A first step to optimizing fleets of autonomous vehicles is to think of shared transit as the primary use case and business case for AV fleets. Traffic gurus at MIT developed an algorithm and found that 2,000 10-person vehicles could handle 95% of New York’s 14,000 cabs.

Adopting a shared model for most or all AVs should drive vehicle design (larger, multipassenger vehicles) and feature offers like Wi-Fi and entertainment to make slightly longer journeys more attractive than driving due to the opportunity to get things done during the trip.

Even without algorithmic optimization, mobility services could be more efficient incentivizing and rewarding the smart routing of vehicles (forcing them to take specific routes) and altered travel times to get people to book rides at off-peak times, according to Carnegie Mellon engineering professor Sean Qian. He studied today’s ride-hailing data, but there is no reason these kinds of rules could not be baked into AV booking apps, too.
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