CBS ve makine öğrenmesi ile elektrikli araç şarj istasyonlarının yer seçimine etki eden kriterlerin belirlenmesi

Year 2025, Volume: 14 Issue: 4

Muhammed Oğuzhan Mete

Abstract

Elektrikli araç kullanımının dünya genelinde artması, yeni şarj istasyonu kurulumlarının ihtiyacını da ortaya çıkarmaktadır. Bu çalışmanın temel amacı günümüzde içten yanmalı motora sahip araçların yerini alan elektrikli araçların şarj istasyonları için uygun yer seçimi modelinin geliştirilmesidir. Bu amaç doğrultusunda Almanya’nın Münih ile İtalya’nın Milano şehirleri çalışma bölgesi olarak belirlenmiştir. Bu şehirlerin seçilmesinde elektrikli araç şarj istasyonlarının sayısının fazla olması ve ülkelerin trafik ve sürüş kültürü açısından Türkiye’ye benzerliği öne çıkmıştır. Çalışma kapsamında Coğrafi Bilgi Sistemleri (CBS) ile konumsal analizler yapılmış, Açıklanabilir Yapay Zeka (XAI) teknikleri ile regresyon analizi gerçekleştirilerek şarj istasyonları için en uygun yerlerin belirlenmesinde kullanılabilecek bir tahmin modeli geliştirilmiştir. Rastgele Orman yöntemiyle kriterlerin ağırlıkları belirlenmiş ve model doğruluğu Milano şehrinde %27 iken Münih şehrinde %87 olarak gözlemlenmiştir. Sonuç olarak elektrikli araç şarj istasyonları için mevcuttaki istasyonlara ait kullanım verisine dayalı çıkarımlar ile en uygun konumların seçiminde etkin kriterler belirlenmiştir. Böylelikle bu alandaki yatırımların karar destek sistemi ile yönlendirilmesi sağlanarak kaynakların etkin kullanılması, elektrikli araç sahiplerine uygun ulaşım altyapısının sunulması ile fosil yakıtlı araçlardan elektrikli araçlara geçişin kolaylaştırılması amaçlanmıştır.

Keywords

Açıklanabilir Yapay Zeka , Coğrafi Bilgi Sistemleri , Elektrikli Araç Şarj İstasyonu , Makine Öğrenmesi , Yer Seçim Analizi

Determination of location selection criteria of electric vehicle charging stations with GIS and machine learning

Abstract

As the use of electric vehicles increases worldwide, the need for new charging station installations arises. The main objective of this study is to develop a suitable location selection model for electric vehicle charging stations, which are replacing vehicles with internal combustion engines nowadays. For this purpose, Munich, Germany, and Milan, Italy, have been selected as the study areas. The selection of these cities is based on the high number of electric vehicle charging stations and the similarity of these cities to Türkiye in terms of traffic and driving culture. Within the scope of the study, spatial analysis is performed with Geographic Information Systems (GIS), regression analysis is performed with Explainable Artificial Intelligence (XAI) techniques, and a model that can be used to determine the most suitable locations for charging stations is developed. The weights of the criteria are determined using the Random Forest method, and the model accuracy is observed to be 27% in Milan and 87% in Munich. As a result, the criteria that are effective for selecting the most suitable locations for electric vehicle charging stations have been determined with inferences based on the utilization data of existing stations. Thus, it aims to facilitate the transition from fossil fuel vehicles to electric vehicles by ensuring the efficient use of resources by directing investments in this field with a decision support system and providing suitable transportation infrastructure for electric vehicle owners.

Keywords

Explainable AI , Geographic Information Systems , Electric Vehicle Charging Station , Machine Learning , Site Selection Analysis

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