Social-Cognitive Biases in Simulated Airline Luggage Screening

This study illustrated how social cognitive biases affect the decision making process of air1ine luggage screeners. Participants (n = 96) performed a computer simulated task to detect hidden weapons in 200 x-ray images of passenger luggage. Participants saw each image for two (high time pressure) or six seconds (low time pressure). Participants observed pictures of the "passenger" who owns the luggage . The "pre-anchor group" answered questions about the passenger before the luggage image appeared, the "post-snchor" group answered questions after the luggage appeared, and the "no-anchor group" answered no questions. Participants either stopped or did not stop the bag. and rated their confidence in their decision. Participants under high time pressure had lower hit rates and higher false alarms, Significant differences between the pre-, no-, and post-anchor groups were based on the gender and race of the passengers. Participants had higher false alarm rates in response to male than female passengers

Examining Passenger Flow Choke Points at Airports Using Discrete Event Simulation

The movement of passengers through an airport quickly, safely, and efficiently is the main function of the various checkpoints (check-in, security. etc) found in airports. Human error combined with other breakdowns in the complex system of the airport can disrupt passenger flow through the airport leading to lengthy waiting times, missing luggage and missed flights. In this paper we present a model of passenger flow through an airport using discrete event simulation that will provide a closer look into the possible reasons for breakdowns and their implications for passenger flow. The simulation is based on data collected at Norfolk International Airport (ORF). The primary goal of this simulation is to present ways to optimize the work force to keep passenger flow smooth even during peak travel times and for emergency preparedness at ORF in case of adverse events. In this simulation we ran three different scenarios: real world, increased check-in stations, and multiple waiting lines. Increased check-in stations increased waiting time and instantaneous utilization. while the multiple waiting lines decreased both the waiting time and instantaneous utilization. This simulation was able to show how different changes affected the passenger flow through the airport