Day-to-Day Traffic Network Modeling under Route-Guidance Misinformation: Endogenous Trust and Resilience in CAV Environments

Electrical Engineering and Systems Science > Systems and Control [Submitted on 13 May 2026] Day-to-Day Traffic Network Modeling under Route-Guidance Misinformation: Endogenous Trust and Resilience in CAV Environments Eunhan Ka, Satish V. Ukkusuri Connected and autonomous vehicles and smart mobility services increasingly use digital route guidance as an operational input to traffic network management. When this information becomes unreliable or adversarial, day-to-day traffic models must represent not only flow adaptation but also the evolution of user trust in the information source. This paper develops a coupled day-to-day traffic assignment and trust-evolution framework for route-guidance misinformation. Within-day congestion is represented by Lighthill-Whitham-Richards network loading, while day-to-day route choice follows bounded-rationality logit learning with trust-dependent reliance on external guidance. Trust is modeled as an aggregate class-level behavioral reliance state encoded by a Beta evidence model and updated from repeated guidance errors. Theoretical analysis establishes stationary equilibria, a conservative stability guide, a weighted compliance index for population-level vulnerability, and an asymmetric recovery law that explains post-attack trust hysteresis. Numerical experiments on Sioux Falls, with an Anaheim robustness check, show that endogenous trust creates a threshold-based resilience mechanism. Below the trust-activation threshold, the attack remains behaviorally stealthy and dynamic trust provides almost no attenuation. Above the threshold, trust erosion reduces the impact of the fixed-trust attack by about 91 percent in Sioux Falls and 85 percent in Anaheim. The experiments also show that CAV penetration increases fixed-trust vulnerability while preserving dynamic attenuation, and that traffic performance can recover before trust, resulting in a 77-day hidden vulnerability window. The results provide a trust-aware modeling basis for resilience analysis in CAV-enabled traffic networks. Comments: 10 pages, 7 figures. Under review at IEEE Transactions on Intelligent Transportation Systems Subjects: Systems and Control (eess.SY); Cryptography and Security (cs.CR); Optimization and Control (math.OC) MSC classes: 90B20, 90B10, 93D20, 93C55 ACM classes: J.7; C.2.0; K.6.5 Cite as: arXiv:2605.14204 [eess.SY]   (or arXiv:2605.14204v1 [eess.SY] for this version)   https://doi.org/10.48550/arXiv.2605.14204 Focus to learn more Submission history From: Eunhan Ka [view email] [v1] Wed, 13 May 2026 23:45:53 UTC (5,033 KB) Access Paper: HTML (experimental) view license Current browse context: eess.SY < prev   |   next > new | recent | 2026-05 Change to browse by: cs cs.CR cs.SY eess math math.OC References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)