Optimal frequency setting of metro services in the age of COVID-19 distancing measures
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Abstract
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 DC 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.
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