Understanding the behaviour of students in choosing a transportation mode to school is crucial to promote Active Commuting to School (ACS) and the adoption of healthier lifestyles. Therefore, analysing all types of transportation modes with multiple factors/features is essential, though it can be a challenge in statistical modelling. The main objective of the present study was to determine the factors that contribute to the choice of a particular mode in school transportation, by using Machine Learning (ML) algorithms: Extreme Gradient Boosting (XGB), Random Forest (RF), Decision Tree (DT) and Multinomial Logistic Regression (MNL). Data from the ‘Hands Up’ Survey in Lisbon, Portugal, between 2018 and 2021, with 10 different modes of transportation were analysed. A range of factors including safety around school, socioeconomic status of schools’ parishes, school regime, school grades and the proximity of schools to the different public transportation modes were considered. The algorithms have been compared in terms of accuracy scores. The XGB algorithm shows the best performance (64 % accuracy and 0.33 Macro F1) for multi-class classification, while RT, DT and MNL provide accuracy of 40 %, 37 % and 47 % respectively. Weighted Average Feature Importance (WAFI) have been determined for all variables. For the best-performing algorithm, the XGB, the combination factor of school regime and school grade is the most relevant factor, contributing to around 21.2 % for multi-class classification. WAFI scores for each variable suggest that the proximity of schools to various public transports is an important factor contributing more than 50 % for the predominance of private car in school transportation.

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Active commuting to school,School transportation mode choice,Hands up survey,Machine learning classification

• Civil Engineering - Engineering and Technology
• Sociology - Social Sciences
• Social and Economic Geography - Social Sciences

This publication is an output of the following project(s):

• Candidates Survey (MPs and non-elected candidates) for the 2019 Legislative Elections
• Candidates Survey (MPs and non-elected candidates) for the 2022 Legislative Elections
• Candidates Survey (MPs and non-elected candidates) for the 2024 Legislative Elections