ELEKTRİKLİ SCOOTER (E-SCOOTER) SÜRÜCÜLERİNİN SÜRÜŞ SÜRESİNİN MAKİNE ÖĞRENMESİ İLE TAHMİNİ

Abstract

Bu çalışma, elektrikli scooter araçlarını tercih eden sürücülerin sürüş sürelerinin tahmin edilmesini amaçlamaktadır. E-scooter'lar genel olarak daha küçük boyutları ve manevra kabiliyetleri sayesinde şehir içi yolculuklarda hızlı ilerleme sağlayabildikleri için trafik sıkışıklığından kaynaklanan zaman kaybını azaltmaktadır. E-scooter'lar daha kompakt yapıları sayesinde park yeri bulma ve kolay park etme konusunda da avantaj sağlıyor. Bu çalışmada e-scooter araçlarını tercih eden sürücülerin sürüş sürelerinin tahmin edilmesi amacıyla ML algoritmaları kullanılmıştır. AB modeli, düşük Ortalama Kareler Hata (MSE) değeriyle (0,005) iyi performans gösterdi. Ortalama Karekök Hata (RMSE) ve Ortalama Mutlak Hata (MAE) değerleri de nispeten düşüktür (sırasıyla 0,069 ve 0,039), bu da modelin tahminlerinin gerçek değerlere yakın olduğunu göstermektedir.Ayrıca R-kare Belirleme Katsayısı (R2) değerinin (0,947) yüksek olması, bu modelin verileri oldukça iyi açıkladığını ve tahminlerinin gerçek değerlere yüksek doğrulukla yaklaştığını göstermektedir.Öte yandan GB algoritması, yüksek hata payı ve düşük doğruluk oranıyla farklı algoritmalara göre zayıf performans gösterdi. Bu sonuçlar, sürücünün e-scooter ile yapacağı yolculuk süresini tahmin ederek zaman yönetiminde avantaj sağlıyor. Sonuç olarak e-scooter'lar sürücülere zamandan tasarruf etme ve günlük hareketliliklerini daha etkin yönetme fırsatı sunarak bu araçları ulaşım açısından cazip hale getiriyor.

Keywords

• E-scooter
• zaman yönetimi
• makine öğrenimi
• tahmin

Ethical Statement

Dergimizin belirlediği etik kurul şartları incelenmiş olup, makale içeriğinde herhangi bir etik onayı gerektiren veri ve yöntem kullanılmadığını beyan ederim.

PREDICTION OF DRIVING TIME OF ELECTRIC SCOOTER (E-SCOOTER) DRIVERS BY MACHINE LEARNING

Abstract

This study aims to estimate the driving times of drivers who prefer electric scooter vehicles. In general, e-scooters reduce the loss of time caused by traffic jams because, thanks to their smaller size and maneuverability, these vehicles provide rapid progress in urban journeys. E-scooters also offer an advantage in finding a parking space and easy parking thanks to their more compact structure. In this study, ML algorithms were used to predict the driving times of drivers who prefer e-scooter vehicles. The AB model has performed well with a low Mean Square Error (MSE) value (0.005). The Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) values are also relatively low (0.069 and 0.039, respectively), indicating that the model's predictions are close to the actual values. Also, the high R-squared-Coefficient of Determination (R2) value (0.947) suggests that this model explains the data quite well, and its predictions approach the actual values with high accuracy. On the other hand, the GB algorithm performed poorly compared to different algorithms, with its high margin of error and low accuracy rate. These results provide an advantage in time management by estimating the travel time a driver will make with the e-scooter. As a result, e-scooters offer drivers the opportunity to save time and manage their daily mobility more effectively, driving these vehicles attractive for transportation.

Keywords

• E-scooter
• time management
• machine learning
• prediction

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