In 2017, the Boston Public Schools organised a Transportation Challenge seeking an innovative solution to optimise their bus routes. The Quantum Team from the MIT Operations Research Center won the challenge with an algorithm that utilised Google Maps data and Boston Public Schools data. This algorithm successfully minimised bus routes, reconfigured bus stops, maximised student ridership, and reduced the time empty buses spent on the road (Boston Public Schools, 2017). AI-powered transportation management systems claim to be able to analyse various factors, such as student locations, traffic conditions, road closures, and weather data, to create efficient bus routes and schedules (e.g. AlphaRoute, 2023). These systems dynamically adjust routes in real-time, reducing travel times, minimising fuel consumption, and enhancing overall transportation efficiency.
However, the success of the algorithm led to an unforeseen turn of events. The same system that initially focused on optimising bus routes was later employed to optimise school start times. This decision, approved by the Boston School Committee Office of Equity, entailed significant changes to start times, causing disruptions to family routines, sleep patterns, and before/after school childcare for parents (Ito, 2018).
