  Traffic congestion has large economic and social costs. The introduction of
autonomous vehicles can potentially reduce this congestion by increasing road
capacity via vehicle platooning and by creating an avenue for influencing
people's choice of routes. We consider a network of parallel roads with two
modes of transportation: (i) human drivers, who will choose the quickest route
available to them, and (ii) a ride hailing service, which provides an array of
autonomous vehicle route options, each with different prices, to users. We
formalize a model of vehicle flow in mixed autonomy and a model of how
autonomous service users make choices between routes with different prices and
latencies. Developing an algorithm to learn the preferences of the users, we
formulate a planning optimization that chooses prices to maximize a social
objective. We demonstrate the benefit of the proposed scheme by comparing the
results to theoretical benchmarks which we show can be efficiently calculated.
