摘要
机器人与机器学习每日新闻的一位新闻记者兼新闻编辑-机器学习的最新研究结果已经发表。根据Newsrx记者在塞尔维亚贝尔格莱德的新闻报道，研究表明，“电动汽车正在成为全球范围内的可持续交通解决方案。电动汽车充电标准(EVCS)不足阻碍了电动汽车的广泛应用。”这项研究的财政支持来自帕杜比斯大学。新闻记者引用了贝尔格莱德大学的一项研究，“最优的EVCS选址至关重要，需要多准则决策(MCDM)分析和地理信息系统(GIS)。该研究首次在选址问题中引入了一种集成GIS、机器学习和MCDM的创新方法。”摘要:本研究旨在通过对现有EVCS站点的主要和次要标准的回顾性考察,填补评价EVCS在城市密集地区放置的空白。以布拉格为例,采用多种MCDM技术,评估EVCS在城市密集地区的适用性。空间分析促进了标准重新分类,随机森林(RF)算法确定了关键标准,特别是交通基础设施和人口密度。层次分析法(AHP),模糊层次分析法,结果表明,模糊层次分析法比其他MCDM方法更适合于评价evc的适宜性分析,而模糊层次分析法与MCDM方法相比,更适合于评价evc的适宜性。这项研究不仅评估了标准重要性和技术有效性，还标志着MCDM从主观到客观的、数据驱动的决策范式转变，通过纳入机器L收益，从主观到客观的、数据驱动的决策。"
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on Machine Learn ing have been published. According to news reporting from Belgrade, Serbia, by N ewsRx journalists, research stated, “Electric vehicles are emerging as sustainab le transportation solutions worldwide. Inadequate electric vehicle charging stat ions (EVCS) hinder their broader adoption.” Financial support for this research came from University of Pardubice. The news correspondents obtained a quote from the research from the University o f Belgrade, “Optimal EVCS site selection is vital, requiring multicriteria decis ion-making (MCDM) analyses and geographic information systems (GIS). The researc h introduces, for the first time in site selection problems, an innovative metho dology that integrates GIS, machine learning, and MCDM, effectively mapping the suitability of EVCS in urban environments. This study aims to fill the gap in ev aluating EVCS placement in densely urbanized areas by adopting a retrospective a pproach to examine both primary and secondary criteria at existing EVCS sites. F ocusing on Prague - a city with a dense EVCS network - it assesses their suitabi lity using various MCDM techniques, representing a significant advance in optimi zing EVCS distribution. Spatial analysis facilitated criteria reclassification, and the random forest (RF) algorithm identified key criteria, particularly trans portation infrastructure and population density. Analytic hierarchy process (AHP ), fuzzy AHP, and stepwise weight assessment ratio analysis (SWARA) are employed to derive criteria weights and suitability maps. Comparative results showed a p redilection towards fuzzy AHP over other MCDM methods for modeling suitability a nalysis for placing EVCS, indicating its marginal effectiveness with the largest high-suitability area (172 km 2 ) and hosting the most EVCS (461) in this zone with the highest average score (4.49). This study not only assesses criteria imp ortance and technique efficacy but also signifies a paradigm shift in MCDM from subjective to objective, data -driven decision-making by incorporating machine l earning.”
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