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

TL;DR

This study analyzes 15 years of U.S. aviation data to reveal a structural shift in how security delays influence flight delays, showing a transition from stabilizing to marginal or delay-contributing effects post-pandemic.

Contribution

It introduces a hierarchical Bayesian model to quantify the changing causal impact of security delays on flight delays over time and across different network scales.

Findings

Security delays shifted from stabilizers to marginal effects post-pandemic.

High-volume hubs show neutralized security delay effects, while smaller networks exhibit increased delay contributions.

The model uncovers a significant change in security delay propagation in the post-pandemic era.

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.