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Multi-Agent Route Choice as a Mean-Field Game


M. Albert

Semester Thesis, FS15 (10440)

The multi-agent route choice problem is formulated in a mean-field game (MFG) setting and corresponding control strategies for decentralized convergence to mean-field Nash equilibria are investigated. Recent results in the domain of constrained mean-field control provide theoretical guarantees for affine traffic models. Convergence and performance of this approach are simulated numerically and compared to the simpler case, where each driver chooses the shortest path in terms of time, ignoring any information on the other drivers. Numerical results show that formulating the multi-agent route choice problem as a MFG is a promising approach, with the potential of reducing congestions and thus travel times, when compared to the standard shortest path approach. Finally, the affine approximation of the nonlinear travel time model, which characterizes our reference traffic model, is generalized to tighter approximations; since for such models there are currently no theoretical results available, we investigate their validity by numerical analysis.

Supervisors: Sergio Grammatico, Basilio Gentile, Dario Paccagnan, Francesca Parise, John Lygeros


Type of Publication:

(13)Semester/Bachelor Thesis

J. Lygeros

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% Autogenerated BibTeX entry
@PhdThesis { Xxx:2015:IFA_5239
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