A COMPARATIVE ANALYSIS OF THE FACTORS INFLUENCING UNIVERSITY STUDENTS' MICROMOBILITY PREFERENCES USING K-NEAREST NEIGHBORS AND LOGISTIC REGRESSION MODELS

Öz

Shared micro-mobility services have swiftly become widely adopted in major urban centers globally. In particular, individuals are encouraged to transition to environmentally friendly modes of transportation to support a sustainable transportation system. For this reason, the tendencies and potential of individuals to use micro-mobility vehicles are being investigated. This paper focused on university students, analyzing their preferences for using micromobility vehicles, particularly for first-mile or last-mile trips in terms of gender and travel time variables. In the study, k-Nearest Neighbors (kNN) and Logistic Regression (LR) algorithms are used in machine learning approach and they were compared. A face-to-face survey was conducted with 150 students randomly to measure the potential use of micromobility vehicles among university students. As a result, LR model is better than kNN model according to the accuracy of the models, 0,63 and 0,43 respectively. On the other hand, 51,82% of male students and 62,50% of female students participating in our study reported that they are not inclined to prefer micromobility vehicles at any stage of their trips, and the main challenge for the potential users is safety.

Anahtar Kelimeler

• Gender
• k-nearest neighbors
• logistic regression
• machine learning
• micro-mobility

K- EN YAKIN KOMŞULAR VE LOJİSTİK REGRESYON MODELLERİ KULLANILARAK ÜNİVERSİTE ÖĞRENCİLERİNİN MİKRO-MOBİLİTE TERCİHLERİNİ ETKİLEYEN FAKTÖRLERİN KARŞILAŞTIRMALI ANALİZİ

Öz

Paylaşımlı mikro-mobilite hizmetleri, dünya genelinde özellikle büyük şehirlerde hızla benimsenmiştir. Son zamanlarda, bireylerin sürdürülebilir bir ulaşım sistemini desteklemek amacıyla çevre dostu ulaşım modlarına geçiş yapmaları teşvik edilmektedir. Bu nedenle, literatürde, yol kullanıcılarının mikro-mobilite araçlarını kullanma eğilimleri ve potansiyelleri araştırılmaktadır. Bu çalışma, üniversite öğrencilerini hedef alarak, cinsiyet ve yolculuk süresi değişkenleri açısından ilk ve son kilometre (ilk ve son adım) yolculukları için mikro-mobilite araçlarını kullanma eğilimlerini analiz etmektedir. Çalışmada, makine öğrenmesi yaklaşımıyla k-En Yakın Komşu (kNN) ve Lojistik Regresyon (LR) algoritmaları kullanılmış ve karşılaştırılmıştır. Üniversite öğrencileri arasında mikro-mobilite araçlarının potansiyel kullanımını ölçmek amacıyla 150 öğrenciyle yüz yüze anket yapılmıştır. Sonuç olarak, LR modelinin doğruluk açısından kNN modelinden (sırasıyla 0,63 ve 0,43) daha iyi olduğu görülmüştür. Öte yandan, çalışmamıza katılan erkek öğrencilerin %51,82'si ve kadın öğrencilerin %62,50'si, yolculuklarının herhangi bir aşamasında mikro-mobilite araçlarını tercih etme eğiliminde olmadıklarını belirtmiş, potansiyel kullanıcılar için ana zorluğun “güvenlik” kriteri olduğu sonucuna ulaşılmıştır.

Anahtar Kelimeler

• Cinsiyet
• k-en yakın komşu
• lojistik regresyon
• makine öğrenmesi
• mikro-mobilite

Kaynakça

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