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Analysis of airline boarding changes in response to COVID-19

Citation

Srinivasan, Ashok; Islam, Tasvirul; Sadeghi Lahijani, Mehran; Namilae, Sirish (2021), Analysis of airline boarding changes in response to COVID-19, Dryad, Dataset, https://doi.org/10.5061/dryad.18931zctb

Abstract

Airlines have introduced a back-to-front boarding process in response to the COVID-19 pandemic. We performed pedestrian dynamics simulation using the Constrained Linear Movement (CALM) model to simulate people boarding a 144-seat Airbus 320 airplane using different boarding procedures, including back to front. This data set contains input files, code, and output files from the simulations containing the trajectories of passengers for a parameter sweep consisting of thousands of simulations. It also contains code to perform analysis of the output to determine passenger contacts.

Methods

We used pedestrian dynamics to simulate people boarding a 144-seat Airbus 320 airplane using different boarding procedures. Simulations start with input files provided here, specifying the initial positions of passengers. The Constrained Linear Movement (CALM) model is used to compute passenger trajectories, which are then output. A parameter sweep is performed to explore the space of possible passenger behavior, thus leading to thousands of simulations. Each simulation is then analyzed to determine the number of contacts arising from it. Each contact corresponds to 1.125 person seconds of contact. It is a function of the contact threshold, which is an input parameter to the code.

Usage Notes

1. A user will need to run the codes on machines with the Message Passing Interface (MPI) installed.

2. It will help a user to read the following paper.

M.S. Lahijani, T. Islam, A. Srinivasan, and S. Namilae. Constrained Linear Movement Model (CALM): Simulation of passenger movement in airplanes. PLoS ONE 15(3): e0229690 (2020). https://doi.org/10.1371/journal.pone.0229690.

Funding

National Science Foundation, Award: 1931511

National Science Foundation, Award: 2027514