Assessing Impact Factors That Affect School Mobility Utilizing a Machine Learning Approach

Stylianos Kolidakis (), Kornilia Maria Kotoula, George Botzoris, Petros Fotios Kamberi and Dimitrios Skoutas
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Stylianos Kolidakis: Athena Research and Innovation Center, Information Management Systems Institute, Artemidos 6 and Epidavrou, 15125 Marousi, Greece
Kornilia Maria Kotoula: Centre for Research and Technology Hellas, Hellenic Institute of Transport, 6th Km Harilaou–Thermi, 57001 Thessaloniki, Greece
George Botzoris: Department of Civil Engineering, Section of Transportation, Kimmeria Campus, Democritus University of Thrace, 67100 Xanthi, Greece
Petros Fotios Kamberi: Athena Research and Innovation Center, Information Management Systems Institute, Artemidos 6 and Epidavrou, 15125 Marousi, Greece
Dimitrios Skoutas: Athena Research and Innovation Center, Information Management Systems Institute, Artemidos 6 and Epidavrou, 15125 Marousi, Greece

Sustainability, 2024, vol. 16, issue 2, 1-31

Abstract: The analysis and modeling of parameters influencing parents’ decisions regarding school travel mode choice have perennially been a subject of interest. Concurrently, the evolution of artificial intelligence (AI) can effectively contribute to generating reliable predictions across various topics. This paper begins with a comprehensive literature review on classical models for predicting school travel mode choice, as well as the diverse applications of AI methods, with a particular focus on transportation. Building upon a published questionnaire survey in the city of Thessaloniki (Greece) and the conducted analysis and exploration of factors shaping the parental framework for school travel mode choice, this study takes a step further: the authors evaluate and propose a machine learning (ML) classification model, utilizing the pre-recorded parental perceptions, beliefs, and attitudes as inputs to predict the choice between motorized or non-motorized school travel. The impact of potential changes in the input values of the ML classification model is also assessed. Therefore, the enhancement of the sense of safety and security in the school route, the adoption of a more active lifestyle by parents, the widening of acceptance of public transportation, etc., are simulated and the impact on the parental choice ratio between non-motorized and motorized school commuting is quantified.

Keywords: machine learning; artificial intelligence; mode choice forecast; school transportation; sustainable mobility; school travel mode choice modeling (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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