  With the forecasted emergence of autonomous vehicles in urban traffic
networks, new control policies are needed to leverage their potential for
reducing congestion. While several efforts have studied the fully autonomous
traffic control problem, there is a lack of models addressing the more imminent
transitional stage wherein legacy and autonomous vehicles share the urban
infrastructure. We address this gap by introducing a new policy for stochastic
network traffic control involving both classes of vehicles. We conjecture that
network links will have dedicated lanes for autonomous vehicles which provide
access to traffic intersections and combine traditional green signal phases
with autonomous vehicle-restricted signal phases named blue phases. We propose
a new pressure-based, decentralized, hybrid network control policy that
activates selected movements at intersections based on the solution of
mixed-integer linear programs. We prove that the proposed policy is stable,
i.e. maximizes network throughput, under conventional travel demand conditions.
We conduct numerical experiments to test the proposed policy under varying
proportions of autonomous vehicles. Our experiments reveal that considerable
trade-offs exist in terms of vehicle-class travel time based on the level of
market penetration of autonomous vehicles. Further, we find that the proposed
hybrid network control policy improves on traditional green phase traffic
signal control for high levels of congestion, thus helping in quantifying the
potential benefits of autonomous vehicles in urban networks.
