학술논문
Understanding Active Transportation to School Behavior in Socioeconomically Disadvantaged Communities: A Machine Learning and SHAP Analysis Approach
Document Type
Academic Journal
Author
Source
Sustainability. December 2023, Vol. 16 Issue 1
Subject
Language
English
ISSN
2071-1050
Abstract
Author(s): Bita Etaati [1]; Arash Jahangiri (corresponding author) [2,*]; Gabriela Fernandez [3]; Ming-Hsiang Tsou [3]; Sahar Ghanipoor Machiani [2] 1. Introduction Engaging in physical activity during adolescence offers a range [...]
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.
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.