Day-to-Day Traffic Network Modeling under Route-Guidance Misinformation: Endogenous Trust and Resilience in CAV Environments
Authors: Eunhan Ka, Satish V. Ukkusuri
Summary
arXiv:2605. 14204v1 Announce Type: cross Abstract: Connected and autonomous vehicles and smart mobility services increasingly use digital route guidance as an operational input to traffic network management.
Relevance
Read next because Day-to-Day Traffic Network Modeling under Route-Guidance Misinformation: Endogenous Trust and Resilience in CAV Environments overlaps with clean result "LoRA persona trained on alone emits at 23.5% when a co-trained partner learns ..., vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: code, class, under, source, model. Source: arxiv cs.CR (Cryptography and Security).
Threat model
Potential threat/caveat for clean result "LoRA persona trained on alone emits at 23.5% when a co-trained partner learns ..., vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)": this item discusses robustness, adversarial.
Abstract
arXiv:2605.14204v1 Announce Type: cross Abstract: 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.