A COMPARATIVE ANALYSIS OF THE FACTORS INFLUENCING UNIVERSITY STUDENTS' MICRO-MOBILITY PREFERENCES USING K-NEAREST NEIGHBORS AND LOGISTIC REGRESSION MODELS DOI Creative Commons
Mahmut Esad Ergin

İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 6, 2024

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

Language: Английский

The Problems of Scooter-Sharing in Smart Cities Based on the Example of the Silesian Region in Poland DOI Creative Commons
Radosław Wolniak, Katarzyna Turoń

Smart Cities, Journal Year: 2025, Volume and Issue: 8(1), P. 16 - 16

Published: Jan. 21, 2025

The rapid urbanization and pursuit of sustainability have elevated shared mobility as a cornerstone smart cities. Among its modalities, scooter-sharing has gained popularity for convenience eco-friendliness, yet it faces significant adoption barriers. This study investigates the challenges to systems within cities, focusing on Silesian region Poland case study. It aims identify region-specific barriers opportunities in Central Eastern Europe provide insights into long-term development trends potential challenges. Using comprehensive statistical methods, including factor analysis regression models, this identifies key such insufficient bike paths, poor path conditions, inadequate signage, fleet maintenance issues, complex rental processes. External factors like adverse weather heavy traffic, coupled with health safety concerns, further hinder adoption, particularly among vulnerable populations. Additionally, explores future scooter-sharing, emphasizing role advanced technologies, adaptive urban planning, sustainable management ensuring feasibility. Drawing global studies, underscores need tailored infrastructural investments, management, user-centric policies align city goals sustainability, accessibility, improved mobility. These findings offer actionable policymakers service providers striving integrate evolving landscape

Language: Английский

Citations

2
A discrete choice analysis of user preferences in micromobility transportation DOI Creative Commons
Pires Abdullah,

Shakir Ullah,

Domokos Esztergár-Kiss

et al.

European Transport Research Review, Journal Year: 2025, Volume and Issue: 17(1)

Published: April 30, 2025

Language: Английский

Citations

0
A COMPARATIVE ANALYSIS OF THE FACTORS INFLUENCING UNIVERSITY STUDENTS' MICRO-MOBILITY PREFERENCES USING K-NEAREST NEIGHBORS AND LOGISTIC REGRESSION MODELS DOI Creative Commons
Mahmut Esad Ergin

İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 6, 2024

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

Language: Английский

Citations

0