  Airborne diseases, including COVID-19, raise the question of transmission
risk in public transportation systems. However, quantitative analysis of the
effectiveness of transmission risk mitigation methods in public transportation
is lacking. The paper develops a transmission risk modeling framework based on
the Wells-Riley model using as inputs transit operating characteristics,
schedule, Origin-Destination (OD) demand, and virus characteristics. The model
is sensitive to various factors that operators can control, as well as external
factors that may be subject of broader policy decisions (e.g. mask wearing).
The model is utilized to assess transmission risk as a function of OD flows,
planned operations, and factors such as mask-wearing, ventilation, and
infection rates. Using actual data from the Massachusetts Bay Transportation
Authority (MBTA) Red Line, the paper explores the transmission risk under
different infection rate scenarios, both in magnitude and spatial
characteristics. The paper assesses the combined impact from viral load related
factors and passenger load factors. Increasing frequency can mitigate
transmission risk, but cannot fully compensate for increases in infection
rates. Imbalanced passenger distribution on different cars of a train is shown
to increase the overall system-wide infection probability. Spatial infection
rate patterns should also be taken into account during policymaking as it is
shown to impact transmission risk. For lines with branches, demand distribution
among the branches is important and headway allocation adjustment among
branches to balance the load on trains to different branches can help reduce
risk.
