Author ORCID Identifier
0000-0002-8246-2115
Document Type
Conference Paper
Disciplines
Statistics
Abstract
Sustainable mobility stands at the forefront of contemporary discussions, driven by the clear imperative to transition towards more environmentally friendly transportation and patterns. This shift is widely recognized as a crucial opportunity to address the challenges and inherent dangers posed by climate change. It is then crucial to introduce attitudes to encourage voluntary behavioral changes toward different sustainable solutions. In this perspective, to foster a future where sustainable personal mobility options are widely embraced and integrated, it is crucial to comprehend the inclination of younger generations to use them. For this reason, a survey on the use of sustainable mobility tools by young generations was recently conducted by the authors on the population of individuals aged 18 to 35 who live in the metropolitan area of Rome. The survey was multipurpose. Here, we focus on the propensity of young people to commute to work or study places on foot, exclusively or in combination with public transport tools. The attitude to move as a pedestrian, one of the most vulnerable road users, is modeled by adopting the Technology Acceptance Model (TAM). The model is enriched by considering the possible effect of the perceived quality of the area where one lives, treated as a proxy of the economic and social well-being of the respondents. Model relationships are estimated according to the Structural Equation Model perspective. Finally, whether the choice to reach the working/studying place with public transport and/or by foot varies according to some contextual variables such as living-to-work/study distance, and urban (Rome) versus suburban residence is evaluated.
DOI
https://doi.org/10.21427/fngr-x695
Recommended Citation
Balzano, Simona; Demni,, Houyem; Pascucci,, Edoardo; Natale, Luisa; Cappelli, Giuseppe; Nardoianni, Sofia; and Porzio, Giovanni C., "Intention to Commute by Public Transportation and/or by Foot: Findings from a PLS Structural Equation Model" (2025). SAML-25 Workshop on Statistical and Machine Learning. 8.
https://arrow.tudublin.ie/saml/8
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.
Publication Details
Statistical and Machine Learning: Methods and Applications (SAML-25) on June 5th and 6th, 2025 at TU Dublin, Ireland.