Search engine for discovering works of Art, research articles, and books related to Art and Culture
ShareThis
Javascript must be enabled to continue!

Numerical Investigations of Virus Transport Aboard a Commuter Bus

View through CrossRef
The authors performed unsteady numerical simulations of virus/particle transport released from a hypothetical passenger aboard a commuter bus. The bus model was sized according to a typical city bus used to transport passengers within the city of Long Beach in California. The simulations were performed for the bus in transit and when the bus was at a bus stop opening the middle doors for 30 seconds for passenger boarding and drop off. The infected passenger was sitting in an aisle seat in the middle of the bus, releasing 1267 particles (viruses)/min. The bus ventilation system released air from two linear slots in the ceiling at 2097 cubic feet per minute (CFM) and the air was exhausted at the back of the bus. Results indicated high exposure for passengers sitting behind the infectious during the bus transit. With air exchange outside during the bus stop, particles were spread to seats in front of the infectious passenger, thus increasing the risk of infection for the passengers sitting in front of the infectious person. With higher exposure time, the risk of infection is increased. One of the most important factors in assessing infection risk of respiratory diseases is the spatial distribution of the airborne pathogens. The deposition of the particles/viruses within the human respiratory system depends on the size, shape, and weight of the virus, the morphology of the respiratory tract, as well as the subject’s breathing pattern. For the current investigation, the viruses are modeled as solid particles of fixed size. While the results provide details of particles transport within a bus along with the probable risk of infection for a short duration, however, these results should be taken as preliminary as there are other significant factors such as the virus’s survival rate, the size distribution of the virus, and the space ventilation rate and mixing that contribute to the risk of infection and have not been taken into account in this investigation.
Mineta Transportation Institute
Title: Numerical Investigations of Virus Transport Aboard a Commuter Bus
Description:
The authors performed unsteady numerical simulations of virus/particle transport released from a hypothetical passenger aboard a commuter bus.
The bus model was sized according to a typical city bus used to transport passengers within the city of Long Beach in California.
The simulations were performed for the bus in transit and when the bus was at a bus stop opening the middle doors for 30 seconds for passenger boarding and drop off.
The infected passenger was sitting in an aisle seat in the middle of the bus, releasing 1267 particles (viruses)/min.
The bus ventilation system released air from two linear slots in the ceiling at 2097 cubic feet per minute (CFM) and the air was exhausted at the back of the bus.
Results indicated high exposure for passengers sitting behind the infectious during the bus transit.
With air exchange outside during the bus stop, particles were spread to seats in front of the infectious passenger, thus increasing the risk of infection for the passengers sitting in front of the infectious person.
With higher exposure time, the risk of infection is increased.
One of the most important factors in assessing infection risk of respiratory diseases is the spatial distribution of the airborne pathogens.
The deposition of the particles/viruses within the human respiratory system depends on the size, shape, and weight of the virus, the morphology of the respiratory tract, as well as the subject’s breathing pattern.
For the current investigation, the viruses are modeled as solid particles of fixed size.
While the results provide details of particles transport within a bus along with the probable risk of infection for a short duration, however, these results should be taken as preliminary as there are other significant factors such as the virus’s survival rate, the size distribution of the virus, and the space ventilation rate and mixing that contribute to the risk of infection and have not been taken into account in this investigation.

Related Results

Optimizing Dallas-Fort Worth Bus Transportation System Using Any Logic
Optimizing Dallas-Fort Worth Bus Transportation System Using Any Logic
The bus transportation system, modeled using the AnyLogic simulation software, aims to optimize the flow of buses and manage key operational challenges such as bus bunching and del...
A Real-Time Control Strategy for Bus Operation to Alleviate Bus Bunching
A Real-Time Control Strategy for Bus Operation to Alleviate Bus Bunching
In order to alleviate bus bunching and improve the balance and punctuality rate of bus operation, a single-line real-time control strategy based on Intelligent Transportation Syste...
Stated preference analysis of bus service attribustes in Phnom Penh
Stated preference analysis of bus service attribustes in Phnom Penh
Current transportation system in Phnom Penh indicates a lack of proper public transportation. With high number of motorcycles, traffic congestion within the city is getting worse. ...
Capítulo 6 – HIV-AIDS, como tratar, o que fazer e o que não fazer durante o tratamento?
Capítulo 6 – HIV-AIDS, como tratar, o que fazer e o que não fazer durante o tratamento?
A infecção pelo vírus do HIV pode ocorrer de diversas maneiras, tendo sua principal forma a via sexual por meio do sexo desprotegido. O vírus do HIV fica em um período de incubação...
Application Based Bus Tracking System
Application Based Bus Tracking System
Abstract: Buses are available to transport people to a variety of locations, although few passengers are aware of their existence. Complete information, such as the verity of buses...
Modeling of Bus Holding Strategy in Public Transit Systems with Multi-Agent Reinforcement Learning
Modeling of Bus Holding Strategy in Public Transit Systems with Multi-Agent Reinforcement Learning
Excessive fluctuations in travel time between stops and demand at bus stops during bus operations can lead to operational instability in bus systems, such as bus bunching. To tackl...
Simulation Analysis of Bus Passenger Boarding and Alighting Behavior Based on Cellular Automata
Simulation Analysis of Bus Passenger Boarding and Alighting Behavior Based on Cellular Automata
Bus passengers’ boarding and alighting behavior is important content when researching bus operation efficiency. This paper uses an improved cellular automata (CA) model and introdu...
Assessment of the satisfaction of Bus passengers in Dhaka, Bangladesh
Assessment of the satisfaction of Bus passengers in Dhaka, Bangladesh
Increased population results in an increase in travel demand. Greater road length and construction of new roads result in faster and longer travels and increase car ownership, whic...

Back to Top