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Journals / Akıllı Ulaşım Sistemleri ve Uygulamaları Dergisi / 2026 / Cilt: 9 Sayı: 2

Predicting Electric Vehicle Ranges Based on Cities Using Machine Learning Algorithms

Makine Öğrenmesi Algoritmaları Kullanarak Şehirlere Göre Elektrikli Araç Menzillerinin Tahmin Edilmesi

Tuğce Altuntaş(ÇANAKKALE ONSEKİZ MART ÜNİVERSİTESİ)0009-0007-3629-2454
Pages
388–404
Publication Date
2026-10-27
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Ultimately, these results demonstrate that systematically integrating urban-level data substantially enhances the precision of range predictions, offering a practical solution to alleviate range anxiety, guide consumer expectations, and equip urban planners and policymakers with a reliable, data-backed foundation for optimizing charging networks and advancing sustainable electric mobility.

📌 3 Key Takeaways

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Objective & Hypothesis:This study introduces a novel, data-driven framework for predicting electric vehicle (EV) range by incorporating city-level geographic and infrastructural variables alongside advanced machine learning (ML) algorithms, thereby addressing the key limitations of traditional models that focus solely on internal vehicle metrics.
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Key Findings & Data:Using a comprehensive dataset of more than 65,000 real-world vehicles across the United States, the research highlights that urban context—including climatic factors, traffic flow, and regional infrastructure—is a crucial determinant of EV energy consumption.
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Conclusion & Impact:Ultimately, these results demonstrate that systematically integrating urban-level data substantially enhances the precision of range predictions, offering a practical solution to alleviate range anxiety, guide consumer expectations, and equip urban planners and policymakers with a reliable, data-backed foundation for optimizing charging networks and advancing sustainable electric mobility.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:şarj edilebilir hibrit · elektirikli araç · makine öğrenmesi

Abstract

This study introduces a novel, data-driven framework for predicting electric vehicle (EV) range by incorporating city-level geographic and infrastructural variables alongside advanced machine learning (ML) algorithms, thereby addressing the key limitations of traditional models that focus solely on internal vehicle metrics. Using a comprehensive dataset of more than 65,000 real-world vehicles across the United States, the research highlights that urban context—including climatic factors, traffic flow, and regional infrastructure—is a crucial determinant of EV energy consumption. A rigorous performance evaluation comparing Random Forest (RF), Gradient Boosting (GB), and Neural Networks (NN) identifies RF as the most effective approach, delivering exceptional predictive accuracy with an R2 of 0.994 and an RMSE of 7.387 miles, significantly outperforming existing models in the literature. Ultimately, these results demonstrate that systematically integrating urban-level data substantially enhances the precision of range predictions, offering a practical solution to alleviate range anxiety, guide consumer expectations, and equip urban planners and policymakers with a reliable, data-backed foundation for optimizing charging networks and advancing sustainable electric mobility.

Özet

Bu çalışma, yalnızca araç içi verilere odaklanan geleneksel modellerin temel kısıtlamalarını ele alarak; gelişmiş makine öğrenimi (ML) algoritmalarının yanı sıra şehir ölçeğindeki coğrafi ve altyapısal değişkenleri de dahil eden, veri odaklı ve yenilikçi bir çerçeve sunmaktadır. Amerika Birleşik Devletleri genelinde 65.000'den fazla gerçek araçtan elde edilen kapsamlı bir veri setini kullanan araştırma; iklimsel faktörler, trafik akışı ve bölgesel altyapı gibi kentsel bağlam unsurlarının, elektrikli araç (EV) enerji tüketiminde belirleyici bir rol oynadığını ortaya koymaktadır. Random Forest (RF), Gradient Boosting (GB) ve Sinir Ağları (NN) yöntemlerinin karşılaştırıldığı titiz bir performans değerlendirmesi sonucunda RF'nin en etkili yaklaşım olduğu tespit edilmiştir; bu yöntem, 0,994'lük R² değeri ve 7,387 millik RMSE değeri ile olağanüstü bir tahmin doğruluğu sağlayarak literatürdeki mevcut modelleri önemli ölçüde geride bırakmıştır. Nihayetinde bu sonuçlar, şehir ölçeğindeki verilerin sistematik bir şekilde entegre edilmesinin menzil tahminlerinin hassasiyetini büyük ölçüde artırdığını göstermektedir; böylece söz konusu yaklaşım; menzil kaygısını hafifletmek, tüketici beklentilerine yön vermek ve şehir plancıları ile politika yapıcıları şarj ağlarının optimize edilmesi ve sürdürülebilir elektrikli mobilitenin geliştirilmesi konularında güvenilir, veri destekli bir temel ile donatmak adına pratik bir çözüm sunmaktadır.

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Cite This Article

Altuntaş, T. (2026). Predicting Electric Vehicle Ranges Based on Cities Using Machine Learning Algorithms. Akıllı Ulaşım Sistemleri ve Uygulamaları Dergisi, Cilt: 9 Sayı: 2, 388–404. https://doi.org/10.51513/jitsa.2023111

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