  Route controlled autonomous vehicles could have a significant impact in
reducing congestion in the future. Before applying multi-agent reinforcement
learning algorithms to route control, we can model the system using a
congestion game to predict and mitigate potential issues. We consider the
problem of distributed operating systems in a transportation network that
control the routing choices of their assigned vehicles. We formulate an
associated network control game, consisting of multiple actors seeking to
optimise the social welfare of their assigned subpopulations in an underlying
nonatomic congestion game. Then we find the inefficiency of the routing
equilibria by calculating the Price of Anarchy for polynomial cost functions.
Finally, we extend the analysis to allow vehicles to choose their operating
system.
