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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Stylianos Kolidakis & Kornilia Maria Kotoula & George Botzoris & Petros Fotios Kamberi & Dimitrios Skoutas, 2024. "Assessing Impact Factors That Affect School Mobility Utilizing a Machine Learning Approach," Sustainability, MDPI, vol. 16(2), pages 1-31, January.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:2:p:588-:d:1316039
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    References listed on IDEAS

    as
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    2. Rachel Copperman & Chandra Bhat, 2007. "An analysis of the determinants of children’s weekend physical activity participation," Transportation, Springer, vol. 34(1), pages 67-87, January.
    3. Ho Yeung Lam & Sisitha Jayasinghe & Kiran D. K. Ahuja & Andrew P. Hills, 2023. "Active School Commuting in School Children: A Narrative Review of Current Evidence and Future Research Implications," IJERPH, MDPI, vol. 20(20), pages 1-20, October.
    4. Md Rokibul Islam & Md Hadiuzzaman & Rajib Banik & Md Mehedi Hasnat & Sarder Rafee Musabbir & Sanjana Hossain, 2016. "Bus service quality prediction and attribute ranking: a neural network approach," Public Transport, Springer, vol. 8(2), pages 295-313, September.
    5. Kolidakis, Stylianos & Botzoris, George & Profillidis, Vassilios & Lemonakis, Panagiotis, 2019. "Road traffic forecasting — A hybrid approach combining Artificial Neural Network with Singular Spectrum Analysis," Economic Analysis and Policy, Elsevier, vol. 64(C), pages 159-171.
    6. Carver, Alison & Timperio, Anna & Crawford, David, 2013. "Parental chauffeurs: what drives their transport choice?," Journal of Transport Geography, Elsevier, vol. 26(C), pages 72-77.
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