# On the Resilience of Traffic Networks under Non-Equilibrium Learning

arXiv: https://arxiv.org/abs/2210.03214 · HTML: https://mlsanity.com/paper/2210.03214/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Oct 2022.

## Built on by (most cited first, 6 of 6)

- [Symbiotic Game and Foundation Models for Cyber Deception Operations in Strategic Cyber Warfare](https://mlsanity.com/paper/2403.10570/) (2024)
- [Is Stochastic Mirror Descent Vulnerable to Adversarial Delay Attacks? A Traffic Assignment Resilience Study](https://mlsanity.com/paper/2304.01161/) (2023)
- [Self-Confirming Transformer for Belief-Conditioned Adaptation in Offline Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2310.04579/) (2023)
- [Commitment with Signaling under Double-sided Information Asymmetry](https://mlsanity.com/paper/2212.11446/) (2022)
- [Distributed Online Convex Optimization with Nonseparable Costs and Constraints](https://mlsanity.com/paper/2602.10452/) (2026)
- [Exit-and-Join Dynamics and Equilibrium in Continuum Cooperative Games](https://mlsanity.com/paper/2606.28824/) (2026)

## Abstract (arXiv)

We investigate the resilience of learning-based textitIntelligent Navigation Systems (INS) to informational flow attacks, which exploit the vulnerabilities of IT infrastructure and manipulate traffic condition data. To this end, we propose the notion of textitWardrop Non-Equilibrium Solution (WANES), which captures the finite-time behavior of dynamic traffic flow adaptation under a learning process. The proposed non-equilibrium solution, characterized by target sets and measurement functions, evaluates the outcome of learning under a bounded number of rounds of interactions, and it pertains to and generalizes the concept of approximate equilibrium. Leveraging finite-time analysis methods, we discover that under the mirror descent (MD) online-learning framework, the traffic flow trajectory is capable of restoring to the Wardrop non-equilibrium solution after a bounded INS attack. The resulting performance loss is of order tildeO(Tβ) (-frac12 ≤ β < 0 )), with a constant dependent on the size of the traffic network, indicating the resilience of the MD-based INS. We corroborate the results using an evacuation case study on a Sioux-Fall transportation network.