Linearly-Solvable Mean-Field Approximation for Multi-Team Road Traffic Games

Abstract

We study the traffic routing game among a large number of selfish drivers over a traffic network. We consider a specific scenario where the strategic drivers can be classified into teams, where drivers in the same team have identical payoff functions. An incentive mechanism is considered to mitigate congestion, where each driver is subject to dynamic tax penalties. We explore a special case in which the tax is affine in the logarithm of the number of drivers selecting the same route from each team. It is shown via a mean-field approximation that a Nash equilibrium in the limit of a large population can be found by linearly solvable algorithms.

Linearly Solvable Mean-Field Traffic Routing Games

“…Linear solvability renders the proposed MFG framework attractive as an incentive mechanism for TSOs for the purpose of traffic congestion mitigation; however, questions from the perspectives of mechanism design theory, such as how to tune parameters α and R (which are assumed given in this paper) to balance the efficiency and budget, are unexplored. Finally, generalization to nonhomogeneous MFGs with multiple classes of players (which was recently studied in [49]) needs further investigation.…”