# Data-Driven Models of Selfish Routing: Why Price of Anarchy Does Depend on Network Topology

@article{Benita2020DataDrivenMO,
title={Data-Driven Models of Selfish Routing: Why Price of Anarchy Does Depend on Network Topology},
author={Francisco Benita and Vittorio Bil{\o} and Barnab{\'e} Monnot and Georgios Piliouras and Cosimo Vinci},
journal={ArXiv},
year={2020},
volume={abs/2009.12871}
}`
• Published 27 September 2020
• Computer Science
• ArXiv
We investigate traffic routing both from the perspective of real world data as well as theory. First, we reveal through data analytics a natural but previously uncaptured regularity of real world routing behavior. Agents only consider, in their strategy sets, paths whose free-flow costs (informally their lengths) are within a small multiplicative $(1+\theta)$ constant of the optimal free-flow cost path connecting their source and destination where $\theta\geq0$. In the case of Singapore…
5 Citations
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• Computer Science
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This work provides tight bounds on the price of anarchy of pure Nash equilibria and on the competitive ratio of the greedy algorithm under very general latency functions, including polynomial ones and proves that the greedy strategy is optimal, as it matches the performance of any possible online algorithm.
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A weighted version of the Stackelberg routing problem in which the delay experienced by non-users of the platform is discounted by some parameter β < 1, and natural algorithms for this problem are studied and provable guarantees in the form of constant approximation ratios are provided.

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