Solving N-player dynamic routing games with congestion: a mean field approach

Abstract

The recent emergence of navigational tools has changed traffic patterns and has now enabled new types of congestion-aware routing control like dynamic road pricing. Using the fundamental diagram of traffic flows -applied in macroscopic and mesoscopic traffic modeling -the article introduces a new N -player dynamic routing game with explicit congestion dynamics. The model is well-posed and can reproduce heterogeneous departure times and congestion spill back phenomena. However, as Nash equilibrium computations are PPAD-complete, solving the game becomes intractable for large but realistic numbers of vehicles N . Therefore, the corresponding mean field game is also introduced. Experiments were performed on several classical benchmark networks of the traffic community: the Pigou, Braess, and Sioux Falls networks with heterogeneous origin, destination and departure time tuples. The Pigou and the Braess examples reveal that the mean field approximation is generally very accurate and computationally efficient as soon as the number of vehicles exceeds a few dozen. On the Sioux Falls network (76 links, 100 time steps), this approach enables learning traffic dynamics with more than 14,000 vehicles.

Developing, evaluating and scaling learning agents in multi-agent environments

“…We introduced and studied the convergence to a Nash equilibrium of several scalable learning algorithms for Mean Field Games, together with more specific applications to flocking [24] or vehicles traffic routing management [25]. Our algorithms rely on Fictitious Play [26,27] or Online Mirror Descent algorithms [28] and can be efficiently combined with Deep Reinforcement Learning [29].…”