On the Resilience of Traffic Networks under Non-Equilibrium Learning

Yunian Pan, Tao Li, Quanyan Zhu

Research output: Chapter in Book/Report/Conference proceedingConference contribution


We investigate the resilience of learning-based Intelligent 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 Wardrop 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. O (Tβ (-1/2 ≤ 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.

Original languageEnglish (US)
Title of host publication2023 American Control Conference, ACC 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9798350328066
StatePublished - 2023
Event2023 American Control Conference, ACC 2023 - San Diego, United States
Duration: May 31 2023Jun 2 2023

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619


Conference2023 American Control Conference, ACC 2023
Country/TerritoryUnited States
CitySan Diego

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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