Schedule-based analysis of airborne transmission risk in public transportation systems
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 th...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Transportation Research Interdisciplinary Perspectives |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590198224002872 |
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| author | Jiali Zhou Haris N. Koutsopoulos |
| author_facet | Jiali Zhou Haris N. Koutsopoulos |
| author_sort | Jiali Zhou |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-4be91baaef6647afb6f334fc4b23485f |
| institution | Kabale University |
| issn | 2590-1982 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Transportation Research Interdisciplinary Perspectives |
| spelling | doaj-art-4be91baaef6647afb6f334fc4b23485f2025-02-09T05:01:12ZengElsevierTransportation Research Interdisciplinary Perspectives2590-19822025-01-0129101301Schedule-based analysis of airborne transmission risk in public transportation systemsJiali Zhou0Haris N. Koutsopoulos1Department of Urban Planning and Design, The University of Hong Kong, Hong Kong SAR, China; Corresponding author.Department of Civil and Environmental Engineering, Northeastern University, Boston, MA 02115, USAAirborne 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.http://www.sciencedirect.com/science/article/pii/S2590198224002872Transmission riskPublic transportationPublic healthResilient transportationTransportation planningOperating strategies |
| spellingShingle | Jiali Zhou Haris N. Koutsopoulos Schedule-based analysis of airborne transmission risk in public transportation systems Transportation Research Interdisciplinary Perspectives Transmission risk Public transportation Public health Resilient transportation Transportation planning Operating strategies |
| title | Schedule-based analysis of airborne transmission risk in public transportation systems |
| title_full | Schedule-based analysis of airborne transmission risk in public transportation systems |
| title_fullStr | Schedule-based analysis of airborne transmission risk in public transportation systems |
| title_full_unstemmed | Schedule-based analysis of airborne transmission risk in public transportation systems |
| title_short | Schedule-based analysis of airborne transmission risk in public transportation systems |
| title_sort | schedule based analysis of airborne transmission risk in public transportation systems |
| topic | Transmission risk Public transportation Public health Resilient transportation Transportation planning Operating strategies |
| url | http://www.sciencedirect.com/science/article/pii/S2590198224002872 |
| work_keys_str_mv | AT jializhou schedulebasedanalysisofairbornetransmissionriskinpublictransportationsystems AT harisnkoutsopoulos schedulebasedanalysisofairbornetransmissionriskinpublictransportationsystems |