School streets implementation: A machine learning perspective

This study explores the application of generative adversarial neural networks in the implementation of school street closures, a concept aimed at improving safety and reducing traffic around schools. The research analyses various school street closure programs worldwide and identifies common challenges. Based on an analysis of 51 successful school street closure programs and a proposed urban feature extraction method, a supervised machine learning model was developed to facilitate the selection of the potential school street candidates. The developed tool aims to streamline the selection process and make the design more effective and context-sensitive. Despite certain limitations, such as the inability to represent all spatial contexts and small-scale urban details, the system can propose a meaningful definition of a school street closure zone. This research contributes to the limited academic literature on school streets and play streets, providing a new perspective on urban traffic regulation.