COVID-19 transmission in U.S. transit buses: A scenario-based approach with agent-based simulation modeling (ABSM)

The transit bus environment is considered one of the primary sources of transmission of the COVID-19 (SARS-CoV-2) virus. Modeling disease transmission in public buses remains a challenge, especially with uncertainties in passenger boarding, alighting, and onboard movements. Although there are initial findings on the effectiveness of some of the mitigation policies (such as face-covering and ventilation), evidence is scarce on how these policies could affect the onboard transmission risk under a realistic bus setting considering different headways, boarding and alighting patterns, and seating capacity control. This study examines the specific policy regimes that transit agencies implemented during early phases of the COVID-19 pandemic in USA, in which it brings crucial insights on combating current and future epidemics. We use an agent-based simulation model (ABSM) based on standard design characteristics for urban buses in USA and two different service frequency settings (10-min and 20-min headways). We find that wearing face-coverings (surgical masks) significantly reduces onboard transmission rates, from no mitigation rates of 85% in higher-frequency buses and 75% in lower-frequency buses to 12.5%. The most effective prevention outcome is the combination of KN-95 masks, open window policies, and half-capacity seating control during higher-frequency bus services, with an outcome of nearly 0% onboard infection rate. Our results advance understanding of COVID-19 risks in the urban bus environment and contribute to effective mitigation policy design, which is crucial to ensuring passenger safety. The findings of this study provide important policy implications for operational adjustment and safety protocols as transit agencies seek to plan for future emergencies. © 2023

Impacts of COVID-19 on Bus Ridership and Recovery Trends in Syracuse, New York

COVID-19 had serious repercussions on public transportation throughout the USA. The aftermath of the peak of the crisis marked the path towards a slow and gradual recovery characterizing the shift to a new normal. Given the limited information on the recovery trends of public transportation, this paper compares the actual ridership and bus supply data for the years of 2019 and 2020 to study the timeline impacts of the pandemic on the bus system of the mid-sized city of Syracuse, NY. A data-driven analysis is presented across the city's bus routes, university bus routes, and categorical bus stops. Various census tract socio-demographic data are also correlated with passenger activity changes and mapped using ArcGIS. The findings show that overall bus ridership in 2020 fell by 70%, on average, during the three months that followed the onset of the pandemic. Since the lifting of the initial restrictions, concerns about using public transportation had partially been alleviated;however, passengers remained reluctant with ridership decline stabilizing at approximately 55% during the last four months of the year. While bus lines serving the university area, which houses a high percentage of youth, were severely affected by the pandemic, passenger activity near hospital stops were less affected and those near major supermarkets/ hypermarkets seemed unaffected, showing a surge especially in the two months that followed the onset of the pandemic. Passenger activity at census tracts having low poverty levels mostly located on the outskirts of the city of Syracuse were the least affected tracts in the last six months of 2020. It is anticipated that the insights presented will help service planners in preparing for similar future events by better understanding what stops and routes are deemed essential during a public health crisis and how the socio-demographics impacted the recovery after restrictions were removed. [ FROM AUTHOR]

Impacts of COVID-19 on Bus Ridership and Recovery Trends in Syracuse, New York

COVID-19 had serious repercussions on public transportation throughout the USA. The aftermath of the peak of the crisis marked the path towards a slow and gradual recovery characterizing the shift to a new normal. Given the limited information on the recovery trends of public transportation, this paper compares the actual ridership and bus supply data for the years of 2019 and 2020 to study the timeline impacts of the pandemic on the bus system of the mid-sized city of Syracuse, NY. A data-driven analysis is presented across the city's bus routes, university bus routes, and categorical bus stops. Various census tract socio-demographic data are also correlated with passenger activity changes and mapped using ArcGIS. The findings show that overall bus ridership in 2020 fell by 70%, on average, during the three months that followed the onset of the pandemic. Since the lifting of the initial restrictions, concerns about using public transportation had partially been alleviated;however, passengers remained reluctant with ridership decline stabilizing at approximately 55% during the last four months of the year. While bus lines serving the university area, which houses a high percentage of youth, were severely affected by the pandemic, passenger activity near hospital stops were less affected and those near major supermarkets/hypermarkets seemed unaffected, showing a surge especially in the two months that followed the onset of the pandemic. Passenger activity at census tracts having low poverty levels mostly located on the outskirts of the city of Syracuse were the least affected tracts in the last six months of 2020. It is anticipated that the insights presented will help service planners in preparing for similar future events by better understanding what stops and routes are deemed essential during a public health crisis and how the socio-demographics impacted the recovery after restrictions were removed.

COVID-19 transmission in U.S. transit buses: A scenario-based approach with agent-based simulation modeling (ABSM)

The transit bus environment is considered one of the primary sources of transmission of the COVID-19 (SARS-CoV-2) virus. Modeling disease transmission in public buses remains a challenge, especially with uncertainties in passenger boarding, alighting, and onboard movements. Although there are initial findings on the effectiveness of some of the mitigation policies (such as face-covering and ventilation), evidence is scarce on how these policies could affect the onboard transmission risk under a realistic bus setting considering different headways, boarding and alighting patterns, and seating capacity control. This study examines the specific policy regimes that transit agencies implemented during early phases of the COVID-19 pandemic inUSA, in which it brings crucial insights on combating current and future epidemics. We use an agent-based simulation model (ABSM) based on standard design characteristics for urban buses in USA and two different service frequency settings (10-min and 20-min headways). We find that wearing face-coverings (surgical masks) significantly reduces onboard transmission rates, from no mitigation rates of 85% in higher-frequency buses and 75% in lower-frequency buses to 12.5%. The most effective prevention outcome is the combination of KN-95 masks, open window policies, and half-capacity seating control during higher-frequency bus services, with an outcome of nearly 0% onboard infection rate. Our results advance understanding of COVID-19 risks in the urban bus environment and contribute to effective mitigation policy design, which is crucial to ensuring passenger safety. The findings of this study provide important policy implications for operational adjustment and safety protocols as transit agencies seek to plan for future emergencies.

Mining urban sustainable performance: Spatio-temporal emission potential changes of urban transit buses in post-COVID-19 future.

Emission benefits of transit buses depend on ridership. Declines in ridership caused by COVID-19 leads uncertainty about the emission reduction capacity of buses. This paper provides a method framework for analyzing spatio-temporal emission patterns of buses in combination with real-time ridership and potential emission changes in the post-COVID-19 future. Based on GPS trajectory and Smart Card data of 2056 buses from 278 routes covering 1.5 million ridership in Qingdao, China, spatio-temporal emissions characteristics of buses are studied. 7589 taxis with 0.2 million passengers' trips are used for acquiring private cars' emissions to evaluate the emissions difference between buses and cars. Empirical results show that the average difference between buses and cars with 2 persons can reach up to 117 g/km-person during 7:00-8:59 and 115 g/km-person during 17:00-18:59. However, buses have various emission benefits around the city at different periods. A double increase in emissions during non-rush hours can be observed compared with rush hours. 224 online survey data are used to study the potential ridership reduction trend in post-COVID-19. Results show that 56.3% of respondents would decrease the usage of buses in the post-COVID-19 future. Based on this figure, our analysis shows that per kilometer-person emissions of buses are higher than cars during non-rush hours, however, still lower than cars during rush hours. We conclude that when ridership reduces by more than 40%, buses cannot be "greener" travel modal than cars as before. Finally, several feasible policies are suggested for this potential challenge. Our study provides convincing evidence for understanding the emission patterns of buses, to support better buses investment decisions and promotion on eco-friendly public transport service in the post-COVID-19 future.