Virus Transmission Risk in Urban Rail Systems: A Microscopic Simulation-based Analysis of Spatio-temporal Characteristics

TL;DR

This paper develops a microscopic simulation and modified Wells-Riley model to analyze airborne disease transmission risks in urban rail systems, emphasizing the effects of mask-wearing, ventilation, and operational factors.

Contribution

It introduces a novel integrated microscopic simulation and risk model capturing spatio-temporal passenger flow characteristics in subway systems.

Findings

Mask-wearing significantly reduces transmission risk.

Enhanced ventilation lowers infection probability.

Frequent, reliable operations decrease overall risk.

Abstract

Transmission risk of air-borne diseases in public transportation systems is a concern. The paper proposes a modified Wells-Riley model for risk analysis in public transportation systems to capture the passenger flow characteristics, including spatial and temporal patterns in terms of number of boarding, alighting passengers, and number of infectors. The model is utilized to assess overall risk as a function of OD flows, actual operations, and factors such as mask wearing, and ventilation. The model is integrated with a microscopic simulation model of subway operations (SimMETRO). Using actual data from a subway system, a case study explores the impact of different factors on transmission risk, including mask-wearing, ventilation rates, infectiousness levels of disease and carrier rates. In general, mask-wearing and ventilation are effective under various demand levels, infectiousness levels, and carrier rates. Mask-wearing is more effective in mitigating risks. Impacts from operations and service frequency are also evaluated, emphasizing the importance of maintaining reliable, frequent operations in lowering transmission risks. Risk spatial patterns are also explored, highlighting locations of higher risk.