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

TL;DR

This paper presents a mixed-integer quadratic programming model to optimize metro service frequency under COVID-19 social distancing measures, balancing operational, passenger, and revenue costs.

Contribution

It introduces a novel optimization model for redesigning metro services considering social distancing, applicable to real-world networks like Washington D.C.

Findings

Optimal vehicle redistribution varies with social distancing levels.

Model effectively evaluates costs associated with different distancing policies.

Provides decision support for public transport operators and policymakers.

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 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.