Bounding the Inefficiency of Route Control in Intelligent Transport Systems

Charlotte Roman, Paolo Turrini

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.

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