Virus Transmission Risk in Urban Rail Systems: Microscopic Simulation-Based Analysis of Spatio-Temporal Characteristics

Abstract

The transmission risk of airborne diseases in public transportation systems is a concern. This 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 the number of boarding and alighting passengers, and in number of infectors. The model is used to assess overall risk as a function of origin–destination 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.

Analysis of Passenger Flow Characteristics of Urban Rail Transit Based on Spatial Data Dynamic Analysis Technology

“…Hourly passenger flow is used to determine the capacity of urban rail transit entrances and exits, channels, and other equipment, and it is the basis for calculating the full-day driving plan and vehicle allocation plan [10].…”

Modeling Virus Transmission Risks in Commuting with Emerging Mobility Services: A Case Study of COVID-19

“…The model is implemented at the metropolitan level for population infection calculation. Zhou and Koutsopoulos (2021) propose a modified Wells-Riley model for infection probability calculation in public transportation systems at the vehicle level. The model captures the spatial and temporal passenger flow characteristics in terms of the number of boarding and alighting passengers and the number of infectors.…”

“…Commuting is an important part of the daily lives of people working in an office. In light of the infectiousness of COVID-19, the infection risk during the commuting process is a concern, especially for people using public transportation, as indicated by many previous studies (Mo et al, 2021;Zhou and Koutsopoulos, 2021). On the other hand, recent emerging mobility services (such as ride-hailing and bikesharing) further deteriorate the infection risks due to shared vehicles or spaces during travel.…”

“…Many previous studies have modeled the COVID-19 infection risks in public transit systems, an important travel mode of commuting. These studies can be categorized from the macro-level at the city scale (Mo et al, 2021) or the micro-scale at the vehicle scale (Shinohara et al, 2021;Zhou and Koutsopoulos, 2021). Researchers have also considered the impact of commuting on the broader spatial transmission of COVID-19 and the related control strategies (Mitze and Kosfeld, 2022;Ando et al, 2021;Fajgelbaum et al, 2021;Kondo, 2021).…”

How do temperature, humidity, and air saturation state affect the COVID-19 transmission risk?

“…Andrews et al used a modified W-R model to estimate the impact of ventilation on infection probability for inmates sharing a cell with an infector (Urrego et al 2015 ). Zhou and Koutsopoulos proposed a modified W-R model for risk analysis in public transportation systems, which integrated with a simulation model of subway operations (Zhou and Koutsopoulos 2021 ). Zhai et al and Zhang et al developed a modified W-R model for integrating the spatial distribution of pathogen concentrations (Guo et al 2021 ; Zhai and Li 2021 ).…”