Understanding short-distance travel to school in Singapore: A data-driven approach

  • Francisco Benita (Corresponding author)
  • , Garvit Bansal
  • , Georgios Piliouras
  • , Bige Tunçer

Research output: Contribution to journalArticleAcademicpeer-review

6 Citations (Scopus)

Abstract

This study examines the school travel mode of children and youth students (ages 7 to 18) in Singapore. Using a large crowdsensing dataset the paper focuses on minute-by-minute decision-making of those students living within 2.5km of school. Data-driven methods are employed in order to identify students’ chosen transport mode (car, walking, taking bus or riding metro). Furthermore, we present attributes of travel mode alternatives computed by a replicable framework that utilises open sources. New algorithms are developed to identify proxies for walking access and public transport access. We found that about 19% of students in the sample live up to a distance of 2.5km from the school. From these, about 45% of trips are made by public transit (e.g., bus and metro), and only 13% are made by walking. The empirical results suggest that the public transport modes of bus and metro are not distinct. Consistent with past research based on traditional survey data, walking time and walking distance are the most influential factors in the decision to walk-to-school. Interestingly, schools’ connectivity to the street network is found to play a key role on the shift from public transport to walking. Likewise, departing at peak hours, the odds to choose public transport modes are about 40-45% lower as compared to walk.

Original languageEnglish
Pages (from-to)349-362
Number of pages14
JournalTravel Behaviour and Society
Volume31
DOIs
Publication statusPublished - Apr 2023
Externally publishedYes

Funding

The research leading to these results is supported by funding from the National Research Foundation, Prime Minister's Office, Singapore, under its Grant RGNRF1402. Georgios Piliouras gratefully acknowledges grant PIE-SGP-AI-2020-01, NRF2019-NRF-ANR095 ALIAS grant and NRF 2018 Fellowship NRF-NRFF2018-07. We are grateful for valuable improvement suggestions from insightful comments and suggestions from two anonymous reviewers.

Keywords

  • Active school travel
  • Children
  • Movement analysis
  • Singapore
  • Trajectory data

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