  During the COVID-19 pandemic, the collapse of the public transit ridership
led to significant budget deficits due to dramatic decreases in fare revenues.
Additionally, public transit agencies are facing challenges of reduced vehicle
capacity due to social distancing requirements, additional costs of cleaning
and protective equipment, and increased downtime for vehicle cleaning. Due to
these constraints on resources and budgets, many transit agencies have adopted
essential service plans with reduced service hours, number of routes, or
frequencies. This paper studies the resiliency during a pandemic of On-Demand
Multimodal Transit Systems (ODMTS), a new generation of transit systems that
combine a network of high-frequency trains and buses with on-demand shuttles to
serve the first and last miles and act as feeders to the fixed network. It
presents a case study for the city of Atlanta and evaluates ODMTS for multiple
scenarios of depressed demand and social distancing representing various stages
of the pandemic.
  The case study relies on an optimization pipeline that provides an end-to-end
ODMTS solution by bringing together methods for demand estimation, network
design, fleet sizing, and real-time dispatching. These methods are adapted to
work in a multimodal setting and to satisfy practical constraints. In
particular, a limit is imposed on the number of passenger transfers, and a new
network design model is introduced to avoid the computational burden stemming
from this constraint. Real data from the Metropolitan Atlanta Rapid Transit
Authority (MARTA) is used to conduct the case study, and the results are
evaluated with a high-fidelity simulation. The case study demonstrates how
ODMTS provide a resilient solution in terms of cost, convenience, and
accessibility for this wide range of scenarios.
