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Understanding Active Transportation to School Behavior in Socioeconomically Disadvantaged Communities: A Machine Learning and SHAP Analysis Approach

Author

Listed:
  • Bita Etaati

    (Big Data Analytics (BDA) Program, San Diego State University, San Diego, CA 92182, USA)

  • Arash Jahangiri

    (Department of Civil, Construction, and Environmental Engineering, San Diego State University, San Diego, CA 92182, USA)

  • Gabriela Fernandez

    (Department of Geography, San Diego State University, San Diego, CA 92182, USA)

  • Ming-Hsiang Tsou

    (Department of Geography, San Diego State University, San Diego, CA 92182, USA)

  • Sahar Ghanipoor Machiani

    (Department of Civil, Construction, and Environmental Engineering, San Diego State University, San Diego, CA 92182, USA)

Abstract

Active Transportation to School (ATS) offers numerous health benefits and is considered an affordable option, especially in disadvantaged neighborhoods. The US Centers for Disease Control and Prevention (CDC) advises 60 min of daily physical exercise for children aged 6 to 17, making ATS a compelling approach to promote a healthier lifestyle among students. Initiated in 2005 by the US Department of Transportation (DOT), the Safe Routes to School (SRTS) program aims to foster safe and regular walking and biking to school for students. This paper examines students’ travel behavior using SRTS survey data and assesses the program’s effectiveness in promoting ATS in Chula Vista, California. Employing machine learning algorithms (random forest, logistic regression, and support vector machines) to predict students’ likelihood to walk to school, it utilizes SHAP (SHapley Additive exPlanations) to pinpoint significant variables influencing ATS across all models. SHAP underscores critical factors affecting transportation choices to school, highlighting the importance of home-to-school distance, with shorter distances positively impacting active transportation. However, only half of students within schools’ walking distance opted to walk to school, underscoring the necessity of addressing parental safety concerns, including factors such as crime rates and traffic speed along the route.

Suggested Citation

  • Bita Etaati & Arash Jahangiri & Gabriela Fernandez & Ming-Hsiang Tsou & Sahar Ghanipoor Machiani, 2023. "Understanding Active Transportation to School Behavior in Socioeconomically Disadvantaged Communities: A Machine Learning and SHAP Analysis Approach," Sustainability, MDPI, vol. 16(1), pages 1-18, December.
    • Handle: RePEc:gam:jsusta:v:16:y:2023:i:1:p:48-:d:1303721
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    References listed on IDEAS

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