Deterministic Decision Support Algorithm for Airport Security Wait Times
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Solution Overview
Problem
Current systems for evaluating wait times and queue lengths at multi-station and multi-stage screening zones in airport security logistics lack predictive analytics and operational insights, leading to inefficiencies and potential security risks due to overwhelmed security agents.
Innovation Solution
A deterministic decision support algorithm and Visual Analytics and Decision Support System (VADSS) platform that predicts wait times and queue lengths by integrating mechanistic, machine learning, and time series models to optimize staffing and resource allocation at Security Screening Checkpoints (SSCPs), using data from multiple sources to forecast passenger arrivals and dynamically adjust checkpoint configurations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual evaluation methods are used for wait times and queue lengths, then system simplicity is maintained, but predictive analytics capability and operational efficiency deteriorate
Solution Approach 1:
The system segments the security screening process into multiple stages (Travel Document Check, primary screening, secondary screening) with multiple stations at each stage. This segmentation allows for granular measurement and prediction of wait times and queue lengths at each specific stage, enabling precise predictive analytics while maintaining manageable system complexity through modular architecture.
Solution Approach 2:
The patent introduces a deterministic decision support algorithm as an intermediary between raw operational data and decision-making. This algorithm processes data from multiple sources (mechanistic models, machine learning, time series) and translates it into actionable predictions about wait times and queue lengths, bridging the gap between complex data and simple operational decisions without requiring the entire system to become unnecessarily complex.
2Loss of time
If more security agents are deployed to reduce wait times, then service quality improves, but operational cost and system complexity increase
Solution Approach 1:
The system implements dynamic staffing recommendations that adjust the number and placement of security agents based on real-time and forecasted passenger flow conditions. Rather than static over-staffing, the deterministic algorithm continuously optimizes agent deployment to match actual demand patterns, reducing wait times when needed while minimizing unnecessary staffing during low-demand periods.
Solution Approach 2:
The patent changes the parameter of staffing levels from fixed to variable based on predictive analytics. The system uses multiple modeling approaches (mechanistic, machine learning, time series) to forecast passenger arrivals and dynamically adjusts staffing parameters accordingly, allowing the organization to respond optimally to changing conditions without committing to excessive baseline staffing levels.
3Reliability
If deterministic algorithms are used for predictions, then prediction accuracy and reliability improve, but adaptability to uncertain future conditions deteriorates
Solution Approach 1:
The deterministic decision support algorithm performs preliminary actions by forecasting future passenger flow conditions using multiple modeling approaches before decisions need to be made. By predicting wait times and queue lengths in advance with high reliability, the system enables proactive staffing and resource allocation decisions that adapt to future conditions before they actually occur, combining deterministic reliability with forward-looking adaptability.
Solution Approach 2:
The system incorporates feedback mechanisms where prediction accuracy is continuously monitored and used to refine the deterministic algorithms. The integration of multiple modeling approaches (mechanistic, machine learning, time series) creates a feedback-rich environment where the system learns from past predictions and actual outcomes, improving both reliability and adaptability over time through iterative refinement based on observed performance.
Data Source
AI summary
In accordance with embodiments disclosed herein, there are provided herein systems, methods, and apparatuses for predicting and evaluating wait times and queue lengths at multi-station and multi-stage screening zones via a deterministic decision support algorithm and complementary prediction model. For example, there is disclosed in accordance with a particular embodiment, a specially configured Visual Analytics and Decision Support System platform (VADSS platform), having means by which to model, predict, and evaluate airport security wait times. Additionally described in accordance with various embodiments is a workforce allocation and configuration decision system for airport security checkpoints (e.g., number of lanes open) based on passenger volume forecasts. The accuracy of such forecasts is critical for the smooth functioning of security checkpoints where unexpected surges in passenger volumes are handled proactively. Thus, the described forecasting model combines flight schedules and other business fundamentals with historically observed throughput patterns to predict passenger volumes in a multi-terminal multi-security screening checkpoint airport. Additionally disclosed is an optimization model and a solution strategy for dynamically selecting a configuration of open screening lanes to minimize passenger queues and wait times that at the same time determine workforce allocations.