Pedestrian movement simulation for an airport considering social distancing strategy

Abstract

This study develops a pedestrian microsimulation model for an international airport that implements a social force model to simulate business-as-usual as well as pandemic pedestrian movement scenarios. The study follows an extensive calibration procedure to formulate pedestrian behavior within the microsimulation environment to reflect COVID-19 restrictions. The calibration process includes three components, which are visual observations, sensitivity analysis, and parameter combinations. The calibration process identifies the combination of the parameters that yields a pedestrian behavior for maximizing the distances among pedestrians in the airport. The study tests three scenarios: (1) a base case scenario with no social distancing parameters in effect, (2) a social distancing scenario that implements calibrated social force model parameters, and (3) a social distancing scenario through a combined implementation of the calibrated parameters and an operational improvement strategy. The simulation results for calibration reveal that the identified combination of the parameters yields 3.8% of instances of pedestrian movements that demonstrate a proximity of less than 2 m between pedestrians. The social distancing scenario demonstrates a significant reduction of such instances by 93–94% in comparison to a scenario with no social distancing strategy implemented. Moreover, the results show that social distancing is likely to increase pedestrian’s activity and queue time in the airport. According to the simulation results, it appears that the airport authorities may require queue management processes, infrastructure development, and additional resource deployment to alleviate the negative impacts of social distancing policies on pedestrian movements and operations within the airport.