Achieving target equilibria in network routing games without knowing the latency functions

The analysis of network routing games typically assumes precise, detailed information about the latency functions. Such information may, however, be unavailable or difficult to obtain. Moreover, one is often primarily interested in enforcing a desired target flow as an equilibrium. We ask whether one can achieve target flows as equilibria without knowing the underlying latency functions. We give a crisp positive answer to this question. We show that one can efficiently compute edge tolls that induce a given target multicommodity flow in a nonatomic routing game using a polynomial number of queries to an oracle that takes tolls as input and outputs the resulting equilibrium flow. This result is obtained via a novel application of the ellipsoid method, and extends to various other settings. We obtain improved query-complexity bounds for series-parallel networks, and single-commodity routing games with linear latency functions. Our techniques provide new insights into network routing games

Achieving target equilibria in network routing games without knowing the latency functions

The analysis of network routing games typically assumes precise, detailed information about the latency functions. Such information may, however, be unavailable or difficult to obtain. Moreover, one is often primarily interested in enforcing a desired target flow as an equilibrium. We ask whether one can achieve target flows as equilibria without knowing the underlying latency functions. We give a crisp positive answer to this question. We show that one can efficiently compute edge tolls that induce a given target multicommodity flow in a nonatomic routing game using a polynomial number of queries to an oracle that takes tolls as input and outputs the resulting equilibrium flow. This result is obtained via a novel application of the ellipsoid method, and extends to various other settings. We obtain improved query-complexity bounds for series-parallel networks, and single-commodity routing games with linear latency functions. Our techniques provide new insights into network routing games

Selfishness need not be bad: a general proof

This article studies the user behavior in non-atomic congestion games. We consider non-atomic congestion games with continuous and non-decreasing functions and investigate the limit of the price of anarchy when the total user volume approaches infinity. We deepen the knowledge on {\em asymptotically well designed games} \cite{Wu2017Selfishness}, {\em limit games} \cite{Wu2017Selfishness}, {\em scalability} \cite{Wu2017Selfishness} and {\em gaugeability} \cite{Colini2017b} that were recently used in the limit analyses of the price of anarchy for non-atomic congestion games. We develop a unified framework and derive new techniques that allow a general limit analysis of the price of anarchy. With these new techniques, we are able to prove a global convergence on the price of anarchy for non-atomic congestion games with arbitrary polynomial price functions and arbitrary user volume vector sequences. Moreover, we show that these new techniques are very flexible and robust and apply also to non-atomic congestion games with price functions of other types. In particular, we prove that non-atomic congestion games with regularly varying price functions are also asymptotically well designed, provided that the price functions are slightly restricted. Our results greatly generalize recent results. In particular, our results further support the view with a general proof that selfishness need not be bad for non-atomic congestion games.Comment: 68 pages, 2 figure

Smart Traffic Operation: from Human-Driven Cars to Mixed Vehicle Autonomy

The goal of my research is to enhance urban mobility by developing reliable and efficient traffic control and management strategies. As cities grow everywhere, and urban roadways become overburdened, the need for the development of such strategies becomes more evident. With the prevalence of smart sensing devices, such as smart phones and smart intersections, cities are becoming smart. Moreover, with the emergence of new and inevitable technologies, such as autonomous and connected vehicles, electric vehicles, and mobility on demand systems, smart cities are rapidly evolving. As we experience the arrival of such technologies, there is an opportunity to reclaim urban mobility. However, a blind utilization of these technologies may deflect us from reaching this goal. In this dissertation, we study the efficient operation of smart cities via management strategies that can guarantee overall societal benefits both in the cities of today and future.We focus on two natural instances of this agenda. In the first part, we tackle some of the existing challenges in the smart operation of traffic networks which are solely shared by human-driven cars. If all vehicles are human-driven, there is room for improving the efficiency of traffic networks by appropriate coordination and control of traffic signal lights. For these networks, we develop signal control algorithms that are capable of minimizing the number of stop-and-go movements, encoding fairness among vehicular arrivals, and are robust to the knowledge of system parameters. In the second part, we analyze fundamentals of traffic networks with mixed vehicle autonomy, where both human-driven and autonomous cars coexist on roadways. We study the mobility implications of selfish autonomy, i.e. autonomous cars that are not concerned about their overall impact and simply attempt to optimize their own travel benefits. Having shown the negative consequences that the increased deployment of selfish autonomy may have, we develop a pricing mechanism which can guarantee the overall societal-scale efficiency of traffic networks with mixed vehicle autonomy. Finally, we show how autonomy can act altruistically, i.e. by taking into account the decision making process of humans, autonomous cars can potentially plan for their actions in the favor of the overall good