PREDICTIVE MODELING IN ELECTROMOBILITY: A TIME SERIES ANALYSIS

Rana Bedir Urfalı; Ersin Kaya

doi:10.17780/ksujes.1817646

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ELEKTROMOBİLİTEDE TAHMİNE DAYALI MODELLEME: BİR ZAMAN SERİSİ ANALİZİ

Öz

Bu çalışma, gelecekteki eğilimleri tahmin etmek amacıyla, bir ildeki elektrikli bisiklet kullanımını zaman serisi modelleri ile analiz etmektedir. Sürdürülebilir ulaşımın kritik bir parçası olan elektrikli bisikletler, kentsel hareketliliği ve talep tahminini iyileştirir; bu da elektrikli bisiklet paylaşım sistemleri yönetimi için hayati önem taşır. Tahminler ilk olarak yalnızca bisiklet kullanım verileriyle tek değişkenli modeller (ARIMA, Prophet, SSA) kullanılarak yapıldı. Ardından, sıcaklık, yağış ve rüzgar hızı gibi dış etkenleri dahil eden çok değişkenli yaklaşımlara (SARIMAX, Multivariate Prophet, MSSA) geçildi. Bu sayede veri seti, çevresel etkileri yakalamak için meteorolojik değişkenlerle birleştirildi. Model performansı RMSE ve MAE metrikleriyle değerlendirildi. Sonuçlar, doğrulukta bölgesel farklılıklar olduğunu gösterdi: ARIMA, tek değişkenli modeller arasında dört bölgede en iyisiydi, ancak SARIMAX ve Multivariate Prophet çoğu bölgede üstün tahminler üretti. Ayrıca MSSA, dış etkileri dahil etmenin faydasını vurgulayarak 13 bölgede SSA'yı sürekli olarak geride bıraktı. Genel bulgular, hava durumu verilerini entegre etmenin tahmin hassasiyetini artırdığını ve daha iyi operasyonel stratejileri desteklediğini ortaya koymaktadır. Bu yaklaşım, elektrikli bisiklet paylaşım hizmetlerini ve şehir içi ulaşım planlamasını optimize ederek daha verimli kaynak kullanımına ve yüksek kullanıcı memnuniyetine katkıda bulunmaktadır.

Anahtar Kelimeler

PREDICTIVE MODELING IN ELECTROMOBILITY: A TIME SERIES ANALYSIS

Abstract

This study analyzes electric bicycle usage in a province with time series models to forecast future trends. Electric bicycles, as an essential element of sustainable transportation, enhance urban mobility and reduce traffic congestion. Accurate demand forecasting is crucial for the operational planning and administration of e-bike sharing systems. In this study, forecasts were first made using only bicycle usage data with univariate time series models (ARIMA, Prophet, SSA). Next, multivariate approaches (SARIMAX, Multivariate Prophet, MSSA) incorporated external factors such as temperature, precipitation, and wind speed. The dataset, therefore, combined bicycle usage records with meteorological variables to capture environmental impacts on demand. Model performance was assessed using RMSE and MAE metrics. Results showed regional variations in accuracy: ARIMA performed best among univariate models in four regions, while SARIMAX and Multivariate Prophet produced superior forecasts in most regions. Furthermore, MSSA consistently outperformed SSA in 13 regions, highlighting the benefit of including external influences. Overall, the findings demonstrate that integrating weather data improves forecast precision and supports better operational strategies. This contributes to optimizing e-bike sharing services and urban transport planning, enabling more efficient resource use and greater user satisfaction.

Keywords

References

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Details

Primary Language

English

Subjects

Artificial Intelligence (Other)

Journal Section

Research Article

Authors

Rana Bedir Urfalı ^*
0009-0003-9495-2482
Türkiye

Ersin Kaya
0000-0001-5668-5078
Türkiye

Publication Date

March 3, 2026

Submission Date

November 4, 2025

Acceptance Date

February 5, 2026

Published in Issue

Year 2026 Volume: 29 Number: 1

DOI

https://doi.org/10.17780/ksujes.1817646

IZ

https://izlik.org/JA29UL67FG

Cite

RIS / Bibtex

APA

Bedir Urfalı, R., & Kaya, E. (2026). PREDICTIVE MODELING IN ELECTROMOBILITY: A TIME SERIES ANALYSIS. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 29(1), 295-314. https://doi.org/10.17780/ksujes.1817646