  Public transport is one of the most disrupted sectors of the COVID-19
pandemic with reported ridership drops up to 90% in majorly affected countries.
As many government authorities strive to partially resume activities, public
transport operators are in an urgent need for models that can evaluate the
impact of different social distancing policies on operational and
passenger-related costs. In this study, we introduce a mixed-integer quadratic
programming model for the redesign of public transport services considering the
operational, passenger, and revenue loss-related costs by evaluating the
effects of different social distancing policies. Our model is applied at the
metro network of Washington D.C. and provides optimal redistribution of
vehicles across lines for different social distancing scenarios. This model can
be used as a decision support tool by other policymakers and public transport
operators that are in need of evaluating the costs related to the
implementation of different social distancing policies.
