Evolutionary Structural Shift in Security Screening Sensitivity within the U.S. Aviation Network: A 15-Year Longitudinal Bayesian Assessment (2010-2024)

Abstract

This paper investigates the evolving causal mechanisms of flight delays in the U.S. domestic aviation network from 2010-2024. Utilizing a three-level hierarchical Bayesian model on Bureau of Transportation Statistics (BTS) on-time performance data, we decouple the marginal contribution factors of weather, national aviation system (NAS), security delays, and late-arriving aircraft, using carrier delays as the baseline reference. Our findings suggest a structural shift: during the pre-pandemic decade (2010-2019), security delays functioned as an operational stabilizer with negative causal leverage (beta approx -1.307). However, in the post-pandemic period, they shift to a statistically marginal effect (beta approx -0.130). While the total volume of security delays remains a marginal fraction of the overall system latency, this structural shift points toward a potential change in the operational sensitivity of the system to security-related frictions. We show that while causal neutralization is characteristic of high-volume hubs (n >= 100), a discernible directional shift into a positive delay driver (beta approx 0.118) is observed as the analysis scales down to include the broader network (n >= 30). Our model identifies a significant change in how security delays propagate through high-volume nodes, evolving from an internalized operational buffer into a statistically discernible contributor to delay probability in the post-pandemic era.

Figures (6)

• Figure 1: Analytical workflow of the hierarchical Bayesian framework.
• Figure 2: Scaling of Causal Sensitivities ($\beta$) across Volumetric Thresholds. The forest plot shows while structural stressors ($\beta_{\text{nas}}$, $\beta_{\text{late}}$, $\beta_{\text{weather}}$) progressively amplify with airport scale, $\beta_{\text{security}}$ exhibits a notable structural crossover.
• Figure 3: The posterior trajectories of $\gamma_t$. The temporal layer functioned as a high-capacity statistical sink, successfully sequestering the poorly behaved longitudinal fluctuations---including the extreme structural breaks of the 2020--2022 period. This robust separation ensures that the observed shifts in causal sensitivities ($\beta$) are not artifacts of underlying temporal noise, but reflections of true structural scaling within the aviation network.
• Figure 4: The Emerging Structural Shift: Structural Shift and Sensitivity Escalation in the Post-Pandemic Aviation Network.
• Figure 5: Posterior Density Shift of Security Sensitivity. The dramatic shift of the $\beta_{\text{security}}$ posterior density visualizes the systemic transition of the aviation network. The almost total lack of overlap between the 2019 baseline and the 2024 full horizon distributions indicates that the reduced operational buffers is a foundational structural change, not a temporary statistical fluctuation.