Modelling elevator traffic with social distancing in a university classroom building

Abstract

Social distancing standards implemented during the COVID-19 pandemic may have negative effects on vertical traffic. We describe a model and use it to predict the elevator traffic under social distancing in a university classroom building, and study the effects of four interventions aimed at improving this traffic. Discrete event-based simulation is used to study whether the lift group meets the forecasted demand when the car capacity is restricted far below its ordinary value to accommodate social distancing. Four low-cost interventions are simulated alone and in combination to quantify the improvements they offer. All four interventions show some improvement, and the combination of all four interventions gives the greatest improvement.

Practical application : Implementing social distancing standards may disproportionately lower the car capacity relative to the building population and thus negatively affect vertical traffic. Building managers seeking to implement low-cost measures to improve elevator traffic under these conditions may look to the percentage improvements described here to aid them in selecting interventions appropriate to their buildings.