Schedule-based analysis of airborne transmission risk in public transportation systems

Abstract

Airborne diseases 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 an airborne 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, and external factors that may be subject of broader policy decisions. 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 OD and AVL data from the Massachusetts Bay Transportation Authority (MBTA) Red Line, the paper explores the airborne transmission risk under different infection rate scenarios. The paper assesses the combined impact from viral load related factors and passenger load factors. Increasing frequency can mitigate risk, but cannot fully compensate for increases in infection rates. Imbalanced passenger distribution on different cars of a train increases the overall system-wide infection probability. Spatial infection rate patterns should also be considered during policymaking. For lines with branches, demand distribution among the branches is important and headway allocation adjustment can reduce risk.