index (PAI), were evaluated together with MCDM methods: traditional VIKOR and psychometric VIKOR. Results & Discussion: The 15 most critical APLs in the compromise solution were ranked for four time periods. Network-based KDE gave the best performance, while Local Moran’s I performed the worst. Sensitivity analysis showed that the Psychometric VIKOR provides acceptable stability in the rankings of the APLs. The innovative MCDM approaches allowed the results of several models to be evaluated together. Thus, more reliable APLs were identified. Local governments with limited budgets can determine which APLs should be considered to improve pedestrian safety with the recommended approach and can apply to any study area.
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来源期刊
CiteScore
6.00
自引率
15.40%
发文量
38
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