But in Boston, automation may offer a route to solving some of these problems. Not in the form of self-driving vehicles, but in the use of machine-learning to improve school bus administration.

In 2017, Boston Public Schools announced a competition – the BPS Transportation Challenge – to find smart ways to improve school bus operations and bring down their $120 million annual cost.

The winner was the Quantum Team from the MIT Operations Research Center. The team developed an algorithm to identify the most efficient and cost-effective routes for BPS’s fleet of 650 buses. And last year, the algorithm got to work contending with multiple variables to reconfigure routes…

Annual costs have been cut by around $5 million, 50 underutilized routes have been scrapped, and the number of students per bus on the remaining services has increased.
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